Introduction to Processing and Analysis of Spatial Multiplexed Proteomics Data
Dates: 9–13 February 2026
Format: Live online, 5 days × 5.5 hours per day
Fee: £450 (standard) | £400 (early bird, first 5 spots)
Time zone: UK (GMT); all sessions are recorded and made available for 30 days
Why This Course Matters Spatial multiplexed proteomics techniques—such as CODEX, CycIF, and MxIF/MACSIMA—are revolutionising how we understand tissue microenvironments, cellular interactions, and spatial heterogeneity in biological systems. However, converting raw multiplexed imaging data into actionable biological insight requires expertise in image processing, spatial statistics, phenotyping, and bioinformatics pipelines.SPMP01 bridges that gap.
Over five intensive days, you will learn both the theoretical foundations and the hands-on computational skills needed to process, analyse, and interpret spatial multiplexed proteomics data. Whether your work lies in basic biology, cancer immunology, neuroscience, or spatial systems biology, this course equips you to handle complex image-based proteomics datasets.
What You’ll Learn Participants will move from foundational concepts to applied workflows across these core topics:Overview and comparison of spatial multiplexed imaging platforms (CODEX, CycIF, MxIF / MACSIMA)Image processing workflows: tile stitching, illumination correction, alignment, and region-of-interest generationHandling multi-resolution image formats (e.g., .tif, .ome.tif, .ome.zarr), and visualization strategiesSingle-cell segmentation: algorithms (e.g. Cellpose, Stardist, Mesmer), mask QC, and error diagnosticsFeature extraction and cell phenotyping (marker intensity gating, clustering, annotation)Spatial neighbourhood and cell–cell interaction analysis: quantifying local and global neighbourhood statisticsBatch processing and scalable workflows (using Nextflow pipelines such as MCMICRO)Best practices for reproducibility, data storage, workflow modularity, and integration with R/Python pipelines.
Through guided coding sessions and worked examples, you will apply these methods to real multiplexed imaging datasets and gain experience interpreting spatial proteomics results.
Format & Support Each day blends lectures, demonstrations, and hands-on practical work Participants are encouraged to bring their own data for discussion (time permitting) All course materials, scripts, and datasets are shared with attendees Livestream sessions are recorded and made available the same day Post-course email support is offered for 30 days to assist with implementation and troubleshooting
Who Should Attend This course is aimed at researchers, computational biologists, bioinformaticians, and technical scientists who work with—or plan to work with—spatial omics and proteomics imaging data. Prior experience with R or Python is advantageous. Basic knowledge of statistics and familiarity with image data (microscopy) will help, but are not strict prerequisites. A comfortable level of computing literacy (e.g. command line use) is expected.
Instructors Dr Victor Perez Meza — an expert in fluorescence microscopy, image artefact correction, and multiplexed imaging workflows MSc Miguel Angel Ibarra Arellano — specialist in reproducible bioimage analysis, neighbourhood spatial statistics, and spatial omics toolsTheir combined experience ensures a mix of methodological insight and practical, cutting-edge implementation.
Who Will Benefit Participants in SPMP01 will be better equipped to: Process and clean raw multiplexed imaging datasets Segment individual cells reliably and assess segmentation quality Assign cell phenotypes and derive per-cell morphological or marker statistics Quantify spatial relationships and neighbourhood structure in tissue Develop reproducible pipelines for spatial proteomics workflows Integrate processed spatial data into downstream statistical or machine learning analyses
In fields such as cancer microenvironment analysis, immunology, neuroscience, and developmental biology, these capabilities are invaluable for linking cellular spatial patterns to functional and phenotypic insights.
