Jolene Ranek

All Publications

• The immunometabolic topography of cellular organization and bacterial control in tuberculosis granulomas. Nature immunology McCaffrey, E. F., Delmastro, A. C., Fitzhugh, I., Ranek, J. S., Douglas, S., Peters, J. M., Fullaway, C. C., Bosse, M., Liu, C. C., Gillen, C., Greenwald, N. F., Anzick, S., Martens, C., Winfree, S., Bai, Y., Sowers, C., Goldston, M., Kong, A., Boonrat, P., Bigbee, C. L., Venugopalan, R., Maiello, P., Klein, E., Rodgers, M. A., Scanga, C. A., Lin, P. L., Bendall, S. C., Kirschner, D. E., Fortune, S. M., Bryson, B. D., Butler, J. R., Mattila, J. T., Flynn, J. L., Angelo, M. 2026

  Abstract

  Despite being heavily infiltrated by immune cells, tuberculosis (TB) granulomas often subvert the host response to Mycobacterium tuberculosis (Mtb) infection and support bacterial persistence. Human TB granulomas are enriched for immunosuppressive factors typically associated with tumor-immune evasion, raising the possibility that they promote tolerance to infection. Here we identify candidate drivers for establishing this tolerogenic niche and show that the magnitude of this response correlates with bacterial persistence. We conducted a multimodal spatial analysis of 52 granulomas from 16 nonhuman primates infected with low-dose Mtb for 9-12 weeks. Each granuloma's bacterial burden was quantified individually, enabling us to assess how granuloma spatial structure and function relate to infection control. We found that a universal feature of TB granulomas is partitioning of the myeloid core into two distinct metabolic environments, one of which is hypoxic. This hypoxic environment is associated with pathological immune cell states, dysfunctional cellular organization of the granuloma, and a near-complete blockade of lymphocyte infiltration that would be required for a successful host response. The extent of these hypoxia-associated features correlates with higher bacterial burden. We conclude that hypoxia correlates with immune cell state and organization within granulomas and might subvert immunity to TB.

  View details for DOI 10.1038/s41590-026-02431-8

  View details for PubMedID 41731147

  View details for PubMedCentralID 3395138

• The automated computational workflow QUICHE reveals structural definitions of antitumor responses in triple-negative breast cancer. Nature cancer Ranek, J. S., Greenwald, N. F., Goldston, M., Camacho Fullaway, C., Sowers, C., Kong, A., Mouron, S., Quintela-Fandino, M., West, R. B., Bendall, S. C., Angelo, M. 2026

  Abstract

  Recent advances in spatial biology can reveal how tissue organization changes in disease; however, interpreting these datasets in a generalized, scalable way remains challenging. Existing computational approaches rely on pairwise comparisons or unsupervised clustering, which can lack statistical rigor and miss rare, clinically relevant cellular niches. Here we present QUICHE-an automated and scalable statistical framework designed to discover cellular niches differentially enriched in populations, histological structures or acellular regions. Using in silico models and spatial proteomic imaging of human tissues, we show that QUICHE can accurately detect low-prevalence, condition-specific niches, outperforming the next best algorithm threefold. To investigate how tumor structure influences recurrence risk in triple-negative breast cancer, we applied QUICHE to a multicenter spatial proteomics cohort of 314 primary tumor resections. We discovered niches consistently enriched in tumor border and extracellular-matrix-remodeling regions, including those associated with recurrence-free survival. These findings were validated in two independent cohorts, suggesting that antitumor responses are driven by coordinated engagement between innate and adaptive immune cells, rather than any single population. QUICHE is provided as an open-source Python package ( https://github.com/jranek/quiche ).

  View details for DOI 10.1038/s43018-026-01122-5

  View details for PubMedID 41708893

  View details for PubMedCentralID 6785247

• Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition in metastatic TNBC. Nature cancer Greenwald, N. F., Nederlof, I., Sowers, C., Ding, D. Y., Park, S., Kong, A., Houlahan, K. E., Varra, S. R., de Graaf, M., Geurts, V., Liu, C. C., Ranek, J. S., Voorwerk, L., de Maaker, M., Kagel, A., McCaffrey, E., Khan, A., Yeh, C. Y., Fullaway, C. C., Khair, Z., Simon, B. G., Bai, Y., Piyadasa, H., Risom, T., Delmastro, A., Hartmann, F. J., Mangiante, L., Sotomayor-Vivas, C., Bendall, S. C., Schumacher, T. N., Ma, Z., Bosse, M., van de Vijver, M. J., Tibshirani, R., Horlings, H. M., Curtis, C., Kok, M., Angelo, M. 2026

  Abstract

  Immune checkpoint inhibition (ICI) benefits only a subset of patients with metastatic triple-negative breast cancer and determinants of response remain unclear. We assembled a longitudinal cohort of 103 female patients from the phase 2 TONIC trial, with samples spanning primary tumors, pretreatment metastases and on-treatment metastases during nivolumab therapy. We profiled 37 proteins in 270 tumors using highly multiplexed imaging and developed SpaceCat, an open-source pipeline that extracts more than 800 imaging features per sample, including cell density, diversity, spatial interactions and functional marker expression. Metastatic but not primary tumors contained features predictive of outcome. Spatial metrics such as immune diversity and T cell infiltration at tumor borders were most informative, while ratios of T cells to cancer cells and PDL1 on myeloid cells were also associated with response. Multivariate models stratified patients with the highest performance on treatment (area under the curve = 0.90). Bulk RNA-seq confirmed the predictive value of on-treatment samples. These findings highlight the value of longitudinal profiling to resolve evolving tumor microenvironment dynamics driving ICI response.

  View details for DOI 10.1038/s43018-026-01114-5

  View details for PubMedID 41708895

  View details for PubMedCentralID 5698905

• Multi-omic landscape of human gliomas from diagnosis to treatment and recurrence. Cancer cell Piyadasa, H., Oberlton, B., Ribi, M., Leow, K., Ranek, J. S., Averbukh, I., Amouzgar, M., Liu, C. C., Franchina, D. G., Greenwald, N. F., McCaffrey, E. F., Kumar, R., Ferrian, S., Tsai, A. G., Filiz, F., Fullaway, C. C., Bosse, M., Varra, S. R., Kong, A., Sowers, C., Gephart, M. H., Nuñez-Perez, P., Yang, E., Travers, M., Schachter, M. J., Liang, S., Santi, M. R., Bucktrout, S., Gherardini, P. F., Connolly, J., Cole, K., Barish, M. E., Brown, C. E., Oldridge, D. A., Drake, R. R., Phillips, J. J., Okada, H., Prins, R., Bendall, S. C., Angelo, M. 2025

  Abstract

  Gliomas are among the most lethal cancers, with limited treatment options. To uncover hallmarks of therapeutic escape and tumor microenvironment (TME) landscape, we applied spatial proteomics, transcriptomics, and glycomics to 670 lesions from 310 adult and pediatric patients. Single-cell analysis shows high B7H3+ tumor cell prevalence in glioblastoma (GBM) and pleomorphic xanthoastrocytoma, while most gliomas, including pediatric cases, express targetable tumor antigens in less than 50% of tumor cells, potentially explaining trial failures. Paired samples of isocitrate dehydrogenase (IDH)-mutant gliomas reveal recurrence driven by tumor-immune spatial reorganization, shifting from T cell and vasculature-associated myeloid cell-enriched niches to microglia and CD206+ macrophage-dominated tumors. Multi-omic integration identified N-glycosylation as the best classifier of grade, while the immune transcriptome best predicted GBM survival. Provided as a community resource, this study offers a framework for glioma targeting, classification, outcome prediction, and a baseline of TME composition across all stages.

  View details for DOI 10.1016/j.ccell.2025.11.006

  View details for PubMedID 41386224

• Automated classification of cellular expression in multiplexed imaging data with Nimbus. Nature methods Rumberger, J. L., Greenwald, N. F., Ranek, J. S., Boonrat, P., Walker, C., Franzen, J., Varra, S. R., Kong, A., Sowers, C., Liu, C. C., Averbukh, I., Piyadasa, H., Vanguri, R., Nederlof, I., Wang, X. J., Van Valen, D., Kok, M., Bendall, S. C., Hollmann, T. J., Kainmueller, D., Angelo, M. 2025

  Abstract

  Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pretrained model that uses the underlying images to classify marker expression of individual cells as positive or negative across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M, and that Nimbus matches or exceeds the accuracy of previous approaches that must be retrained on each dataset. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference .

  View details for DOI 10.1038/s41592-025-02826-9

  View details for PubMedID 41062826

  View details for PubMedCentralID 8647621