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The Laboratory of Systems Pharmacology (LSP) and Harvard Program in Therapeutic Sciences (HiTS) at Harvard Medical School has a number of publications related to MCMICRO.

  • Hoffer, J., Rashid, R., Muhlich, J., Chen, Y.-A., Russell, D., Ruokonen, J., Krueger, R., Pfister, H., Santagata, S., & Sorger, P. (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

  • Lin, J.-R., Izar, B., Wang, S., Yapp, C., Mei, S., Shah, P. M., Santagata, S., & Sorger, P. K. (2018). Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. ELife, 7. https://doi.org/10.7554/eLife.31657

  • Muhlich, J., Chen, Y.-A., Russell, D., & Sorger, P. K. (2021). Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR software. BioRxiv. https://doi.org/10.1101/2021.04.20.440625

  • Nirmal, A. J., Maliga, Z., Vallius, T., Quattrochi, B., Chen, A. A., Jacobson, C. A., Pelletier, R. J., Yapp, C., Arias-Camison, R., Chen, Y.-A., Lian, C. G., Murphy, G. F., Santagata, S., & Sorger, P. K. (2022). The spatial landscape of progression and immunoediting in primary melanoma at single cell resolution. Cancer Discoveryhttps://doi.org/10.1158/2159-8290.CD-21-1357

  • Rashid, R., Chen, Y.-A., Hoffer, J., Muhlich, J. L., Lin, J.-R., Krueger, R., Pfister, H., Mitchell, R., Santagata, S., & Sorger, P. K. (2021). Narrative online guides for the interpretation of digital-pathology images and tissue-atlas data. Nature Biomedical Engineering. https://doi.org/10.1038/s41551-021-00789-8

  • 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. https://doi.org/10.1038/s41592-021-01308-y

  • Schapiro, D., Yapp, C., Sokolov, A., Reynolds, S. M., Chen, Y.-A., Sudar, D., Xie, Y., Muhlich, J. L., 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. https://doi.org/10.1038/s41592-022-01415-4

  • Yapp, C., Novikov, E., Jang, W.-D., Vallius, T., Chen, Y.-A., Cicconet, M., Maliga, Z., Jacobson, C. A., Wei, D., Santagata, S., Pfister, H., & Sorger, P. K. (2021). UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues. BioRxiv. https://doi.org/10.1101/2021.04.02.438285

  • View the HiTS bioRxiv channel and the LSP website for the most up-to-date list of our work.