The Center for Cancer Systems Pharmacology

Research

Image Analysis & Visualization Software

The LSP develops open-source software tools that improve the reproducibility, accuracy, and accessibility of spatial biology datasets.

Spatial biology methods like highly-multiplexed tissue imaging make it possible to visualize the precise locations of 60-100 proteins within human tissues. The resulting images contain deep biological insights, but their size - which can be 200 GB to 1 TB - makes them challenging to analyze and share. 

LSP investigators have developed numerous open-source software solutions that address these persistent challenges. These include tools for identifying single-cell boundaries (a process known as segmentation), performing quality control, analyzing the single-cell data, and visualizing tissue images online (Minerva). Many of these tools have been integrated into MCMICRO, a customizable software pipeline that is used by labs across the globe. 

LSP investigators are collaborating with external communities (like nf-core and the NCI Imaging Data Commons) to integrate our software packages into standardized infrastructures. We aim for our software tools to ensure precise and reproducible tissue imaging datasets so that the deep molecular insights within spatial datasets can be fully realized. 

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Featured Publications

Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data.

Rashid, et al.
Nature Biomedical Engineering. 2022 May. PMCID: 9079188.
Journal
PubMed

Quality control for single-cell analysis of high-plex tissue profiles using CyLinter

Baker et al
Nat Methods. 2024 Oct 30; PMID: 39478175
Journal
PubMed

Related Publications

de Bruijn, I., Nikolov, M., Lau, C., Clayton, A., Gibbs, D. L., Mitraka, E., Pozhidayeva, D., Lash, A., Sumer, S. O., Altreuter, J., Anton, K., DeFelice, M., Li, X., Lisman, A., Longabaugh, W. J. R., Muhlich, J., Santagata, S., Nandakumar, S., Sorger, P. K., … Eddy, J. A. (2025). Sharing data from the Human Tumor Atlas Network through standards, infrastructure and community engagement. Nature Methods, 22(4), 664–671. https://doi.org/10.1038/s41592-025-02643-0
Zhou, F. Y., Marin, Z., Yapp, C., Zou, Q., Nanes, B. A., Daetwyler, S., Jamieson, A., Islam, M. T., Jenkins, E., Gihana, G. M., Lin, J., Borges, H. M., Chang, B.-J., Weems, A., Morrison, S. J., Sorger, P. K., Fiolka, R. P., Dean, K. M., & Danuser, G. (2025). Universal consensus 3D segmentation of cells from 2D segmented stacks [Preprint]. bioRxiv. https://doi.org/10.1101/2024.05.03.592249
Prabhakaran, S., Yapp, C., Baker, G. J., Beyer, J., Chang, Y. H., Creason, A. L., Krueger, R., Muhlich, J., Patterson, N. H., Sidak, K., Sudar, D., Taylor, A. J., Ternes, L., Troidl, J., Yubin, X., Sokolov, A., & Tyson, D. R. (2025). Addressing persistent challenges in digital image analysis of cancer tissue: resources developed from a hackathon. Molecular Oncology. https://doi.org/10.1002/1878-0261.13783
Morth, E., Sidak, K., Maliga, Z., Moller, T., Gehlenborg, N., Sorger, P., Pfister, H., Beyer, J., & Kruger, R. (2025). Cell2Cell: Explorative Cell Interaction Analysis in Multi-Volumetric Tissue Data. IEEE Transactions on Visualization and Computer Graphics, 31(1), 569–579. https://doi.org/10.1109/TVCG.2024.3456406
Baker, G. J., Novikov, E., Zhao, Z., Vallius, T., Davis, J. A., Lin, J.-R., Muhlich, J. L., Mittendorf, E. A., Santagata, S., Guerriero, J. L., & Sorger, P. K. (2024). Quality control for single-cell analysis of high-plex tissue profiles using CyLinter. Nature Methods, 21(12), 2248–2259. https://doi.org/10.1038/s41592-024-02328-0
Warchol, S., Troidl, J., Muhlich, J., Krueger, R., Hoffer, J., Lin, T., Beyer, J., Glassman, E., Sorger, P., & Pfister, H. (2024). psudo: Exploring Multi‐Channel Biomedical Image Data with Spatially and Perceptually Optimized Pseudocoloring. Computer Graphics Forum, e15103. https://doi.org/10.1111/cgf.15103
Nirmal, A. J., & Sorger, P. K. (2024). SCIMAP: A Python Toolkit for Integrated Spatial Analysis of Multiplexed Imaging Data. Journal of Open Source Software, 9(97), 6604. https://doi.org/10.21105/joss.06604
Wan, G., Maliga, Z., Yan, B., Vallius, T., Shi, Y., Khattab, S., Chang, C., Nirmal, A. J., Yu, K.-H., Liu, D., Lian, C. G., DeSimone, M. S., Sorger, P. K., & Semenov, Y. R. (2024). SpatialCells: automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data. Briefings in Bioinformatics, 25(3), bbae189. https://doi.org/10.1093/bib/bbae189
Nirmal, A. J., Yapp, C., Santagata, S., & Sorger, P. K. (2023). Cell Spotter (CSPOT): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images [Preprint]. bioRxiv. https://doi.org/10.1101/2023.11.15.567196
Muhlich, J. L., Chen, Y.-A., Yapp, C., Russell, D., Santagata, S., & Sorger, P. K. (2022). Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR. Bioinformatics, btac544. https://doi.org/10.1093/bioinformatics/btac544
Rashid, R., Chen, Y.-A., Hoffer, J., Muhlich, J. L., Lin, J.-R., Krueger, R., Pfister, H., Mitchell, R., Santagata, S., & Sorger, P. K. (2022). Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data. Nature Biomedical Engineering, 6(5), 515–526. https://doi.org/10.1038/s41551-021-00789-8
Schapiro, D., Yapp, C., Sokolov, A., Reynolds, S. M., Chen, Y.-A., Sudar, D., Xie, Y., Muhlich, J., Arias-Camison, R., Arena, S., Taylor, A. J., Nikolov, M., Tyler, M., Lin, J.-R., Burlingame, E. A., Human Tumor Atlas Network, Chang, Y. H., Farhi, S. L., Thorsson, V., … Sorger, P. K. (2022). MITI minimum information guidelines for highly multiplexed tissue images. Nature Methods, 19(3), 262–267. https://doi.org/10.1038/s41592-022-01415-4
Schapiro, D., Sokolov, A., Yapp, C., Chen, Y.-A., Muhlich, J. L., Hess, J., Creason, A. L., Nirmal, A. J., Baker, G. J., Nariya, M. K., Lin, J.-R., Maliga, Z., Jacobson, C. A., Hodgman, M. W., Ruokonen, J., Farhi, S. L., Abbondanza, D., McKinley, E. T., Persson, D., … Sorger, P. K. (2022). MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging. Nature Methods, 19(3), 311–315. https://doi.org/10.1038/s41592-021-01308-y
Hoffer, J., Rashid, R., Muhlich, J. L., Chen, Y.-A., Russell, D. P. W., Ruokonen, J., Krueger, R., Pfister, H., Santagata, S., & Sorger, P. K. (2020). Minerva: a light-weight, narrative image browser for multiplexed tissue images. Journal of Open Source Software, 5(54), 2579. https://doi.org/10.21105/joss.02579
Goldberg, I. G., Allan, C., Burel, J.-M., Creager, D., Falconi, A., Hochheiser, H., Johnston, J., Mellen, J., Sorger, P. K., & Swedlow, J. R. (2005). The Open Microscopy Environment (OME) data model and XML file: open tools for informatics and quantitative analysis in biological imaging. Genome Biology, 6(5), R47. https://doi.org/10.1186/gb-2005-6-5-r47
Swedlow, J. R., Goldberg, I., Brauner, E., & Sorger, P. K. (2003). Informatics and quantitative analysis in biological imaging. Science, 300(5616), 100–102. https://doi.org/10.1126/science.1082602

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