Analyzing MACSima 47-plex mIF with MIKAIA: AI Cell Segmentation + Cell Typing + Cell-cell Connections + Cellular Neighborhoods

This MIKAIA^® app note demonstrates how to analyze a 47-plex spatial proteomics scan created with MACSima ™ (by Miltenyi Biotec), utilizing sequential immunofluorescence for the analysis:

• Instrument: MACSima™ by Miltenyi Biotec
• Tissue: Synovial tissue of patient with psoriatic arthritis
• Wet lab & Imaging: Fraunhofer ITMP, Frankfurt, Klaus Scholich lab
• Panel: 47-plex immune panel composed by Fraunhofer ITMP
• Analysis Software: MIKAIA^® v2.3; apps used: FL Cell Analysis, Cell-Cell Connections, Cellular Neighborhood App

This video was created with MIKAIA^® 2.3.0 and shows the step-by-step analysis of a 47-plex spatial proteomics scan created with MACSima ™. Image generated and kindly provided by colleagues at Fraunhofer ITMP (Prof. Dr. Klaus Scholich, Nathalie Behr). Voice-over / speaker: Volker Bruns, Fraunhofer IIS

Video structure

1. Cell segmentation and cell typing
   1. How to import a multiplex image with one TIFF file per marker
   2. Configuring black and white levels per channel using channel-preprocessing preview dialog.
   3. Configuring a cell type mapping (e.g., “T helper cell” when CD3 and CD4 are expressed)
   4. Configuring per marker threshold (here the “auto” threshold mode is used)
   5. Reviewing and interpreting cell typing results
   6. Interpreting diagrams and working with interactive scatter plot
   7. Alternative unbiased cell typing using clustering, incl. T-SNE & UMAP visualization
   8. Exporting quantitative results to CSV spreadsheet
   9. Enabling interactive heatmap
2. Cell-cell connections analysis
   • The app connects each cell to its closest neighbor cells (Delauney triangulation) and collects statistics such as which cell types are connected, what is average distance, bystander analysis, etc.
3. Cellular neighborhood (CN) analysis
   • This app centers on each cell and then collects all cells in the vicinity (either k-nearest or by radius or both).
     It then can do two analyses:
     1. It reports per cell type the average composition of the neighborhood, grouped by increasing distance (histogram)
     2. It identifies cellular neighborhood (CN) types by k-means clustering and assigns each cell to a CN.

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Contacts for Spatial Proteomics Imaging

Prof. Dr. Klaus Scholich

Head of Innovation Area Cross-functional Imaging
Fraunhofer Institute for Translational Medicine and Pharmacology ITMP
Theodor-Stern-Kai 7
60596 Frankfurt am Main
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Nathalie Behr

Doctoral Candidate & Application Specialist
Fraunhofer Institute for Translational Medicine and Pharmacology ITMP
Theodor-Stern-Kai 7
60596 Frankfurt am Main
Send email

This project is conducted in the context of
and receives funding by
Fraunhofer Cluster of Excellence
Immune-Mediated Diseases (CIMD)