Potchara Boonrat

All Publications

• Clone tracking through repeated malaria identifies high-fidelity memory CD4 T cell responses. Science immunology Nideffer, J., Bach, F., Nankya, F., Musinguzi, K., Borna, Š., Mantilla, M., Zedi, M., Garcia Romero, A., Gerungan, C., Yang, N., Kim, S., van der Ploeg, K., Camanag, K., Lopez, L., Nansubuga, E., Nankabirwa, J. I., Arinaitwe, E., Boonrat, P., Strubbe, S., Cepika, A. M., Takahashi, S., Dorsey, G., Greenhouse, B., Rodriguez-Barraquer, I., Kamya, M. R., Bacchetta, R., Ssewanyana, I., Haque, A., Roncarolo, M. G., Jagannathan, P. 2025; 10 (106): eads2957

  Abstract

  Few studies have tracked human CD4+ T cell clones through repeated infections. We used longitudinal single-cell RNA and T cell receptor (TCR) tracking to study the functional stability and memory potential of CD4+ T cell clonotypes during repeated Plasmodium falciparum (Pf) infections in Ugandan children and adults. Nearly all clonotypes displayed a strong preference for one of seven CD4+ subsets. This phenomenon of "clonal fidelity" was influenced by clonal expansion, linking T cell polarization and proliferation in vivo. Using clone tracking, we characterized subset-specific activation trajectories and identified antigen-specific clones. Type 1 regulatory T (TR1) cells accounted for nearly 90% of Pf-specific CD4+ T cells in blood. Tracking these clones longitudinally for hundreds of days, we observed malaria-induced expansion of TR1 effectors, long-term persistence of TR1 memory cells, and high-fidelity recall responses after reinfection. This work establishes clonal fidelity as a natural phenomenon and demonstrates the stable, long-term memory potential of TR1 cells.

  View details for DOI 10.1126/sciimmunol.ads2957

  View details for PubMedID 40279404

• The immunometabolic topography of tuberculosis granulomas governs cellular organization and bacterial control. bioRxiv : the preprint server for biology 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., Kirschner, D., Fortune, S., Bryson, B. D., Butler, J. R., Mattila, J. T., Flynn, J. L., Angelo, M. 2025

  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. We previously discovered that human TB granulomas are enriched for immunosuppressive factors typically associated with tumor-immune evasion, raising the intriguing possibility that they promote tolerance to infection. In this study, our goal was to identify the prime drivers for establishing this tolerogenic niche and to determine if the magnitude of this response correlates with bacterial persistence. To do this, we conducted a multimodal spatial analysis of 52 granulomas from 16 non-human primates (NHP) who were infected with low dose Mtb for 9-12 weeks. Notably, each granuloma's bacterial burden was individually quantified allowing us to directly ask how granuloma spatial structure and function relate to infection control. We found that a universal feature of TB granulomas was partitioning of the myeloid core into two distinct metabolic environments, one of which is hypoxic. This hypoxic environment associated with pathologic 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 correlated with worsened bacterial burden. We conclude that hypoxia governs immune cell state and organization within granulomas and is a potent driver of subverted immunity during TB.

  View details for DOI 10.1101/2025.02.18.638923

  View details for PubMedID 40027668

  View details for PubMedCentralID PMC11870603

• Automated classification of cellular expression in multiplexed imaging data with Nimbus. bioRxiv : the preprint server for biology 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., Hollmann, T. J., Kainmueller, D., Angelo, M. 2024

  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 pre-trained model that uses the underlying images to classify marker expression 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. 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 10.1101/2024.06.02.597062

  View details for PubMedID 38895405

  View details for PubMedCentralID PMC11185540

• High-resolution natural killer cell phenotyping by mass cytometry in pediatric transplant recipients Zhang, W., Pena, J. K., Boonrat, P., Harden, J. T., Esquivel, C. O., Martinez, O. M., Krams, S. M. WILEY. 2023

  View details for Web of Science ID 001002465500174