Michael Angelo

All Publications

• Multi-omic landscape of human gliomas from diagnosis to treatment and recurrence. bioRxiv : the preprint server for biology Piyadasa, H., Oberlton, B., Ribi, M., Ranek, J. S., Averbukh, I., Leow, K., Amouzgar, M., Liu, C. C., 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., 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) evolution, 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 (PXA), while most gliomas, including pediatric cases, express targetable tumor antigens in less than 50% of tumor cells, potentially explaining trial failures. Longitudinal 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 opens new avenues for glioma targeting, classification, outcome prediction, and a baseline of TME composition across all stages.

  View details for DOI 10.1101/2025.03.12.642624

  View details for PubMedID 40161803

  View details for PubMedCentralID PMC11952471

• 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

• Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition. bioRxiv : the preprint server for biology 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., Bai, Y., Piyadasa, H., Risom, T., Delmastro, A., Hartmann, F. J., Mangiante, L., Sotomayor-Vivas, C., Schumacher, T. N., Ma, Z., Bosse, M., van de Vijver, M. J., Tibshirani, R., Horlings, H. M., Curtis, C., Kok, M., Angelo, M. 2025

  Abstract

  Immune checkpoint inhibition (ICI) has fundamentally changed cancer treatment. However, only a minority of patients with metastatic triple negative breast cancer (TNBC) benefit from ICI, and the determinants of response remain largely unknown. To better understand the factors influencing patient outcome, we assembled a longitudinal cohort with tissue from multiple timepoints, including primary tumor, pre-treatment metastatic tumor, and on-treatment metastatic tumor from 117 patients treated with ICI (nivolumab) in the phase II TONIC trial. We used highly multiplexed imaging to quantify the subcellular localization of 37 proteins in each tumor. To extract meaningful information from the imaging data, we developed SpaceCat, a computational pipeline that quantifies features from imaging data such as cell density, cell diversity, spatial structure, and functional marker expression. We applied SpaceCat to 678 images from 294 tumors, generating more than 800 distinct features per tumor. Spatial features were more predictive of patient outcome, including features like the degree of mixing between cancer and immune cells, the diversity of the neighboring immune cells surrounding cancer cells, and the degree of T cell infiltration at the tumor border. Non-spatial features, including the ratio between T cell subsets and cancer cells and PD-L1 levels on myeloid cells, were also associated with patient outcome. Surprisingly, we did not identify robust predictors of response in the primary tumors. In contrast, the metastatic tumors had numerous features which predicted response. Some of these features, such as the cellular diversity at the tumor border, were shared across timepoints, but many of the features, such as T cell infiltration at the tumor border, were predictive of response at only a single timepoint. We trained multivariate models on all of the features in the dataset, finding that we could accurately predict patient outcome from the pre-treatment metastatic tumors, with improved performance using the on-treatment tumors. We validated our findings in matched bulk RNA-seq data, finding the most informative features from the on-treatment samples. Our study highlights the importance of profiling sequential tumor biopsies to understand the evolution of the tumor microenvironment, elucidating the temporal and spatial dynamics underlying patient responses and underscoring the need for further research on the prognostic role of metastatic tissue and its utility in stratifying patients for ICI.

  View details for DOI 10.1101/2025.01.26.634557

  View details for PubMedID 39975273

  View details for PubMedCentralID PMC11838242

• Highly multiplexed imaging reveals prognostic immune and stromal spatial biomarkers in breast cancer. JCI insight Eng, J. R., Bucher, E., Hu, Z., Walker, C. R., Risom, T., Angelo, M., Gonzalez-Ericsson, P., Sanders, M. E., Chakravarthy, A. B., Pietenpol, J. A., Gibbs, S. L., Sears, R. C., Chin, K. 2025

  Abstract

  Spatial profiling of tissues promises to elucidate tumor-microenvironment interactions and generate prognostic and predictive biomarkers. We analyzed single-cell, spatial data from three multiplex imaging technologies: cyclic immunofluorescence (CycIF) data we generated from 102 breast cancer patients with clinical follow-up, and publicly available imaging mass cytometry and multiplex ion-beam imaging datasets. Similar single-cell phenotyping results across imaging platforms enabled combined analysis of epithelial phenotypes to delineate prognostic subtypes among estrogen-receptor positive (ER+) patients. We utilized discovery and validation cohorts to identify biomarkers with prognostic value. Increased lymphocyte infiltration was independently associated with longer survival in triple-negative (TN) and high-proliferation ER+ breast tumors. An assessment of ten spatial analysis methods revealed robust spatial biomarkers. In ER+ disease, quiescent stromal cells close to tumor were abundant in good prognosis tumors, while tumor cell neighborhoods containing mixed fibroblast phenotypes were enriched in poor prognosis tumors. In TN disease, macrophage/tumor and B/T lymphocyte neighbors were enriched and lymphocytes were dispersed in good prognosis tumors, while tumor cell neighborhoods containing vimentin-positive fibroblasts were enriched in poor prognosis tumors. In conclusion, we generated comparable single-cell spatial proteomic data from several clinical cohorts to enable prognostic spatial biomarker identification and validation.

  View details for DOI 10.1172/jci.insight.176749

  View details for PubMedID 39808504

• Society for Immunotherapy of Cancer: updates and best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) image analysis and data sharing. Journal for immunotherapy of cancer Taube, J. M., Sunshine, J. C., Angelo, M., Akturk, G., Eminizer, M., Engle, L. L., Ferreira, C. S., Gnjatic, S., Green, B., Greenbaum, S., Greenwald, N. F., Hedvat, C. V., Hollmann, T. J., Jiménez-Sánchez, D., Korski, K., Lako, A., Parra, E. R., Rebelatto, M. C., Rimm, D. L., Rodig, S. J., Rodriguez-Canales, J., Roskes, J. S., Schalper, K. A., Schenck, E., Steele, K. E., Surace, M. J., Szalay, A. S., Tetzlaff, M. T., Wistuba, I. I., Yearley, J. H., Bifulco, C. B. 2025; 13 (1)

  Abstract

  Multiplex immunohistochemistry and immunofluorescence (mIHC/IF) are emerging technologies that can be used to help define complex immunophenotypes in tissue, quantify immune cell subsets, and assess the spatial arrangement of marker expression. mIHC/IF assays require concerted efforts to optimize and validate the multiplex staining protocols prior to their application on slides. The best practice guidelines for staining and validation of mIHC/IF assays across platforms were previously published by this task force. The current effort represents a complementary manuscript for mIHC/IF analysis focused on the associated image analysis and data management.The Society for Immunotherapy of Cancer convened a task force of pathologists and laboratory leaders from academic centers as well as experts from pharmaceutical and diagnostic companies to develop best practice guidelines for the quantitative image analysis of mIHC/IF output and data management considerations.Best-practice approaches for image acquisition, color deconvolution and spectral unmixing, tissue and cell segmentation, phenotyping, and algorithm verification are reviewed. Additional quality control (QC) measures such as batch-to-batch correction and QC for assembled images are also discussed. Recommendations for sharing raw outputs, processed results, key analysis programs and source code, and representative photomicrographs from mIHC/IF assays are included. Lastly, multi-institutional harmonization efforts are described.mIHC/IF technologies are maturing and are routinely included in research studies and moving towards clinical use. Guidelines for how to perform and standardize image analysis on mIHC/IF-stained slides will likely contribute to more comparable results across laboratories and pave the way for clinical implementation. A checklist encompassing these two-part guidelines for the generation of robust data from quantitative mIHC/IF assays will be provided in a third publication from this task force. While the current effort is mainly focused on best practices for characterizing the tumor microenvironment, these principles are broadly applicable to any mIHC/IF assay and associated image analysis.

  View details for DOI 10.1136/jitc-2024-008875

  View details for PubMedID 39779210

• Toward clinical applications of spatial-omics in cancer research. Nature cancer Walker, C., Angelo, M. 2024

  View details for DOI 10.1038/s43018-024-00868-0

  View details for PubMedID 39690225

  View details for PubMedCentralID 11294822

• Multi-ancestry GWAS of severe pregnancy nausea and vomiting identifies risk loci associated with appetite, insulin signaling, and brain plasticity. Research square Fejzo, M., Wang, X., Zöllner, J., Pujol-Gualdo, N., Laisk, T., Finer, S., van Heel, D. A., Brumpton, B., Bhatta, L., Hveem, K., Jasper, E. A., Velez Edwards, D. R., Hellwege, J. N., Edwards, T., Jarvik, G. P., Luo, Y., Khan, A., MacGibbon, K., Gao, Y., Ge, G., Averbukh, I., Soon, E., Angelo, M., Magnus, P., Johansson, S., Njølstad, P. R., Vaudel, M., Shu, C., Mancuso, N. 2024

  Abstract

  While most pregnancies are affected by nausea and vomiting, hyperemesis gravidarum (HG) is at the severe end of the clinical spectrum and is associated with dehydration, undernutrition, and adverse maternal, fetal, and child outcomes. Herein we performed a multi-ancestry genome-wide association study (GWAS) of severe nausea and vomiting of pregnancy of 10,974 cases and 461,461 controls across European, Asian, African, and Latino ancestries. We identified ten significantly associated loci, of which six were novel (SLITRK1, SYN3, IGSF11, FSHB, TCF7L2, and CDH9), and confirmed previous genome-wide significant associations with risk genes GDF15, IGFBP7, PGR, and GFRAL. In a spatiotemporal analysis of placental development, GDF15 and TCF7L2 were expressed primarily in extra villous trophoblast, and using a weighted linear model of maternal, paternal, and fetal effects, we confirmed opposing effects for GDF15 between maternal and fetal genotype. Conversely, IGFBP7 and PGR were primarily expressed in developing maternal spiral arteries during placentation, with effects limited to the maternal genome. Risk loci were found to be under significant evolutionary selection, with the strongest effects on nausea and vomiting mid-pregnancy. Selected loci were associated with abnormal pregnancy weight gain, pregnancy duration, birth weight, head circumference, and pre-eclampsia. Potential roles for candidate genes in appetite, insulin signaling, and brain plasticity provide new pathways to explore etiological mechanisms and novel therapeutic avenues.

  View details for DOI 10.21203/rs.3.rs-5487737/v1

  View details for PubMedID 39764105

  View details for PubMedCentralID PMC11702859

• Multi-ancestry GWAS of severe pregnancy nausea and vomiting identifies risk loci associated with appetite, insulin signaling, and brain plasticity. medRxiv : the preprint server for health sciences Fejzo, M., Wang, X., Zöllner, J., Pujol-Gualdo, N., Laisk, T., Finer, S., van Heel, D. A., Brumpton, B., Bhatta, L., Hveem, K., Jasper, E. A., Velez Edwards, D. R., Hellwege, J. N., Edwards, T., Jarvik, G. P., Luo, Y., Khan, A., MacGibbon, K., Gao, Y., Ge, G., Averbukh, I., Soon, E., Angelo, M., Magnus, P., Johansson, S., Njølstad, P. R., Vaudel, M., Shu, C., Mancuso, N. 2024

  Abstract

  While most pregnancies are affected by nausea and vomiting, hyperemesis gravidarum (HG) is at the severe end of the clinical spectrum and is associated with dehydration, undernutrition, and adverse maternal, fetal, and child outcomes. Herein we performed a multi-ancestry genome-wide association study (GWAS) of severe nausea and vomiting of pregnancy of 10,974 cases and 461,461 controls across European, Asian, African, and Latino ancestries. We identified ten significantly associated loci, of which six were novel (SLITRK1, SYN3, IGSF11, FSHB, TCF7L2, and CDH9), and confirmed previous genome-wide significant associations with risk genes GDF15, IGFBP7, PGR, and GFRAL. In a spatiotemporal analysis of placental development, GDF15 and TCF7L2 were expressed primarily in extra villous trophoblast, and using a weighted linear model of maternal, paternal, and fetal effects, we confirmed opposing effects for GDF15 between maternal and fetal genotype. Conversely, IGFBP7 and PGR were primarily expressed in developing maternal spiral arteries during placentation, with effects limited to the maternal genome. Risk loci were found to be under significant evolutionary selection, with the strongest effects on nausea and vomiting mid-pregnancy. Selected loci were associated with abnormal pregnancy weight gain, pregnancy duration, birth weight, head circumference, and pre-eclampsia. Potential roles for candidate genes in appetite, insulin signaling, and brain plasticity provide new pathways to explore etiological mechanisms and novel therapeutic avenues.

  View details for DOI 10.1101/2024.11.19.24317559

  View details for PubMedID 39606329

  View details for PubMedCentralID PMC11601681

• 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

• Germline-mediated immunoediting sculpts breast cancer subtypes and metastatic proclivity. Science (New York, N.Y.) Houlahan, K. E., Khan, A., Greenwald, N. F., Vivas, C. S., West, R. B., Angelo, M., Curtis, C. 2024; 384 (6699): eadh8697

  Abstract

  Tumors with the same diagnosis can have different molecular profiles and response to treatment. It remains unclear when and why these differences arise. Somatic genomic aberrations occur within the context of a highly variable germline genome. Interrogating 5870 breast cancer lesions, we demonstrated that germline-derived epitopes in recurrently amplified genes influence somatic evolution by mediating immunoediting. Individuals with a high germline-epitope burden in human epidermal growth factor receptor 2 (HER2/ERBB2) are less likely to develop HER2-positive breast cancer compared with other subtypes. The same holds true for recurrent amplicons defining three aggressive estrogen receptor (ER)-positive subgroups. Tumors that overcome such immune-mediated negative selection are more aggressive and demonstrate an "immune cold" phenotype. These data show that the germline genome plays a role in dictating somatic evolution.

  View details for DOI 10.1126/science.adh8697

  View details for PubMedID 38815010

• Insights and Opportunity Costs in Applying Spatial Biology to Study the Tumor Microenvironment. Cancer discovery Walker, C. R., Angelo, M. 2024: OF1-OF4

  Abstract

  The recent development of high-dimensional spatial omics tools has revealed the functional importance of the tumor microenvironment in driving tumor progression. Here, we discuss practical factors to consider when designing a spatial biology cohort and offer perspectives on the future of spatial biology research.

  View details for DOI 10.1158/2159-8290.CD-24-0348

  View details for PubMedID 38587535

• Author Correction: Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP). Nature cell biology Jain, S., Pei, L., Spraggins, J. M., Angelo, M., Carson, J. P., Gehlenborg, N., Ginty, F., Goncalves, J. P., Hagood, J. S., Hickey, J. W., Kelleher, N. L., Laurent, L. C., Lin, S., Lin, Y., Liu, H., Naba, A., Nakayasu, E. S., Qian, W., Radtke, A., Robson, P., Stockwell, B. R., Van de Plas, R., Vlachos, I. S., Zhou, M., HuBMAP Consortium, Borner, K., Snyder, M. P., Ahn, K. J., Allen, J., Anderson, D. M., Anderton, C. R., Curcio, C., Angelin, A., Arvanitis, C., Atta, L., Awosika-Olumo, D., Bahmani, A., Bai, H., Balderrama, K., Balzano, L., Bandyopadhyay, G., Bandyopadhyay, S., Bar-Joseph, Z., Barnhart, K., Barwinska, D., Becich, M., Becker, L., Becker, W., Bedi, K., Bendall, S., Benninger, K., Betancur, D., Bettinger, K., Billings, S., Blood, P., Bolin, D., Border, S., Bosse, M., Bramer, L., Brewer, M., Brusko, M., Bueckle, A., Burke, K., Burnum-Johnson, K., Butcher, E., Butterworth, E., Cai, L., Calandrelli, R., Caldwell, M., Campbell-Thompson, M., Cao, D., Cao-Berg, I., Caprioli, R., Caraccio, C., Caron, A., Carroll, M., Chadwick, C., Chen, A., Chen, D., Chen, F., Chen, H., Chen, J., Chen, L., Chen, L., Chiacchia, K., Cho, S., Chou, P., Choy, L., Cisar, C., Clair, G., Clarke, L., Clouthier, K. A., Colley, M. E., Conlon, K., Conroy, J., Contrepois, K., Corbett, A., Corwin, A., Cotter, D., Courtois, E., Cruz, A., Csonka, C., Czupil, K., Daiya, V., Dale, K., Davanagere, S. A., Dayao, M., de Caestecker, M. P., Decker, A., Deems, S., Degnan, D., Desai, T., Deshpande, V., Deutsch, G., Devlin, M., Diep, D., Dodd, C., Donahue, S., Dong, W., Dos Santos Peixoto, R., Duffy, M., Dufresne, M., Duong, T. E., Dutra, J., Eadon, M. T., El-Achkar, T. M., Enninful, A., Eraslan, G., Eshelman, D., Espin-Perez, A., Esplin, E. D., Esselman, A., Falo, L. D., Falo, L., Fan, J., Fan, R., Farrow, M. A., Farzad, N., Favaro, P., Fermin, J., Filiz, F., Filus, S., Fisch, K., Fisher, E., Fisher, S., Flowers, K., Flynn, W. F., Fogo, A. B., Fu, D. A., Fulcher, J., Fung, A., Furst, D., Gallant, M., Gao, F., Gao, Y., Gaulton, K., Gaut, J. P., Gee, J., Ghag, R. R., Ghazanfar, S., Ghose, S., Gisch, D., Gold, I., Gondalia, A., Gorman, B., Greenleaf, W., Greenwald, N., Gregory, B., Guo, R., Gupta, R., Hakimian, H., Haltom, J., Halushka, M., Han, K. S., Hanson, C., Harbury, P., Hardi, J., Harlan, L., Harris, R. C., Hartman, A., Heidari, E., Helfer, J., Helminiak, D., Hemberg, M., Henning, N., Herr, B. W., Ho, J., Holden-Wiltse, J., Hong, S., Hong, Y., Honick, B., Hood, G., Hu, P., Hu, Q., Huang, M., Huyck, H., Imtiaz, T., Isberg, O. G., Itkin, M., Jackson, D., Jacobs, M., Jain, Y., Jewell, D., Jiang, L., Jiang, Z. G., Johnston, S., Joshi, P., Ju, Y., Judd, A., Kagel, A., Kahn, A., Kalavros, N., Kalhor, K., Karagkouni, D., Karathanos, T., Karunamurthy, A., Katari, S., Kates, H., Kaushal, M., Keener, N., Keller, M., Kenney, M., Kern, C., Kharchenko, P., Kim, J., Kingsford, C., Kirwan, J., Kiselev, V., Kishi, J., Kitata, R. B., Knoten, A., Kollar, C., Krishnamoorthy, P., Kruse, A. R., Da, K., Kundaje, A., Kutschera, E., Kwon, Y., Lake, B. B., Lancaster, S., Langlieb, J., Lardenoije, R., Laronda, M., Laskin, J., Lau, K., Lee, H., Lee, M., Lee, M., Strekalova, Y. L., Li, D., Li, J., Li, J., Li, X., Li, Z., Liao, Y., Liaw, T., Lin, P., Lin, Y., Lindsay, S., Liu, C., Liu, Y., Liu, Y., Lott, M., Lotz, M., Lowery, L., Lu, P., Lu, X., Lucarelli, N., Lun, X., Luo, Z., Ma, J., Macosko, E., Mahajan, M., Maier, L., Makowski, D., Malek, M., Manthey, D., Manz, T., Margulies, K., Marioni, J., Martindale, M., Mason, C., Mathews, C., Maye, P., McCallum, C., McDonough, E., McDonough, L., Mcdowell, H., Meads, M., Medina-Serpas, M., Ferreira, R. M., Messinger, J., Metis, K., Migas, L. G., Miller, B., Mimar, S., Minor, B., Misra, R., Missarova, A., Mistretta, C., Moens, R., Moerth, E., Moffitt, J., Molla, G., Monroe, M., Monte, E., Morgan, M., Muraro, D., Murphy, B. R., Murray, E., Musen, M. A., Naglah, A., Nasamran, C., Neelakantan, T., Nevins, S., Nguyen, H., Nguyen, N., Nguyen, T., Nguyen, T., Nigra, D., Nofal, M., Nolan, G., Nwanne, G., O'Connor, M., Okuda, K., Olmer, M., O'Neill, K., Otaluka, N., Pang, M., Parast, M., Pasa-Tolic, L., Paten, B., Patterson, N. H., Peng, T., Phillips, G., Pichavant, M., Piehowski, P., Pilner, H., Pingry, E., Pita-Juarez, Y., Plevritis, S., Ploumakis, A., Pouch, A., Pryhuber, G., Puerto, J., Qaurooni, D., Qin, L., Quardokus, E. M., Rajbhandari, P., Rakow-Penner, R., Ramasamy, R., Read, D., Record, E. G., Reeves, D., Ricarte, A., Rodriguez-Soto, A., Ropelewski, A., Rosario, J., Roselkis, M., Rowe, D., Roy, T. K., Ruffalo, M., Ruschman, N., Sabo, A., Sachdev, N., Saka, S., Salamon, D., Sarder, P., Sasaki, H., Satija, R., Saunders, D., Sawka, R., Schey, K., Schlehlein, H., Scholten, D., Schultz, S., Schwartz, L., Schwenk, M., Scibek, R., Segre, A., Serrata, M., Shands, W., Shen, X., Shendure, J., Shephard, H., Shi, L., Shi, T., Shin, D., Shirey, B., Sibilla, M., Silber, M., Silverstein, J., Simmel, D., Simmons, A., Singhal, D., Sivajothi, S., Smits, T., Soncin, F., Song, Q., Stanley, V., Stuart, T., Su, H., Su, P., Sun, X., Surrette, C., Swahn, H., Tan, K., Teichmann, S., Tejomay, A., Tellides, G., Thomas, K., Thomas, T., Thompson, M., Tian, H., Tideman, L., Trapnell, C., Tsai, A. G., Tsai, C., Tsai, L., Tsui, E., Tsui, T., Tung, J., Turner, M., Uranic, J., Vaishnav, E. D., Varra, S. R., Vaskivskyi, V., Velickovic, D., Velickovic, M., Verheyden, J., Waldrip, J., Wallace, D., Wan, X., Wang, A., Wang, F., Wang, M., Wang, S., Wang, X., Wasserfall, C., Wayne, L., Webber, J., Weber, G. M., Wei, B., Wei, J., Weimer, A., Welling, J., Wen, X., Wen, Z., Williams, M., Winfree, S., Winograd, N., Woodard, A., Wright, D., Wu, F., Wu, P., Wu, Q., Wu, X., Xing, Y., Xu, T., Yang, M., Yang, M., Yap, J., Ye, D. H., Yin, P., Yuan, Z., Yun, C. J., Zahraei, A., Zemaitis, K., Zhang, B., Zhang, C., Zhang, C., Zhang, C., Zhang, K., Zhang, S., Zhang, T., Zhang, Y., Zhao, B., Zhao, W., Zheng, J. W., Zhong, S., Zhu, B., Zhu, C., Zhu, D., Zhu, Q., Zhu, Y. 2024

  View details for DOI 10.1038/s41556-024-01384-0

  View details for PubMedID 38429479

• Harmonizing the Generation and Pre-publication Stewardship of FAIR Image data. ArXiv Bialy, N., Alber, F., Andrews, B., Angelo, M., Beliveau, B., Bintu, L., Boettiger, A., Boehm, U., Brown, C. M., Maina, M. B., Chambers, J. J., Cimini, B. A., Eliceiri, K., Errington, R., Faklaris, O., Gaudreault, N., Germain, R. N., Goscinski, W., Grunwald, D., Halter, M., Hanein, D., Hickey, J. W., Lacoste, J., Laude, A., Lundberg, E., Ma, J., Malacrida, L., Moore, J., Nelson, G., Neumann, E. K., Nitschke, R., Onami, S., Pimentel, J. A., Plant, A. L., Radtke, A. J., Sabata, B., Schapiro, D., Schöneberg, J., Spraggins, J. M., Sudar, D., Adrien Maria Vierdag, W. M., Volkmann, N., Wählby, C., Wang, S. S., Yaniv, Z., Strambio-De-Castillia, C. 2024

  Abstract

  Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled "Enabling Global Image Data Sharing in the Life Sciences," which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges, and democratize access to common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.

  View details for DOI 10.1242/jcs.254151

  View details for PubMedID 38351940

  View details for PubMedCentralID PMC10862930

• Spatiotemporal Immune Cells Profiling in Gastrointestinal Tissue Biopsies to Detect Oral Immunotherapy Induced Changes in Peanut Allergic Individuals Kaushik, A., Angoshtari, R., Kwow, S., Kambham, N., Fernandez-Becker, N., Manohar, M., Angelo, M., Galli, S., Nadeau, K., Dekruyff, R., Chinthrajah, S. MOSBY-ELSEVIER. 2024: AB371

  View details for Web of Science ID 001267526000853

• Single-Cell Imaging Maps Inflammatory Cell Subsets to Pulmonary Arterial Hypertension Vasculopathy. American journal of respiratory and critical care medicine Ferrian, S., Cao, A., McCaffrey, E. F., Saito, T., Greenwald, N. F., Nicolls, M. R., Bruce, T., Zamanian, R. T., Del Rosario, P., Rabinovitch, M., Angelo, M. 2023

  Abstract

  Rationale: Elucidating the immune landscape within and surrounding pulmonary arteries (PAs) is critical in understanding immune-driven vascular pathology in pulmonary arterial hypertension (PAH). Although more severe vascular pathology is often observed in hereditary (H)PAH patients with BMPR2 mutations, the involvement of specific immune cell subsets remains unclear. Methods: We used cutting-edge multiplexed ion beam imaging by time-of-flight (MIBI-TOF) to compare PAs and adjacent tissue in PAH lungs (idiopathic (I)PAH and HPAH) with unused donor lungs. Measurements: We quantified immune cells' proximity and abundance, focusing on those linked to vascular pathology, and evaluated their impact on pulmonary arterial smooth muscle cells (SMCs) and endothelial cells (ECs). Results: Distinct immune infiltration patterns emerged between PAH subtypes, with intramural involvement independently linked to PA occlusive changes. Notably, we identified monocyte-derived dendritic cells (mo-DCs) within PA subendothelial and adventitial regions, influencing vascular remodeling by promoting SMC proliferation and suppressing endothelial gene expression across PAH subtypes. In HPAH patients, pronounced immune dysregulation encircled PA walls, characterized by heightened perivascular inflammation involving TIM-3+ T cells. This correlated with an expanded DC subset expressing IDO-1, TIM-3, and SAMHD1, alongside increased neutrophils, SMCs, and α-SMA+ECs, reinforcing the severity of pulmonary vascular lesions. Conclusions: This study presents the first architectural map of PAH lungs, connecting immune subsets not only with specific PA lesions but also with heightened severity in HPAH compared to IPAH. Our findings emphasize the therapeutic potential of targeting mo-DCs, neutrophils, cellular interactions, and immune responses to alleviate severe vascular pathology in IPAH and HPAH.

  View details for DOI 10.1164/rccm.202209-1761OC

  View details for PubMedID 37934691

• SPATIALLY-RESOLVED TRANSCRIPTOME ANALYSIS OF BRAIN METASTATIC BREAST CANCER REVEAL KEY MEDIATORS OF BRAIN-TROPIC METASTATIC POTENTIAL Umeh-Garcia, M., Godfrey, B., Perez, P., Varma, S., Ahmadian, S., Toland, A., Granucci, M., Averbukh, I., Tian, L., West, R., Angelo, M., Plevritis, S., Gephart, M. OXFORD UNIV PRESS INC. 2023

  View details for Web of Science ID 001115245401351

• A platform-independent framework for phenotyping of multiplex tissue imaging data. PLoS computational biology Ahmadian, M., Rickert, C., Minic, A., Wrobel, J., Bitler, B. G., Xing, F., Angelo, M., Hsieh, E. W., Ghosh, D., Jordan, K. R. 2023; 19 (9): e1011432

  Abstract

  Multiplex imaging is a powerful tool to analyze the structural and functional states of cells in their morphological and pathological contexts. However, hypothesis testing with multiplex imaging data is a challenging task due to the extent and complexity of the information obtained. Various computational pipelines have been developed and validated to extract knowledge from specific imaging platforms. A common problem with customized pipelines is their reduced applicability across different imaging platforms: Every multiplex imaging technique exhibits platform-specific characteristics in terms of signal-to-noise ratio and acquisition artifacts that need to be accounted for to yield reliable and reproducible results. We propose a pixel classifier-based image preprocessing step that aims to minimize platform-dependency for all multiplex image analysis pipelines. Signal detection and noise reduction as well as artifact removal can be posed as a pixel classification problem in which all pixels in multiplex images can be assigned to two general classes of either I) signal of interest or II) artifacts and noise. The resulting feature representation maps contain pixel-scale representations of the input data, but exhibit significantly increased signal-to-noise ratios with normalized pixel values as output data. We demonstrate the validity of our proposed image preprocessing approach by comparing the results of two well-accepted and widely-used image analysis pipelines.

  View details for DOI 10.1371/journal.pcbi.1011432

  View details for PubMedID 37733781

• Loss-of-function mutations in Dnmt3a and Tet2 lead to accelerated atherosclerosis and concordant macrophage phenotypes. Nature cardiovascular research Rauch, P. J., Gopakumar, J., Silver, A. J., Nachun, D., Ahmad, H., McConkey, M., Nakao, T., Bosse, M., Rentz, T., Vivanco Gonzalez, N., Greenwald, N. F., McCaffrey, E. F., Khair, Z., Gopakumar, M., Rodrigues, K. B., Lin, A. E., Sinha, E., Fefer, M., Cohen, D. N., Vromman, A., Shvartz, E., Sukhova, G., Bendall, S., Angelo, M., Libby, P., Ebert, B. L., Jaiswal, S. 2023; 2 (9): 805-818

  Abstract

  Clonal hematopoiesis of indeterminate potential (CHIP) is defined by the presence of a cancer-associated somatic mutation in white blood cells in the absence of overt hematological malignancy. It arises most commonly from loss-of-function mutations in the epigenetic regulators DNMT3A and TET2. CHIP predisposes to both hematological malignancies and atherosclerotic cardiovascular disease in humans. Here we demonstrate that loss of Dnmt3a in myeloid cells increased murine atherosclerosis to a similar degree as previously seen with loss of Tet2. Loss of Dnmt3a enhanced inflammation in macrophages in vitro and generated a distinct adventitial macrophage population in vivo which merges a resident macrophage profile with an inflammatory cytokine signature. These changes surprisingly phenocopy the effect of loss of Tet2. Our results identify a common pathway promoting heightened innate immune cell activation with loss of either gene, providing a biological basis for the excess atherosclerotic disease burden in carriers of these two most prevalent CHIP mutations.

  View details for DOI 10.1038/s44161-023-00326-7

  View details for PubMedID 39196062

  View details for PubMedCentralID 8050831

• Loss-of-function mutations in <i>Dnmt3a</i> and <i>Tet2</i> lead to accelerated atherosclerosis and concordant macrophage phenotypes NATURE CARDIOVASCULAR RESEARCH Rauch, P. J., Gopakumar, J., Silver, A. J., Nachun, D., Ahmad, H., Mcconkey, M., Nakao, T., Bosse, M., Rentz, T., Gonzalez, N., Greenwald, N. F., Mccaffrey, E. F., Khair, Z., Gopakumar, M., Rodrigues, K. B., Lin, A. E., Sinha, E., Fefer, M., Cohen, D. N., Vromman, A., Shvartz, E., Sukhova, G., Bendall, S., Angelo, M., Libby, P., Ebert, B. L., Jaiswal, S. 2023; 2 (9): 805-+

  View details for DOI 10.1038/s44161-023-00326-7

  View details for Web of Science ID 001127053400010

• Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering. Nature communications Liu, C. C., Greenwald, N. F., Kong, A., McCaffrey, E. F., Leow, K. X., Mrdjen, D., Cannon, B. J., Rumberger, J. L., Varra, S. R., Angelo, M. 2023; 14 (1): 4618

  Abstract

  While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes, imaging studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside of cells, such as the extracellular matrix. Here, we describe a pipeline, Pixie, that achieves robust and quantitative annotation of pixel-level features using unsupervised clustering and show its application across a variety of biological contexts and multiplexed imaging platforms. Furthermore, current cell phenotyping strategies that rely on unsupervised clustering can be labor intensive and require large amounts of manual cluster adjustments. We demonstrate how pixel clusters that lie within cells can be used to improve cell annotations. We comprehensively evaluate pre-processing steps and parameter choices to optimize clustering performance and quantify the reproducibility of our method. Importantly, Pixie is open source and easily customizable through a user-friendly interface.

  View details for DOI 10.1038/s41467-023-40068-5

  View details for PubMedID 37528072

  View details for PubMedCentralID 6086938

• Advances and prospects for the Human BioMolecular Atlas Program (HuBMAP). Nature cell biology Jain, S., Pei, L., Spraggins, J. M., Angelo, M., Carson, J. P., Gehlenborg, N., Ginty, F., Gonçalves, J. P., Hagood, J. S., Hickey, J. W., Kelleher, N. L., Laurent, L. C., Lin, S., Lin, Y., Liu, H., Naba, A., Nakayasu, E. S., Qian, W. J., Radtke, A., Robson, P., Stockwell, B. R., Van de Plas, R., Vlachos, I. S., Zhou, M., Börner, K., Snyder, M. P. 2023

  Abstract

  The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.

  View details for DOI 10.1038/s41556-023-01194-w

  View details for PubMedID 37468756

  View details for PubMedCentralID 8238499

• Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology. Nature communications Bai, Y., Zhu, B., Oliveria, J., Cannon, B. J., Feyaerts, D., Bosse, M., Vijayaragavan, K., Greenwald, N. F., Phillips, D., Schurch, C. M., Naik, S. M., Ganio, E. A., Gaudilliere, B., Rodig, S. J., Miller, M. B., Angelo, M., Bendall, S. C., Rovira-Clave, X., Nolan, G. P., Jiang, S. 2023; 14 (1): 4013

  Abstract

  Cellular organization and functions encompass multiple scales in vivo. Emerging high-plex imaging technologies are limited in resolving subcellular biomolecular features. Expansion Microscopy (ExM) and related techniques physically expand samples for enhanced spatial resolution, but are challenging to be combined with high-plex imaging technologies to enable integrative multiscaled tissue biology insights. Here, we introduce Expand and comPRESS hydrOgels (ExPRESSO), an ExM framework that allows high-plex protein staining, physical expansion, and removal of water, while retaining the lateral tissue expansion. We demonstrate ExPRESSO imaging of archival clinical tissue samples on Multiplexed Ion Beam Imaging and Imaging Mass Cytometry platforms, with detection capabilities of>40 markers. Application of ExPRESSO on archival human lymphoid and brain tissues resolved tissue architecture at the subcellular level, particularly that of the blood-brain barrier. ExPRESSO hence provides a platform for extending the analysis compatibility of hydrogel-expanded biospecimensto mass spectrometry, with minimal modifications to protocols and instrumentation.

  View details for DOI 10.1038/s41467-023-39616-w

  View details for PubMedID 37419873

• A spatially resolved timeline of the human maternal-fetal interface. Nature Greenbaum, S., Averbukh, I., Soon, E., Rizzuto, G., Baranski, A., Greenwald, N. F., Kagel, A., Bosse, M., Jaswa, E. G., Khair, Z., Kwok, S., Warshawsky, S., Piyadasa, H., Goldston, M., Spence, A., Miller, G., Schwartz, M., Graf, W., Van Valen, D., Winn, V. D., Hollmann, T., Keren, L., van de Rijn, M., Angelo, M. 2023; 619 (7970): 595-605

  Abstract

  Beginning in the first trimester, fetally derived extravillous trophoblasts (EVTs) invade the uterus and remodel its spiral arteries, transforming them into large, dilated blood vessels. Several mechanisms have been proposed to explain how EVTs coordinate with the maternal decidua to promote a tissue microenvironment conducive to spiral artery remodelling (SAR)1-3. However, it remains a matter of debate regarding which immune and stromal cells participate in these interactions and how this evolves with respect to gestational age. Here we used a multiomics approach, combining the strengths of spatial proteomics and transcriptomics, to construct a spatiotemporal atlas of the human maternal-fetal interface in the first half of pregnancy. We used multiplexed ion beam imaging by time-of-flight and a 37-plex antibody panel to analyse around 500,000 cells and 588 arteries within intact decidua from 66 individuals between 6 and 20 weeks of gestation, integrating this dataset with co-registered transcriptomics profiles. Gestational age substantially influenced the frequency of maternal immune and stromal cells, with tolerogenic subsets expressing CD206, CD163, TIM-3, galectin-9 and IDO-1 becoming increasingly enriched and colocalized at later time points. By contrast, SAR progression preferentially correlated with EVT invasion and was transcriptionally defined by 78 gene ontology pathways exhibiting distinct monotonic and biphasic trends. Last, we developed an integrated model of SAR whereby invasion is accompanied by the upregulation of pro-angiogenic, immunoregulatory EVT programmes that promote interactions with the vascular endothelium while avoiding the activation of maternal immune cells.

  View details for DOI 10.1038/s41586-023-06298-9

  View details for PubMedID 37468587

  View details for PubMedCentralID PMC10356615

• Synthesis, Characterization, and Applications of a Superior Dendrimer-Based Polymer for Multiplexed Ion Beam Imaging Time-of-Flight Analysis. Biomacromolecules Kumar, R., Liu, C. C., Bendall, S. C., Angelo, M. 2023

  Abstract

  High-dimensional single-cell mass spectrometric imaging techniques such as multiplexed ion beam imaging by time-of-flight mass spectrometry (MIBI-TOF), imaging mass cytometry (IMC), and flow cytometry-based CyTOF utilize antibodies conjugated to linear metal-chelating polymers. Here, we report on the synthesis and characterization of a dendrimer-based polymer and its utilization in tissue imaging using MIBI-TOF. We compared the staining performance in FFPE tissue of antibodies for lineage-specific immune proteins (CD20, CD3, CD45, FoxP3) that were conjugated with dendrimer or linear polymer. Staining of serial tissue sections with dendron-conjugated and linear-polymer-conjugated antibodies revealed comparable avidities of dendrons and linear polymers with log2 (ratio of mean positive pixel intensity of staining for linear polymers to dendrons) within the range ±0.25. Interestingly, dendron-conjugated antibodies were observed to have some advantages over linear polymer-conjugated antibodies. For example, tissue staining of a nuclear protein, FoxP3 with dendron-conjugated antibodies showed notably less background staining than that of linear-polymer-conjugated antibodies. Additionally, dendron-conjugated antibodies did not exhibit off-target cytosolic binding in neural tissue typically observed when using linear polymer conjugates. Taken together, this work provides a versatile framework for using third-generation dendron-conjugated antibodies with improved staining over conventional linear polymers.

  View details for DOI 10.1021/acs.biomac.3c00174

  View details for PubMedID 37352475

• Spatial proteomics reveals human microglial states shaped by anatomy and neuropathology. Research square Mrdjen, D., Amouzgar, M., Cannon, B., Liu, C., Spence, A., McCaffrey, E., Bharadwaj, A., Tebaykin, D., Bukhari, S., Hartmann, F. J., Kagel, A., Vijayaragavan, K., Oliveria, J. P., Yakabi, K., Serrano, G. E., Corrada, M. M., Kawas, C. H., Camacho, C., Bosse, M., Tibshirani, R., Beach, T. G., Angelo, M., Montine, T., Bendall, S. C. 2023

  Abstract

  Microglia are implicated in aging, neurodegeneration, and Alzheimer's disease (AD). Traditional, low-plex, imaging methods fall short of capturing in situ cellular states and interactions in the human brain. We utilized Multiplexed Ion Beam Imaging (MIBI) and data-driven analysis to spatially map proteomic cellular states and niches in healthy human brain, identifying a spectrum of microglial profiles, called the microglial state continuum (MSC). The MSC ranged from senescent-like to active proteomic states that were skewed across large brain regions and compartmentalized locally according to their immediate microenvironment. While more active microglial states were proximal to amyloid plaques, globally, microglia significantly shifted towards a, presumably, dysfunctional low MSC in the AD hippocampus, as confirmed in an independent cohort (n=26). This provides an in situ single cell framework for mapping human microglial states along a continuous, shifting existence that is differentially enriched between healthy brain regions and disease, reinforcing differential microglial functions overall.

  View details for DOI 10.21203/rs.3.rs-2987263/v1

  View details for PubMedID 37398389

  View details for PubMedCentralID PMC10312937

• Spatial proteomics of tumor microenvironments reveal why location matters. Nature immunology Piyadasa, H., Angelo, M., Bendall, S. C. 2023

  View details for DOI 10.1038/s41590-023-01471-8

  View details for PubMedID 36959293

  View details for PubMedCentralID 5998822

• Germline-mediated immunoediting sculpts breast cancer subtypes and metastatic proclivity. bioRxiv : the preprint server for biology Houlahan, K. E., Khan, A., Greenwald, N. F., West, R. B., Angelo, M., Curtis, C. 2023

  Abstract

  Cancer represents a broad spectrum of molecularly and morphologically diverse diseases. Individuals with the same clinical diagnosis can have tumors with drastically different molecular profiles and clinical response to treatment. It remains unclear when these differences arise during disease course and why some tumors are addicted to one oncogenic pathway over another. Somatic genomic aberrations occur within the context of an individual's germline genome, which can vary across millions of polymorphic sites. An open question is whether germline differences influence somatic tumor evolution. Interrogating 3,855 breast cancer lesions, spanning pre-invasive to metastatic disease, we demonstrate that germline variants in highly expressed and amplified genes influence somatic evolution by modulating immunoediting at early stages of tumor development. Specifically, we show that the burden of germline-derived epitopes in recurrently amplified genes selects against somatic gene amplification in breast cancer. For example, individuals with a high burden of germline-derived epitopes in ERBB2, encoding human epidermal growth factor receptor 2 (HER2), are significantly less likely to develop HER2-positive breast cancer compared to other subtypes. The same holds true for recurrent amplicons that define four subgroups of ER-positive breast cancers at high risk of distant relapse. High epitope burden in these recurrently amplified regions is associated with decreased likelihood of developing high risk ER-positive cancer. Tumors that overcome such immune-mediated negative selection are more aggressive and demonstrate an "immune cold" phenotype. These data show the germline genome plays a previously unappreciated role in dictating somatic evolution. Exploiting germline-mediated immunoediting may inform the development of biomarkers that refine risk stratification within breast cancer subtypes.

  View details for DOI 10.1101/2023.03.15.532870

  View details for PubMedID 36993286

  View details for PubMedCentralID PMC10055121

• Dynamic CD8+ T cell responses to cancer immunotherapy in human regional lymph nodes are disrupted in metastatic lymph nodes. Cell Rahim, M. K., Okholm, T. L., Jones, K. B., McCarthy, E. E., Liu, C. C., Yee, J. L., Tamaki, S. J., Marquez, D. M., Tenvooren, I., Wai, K., Cheung, A., Davidson, B. R., Johri, V., Samad, B., O'Gorman, W. E., Krummel, M. F., van Zante, A., Combes, A. J., Angelo, M., Fong, L., Algazi, A. P., Ha, P., Spitzer, M. H. 2023; 186 (6): 1127-1143.e18

  Abstract

  CD8+ T cell responses are critical for anti-tumor immunity. While extensively profiled in the tumor microenvironment, recent studies in mice identified responses in lymph nodes (LNs) as essential; however, the role of LNs in human cancer patients remains unknown. We examined CD8+ T cells in human head and neck squamous cell carcinomas, regional LNs, and blood using mass cytometry, single-cell genomics, and multiplexed ion beam imaging. We identified progenitor exhausted CD8+ T cells (Tpex) that were abundant in uninvolved LN and clonally related to terminally exhausted cells in the tumor. After anti-PD-L1 immunotherapy, Tpex in uninvolved LNs reduced in frequency but localized near dendritic cells and proliferating intermediate-exhausted CD8+ T cells (Tex-int), consistent with activation and differentiation. LN responses coincided with increased circulating Tex-int. In metastatic LNs, these response hallmarks were impaired, with immunosuppressive cellular niches. Our results identify important roles for LNs in anti-tumor immune responses in humans.

  View details for DOI 10.1016/j.cell.2023.02.021

  View details for PubMedID 36931243

• Multiplexed Tissue Imaging for Immune Cells Profiling During Peanut Allergy Immunotherapy Kaushik, A., Angoshtari, R., Kwow, S., Kambham, N., Fernandez-Becker, N., Manohar, M., Angelo, M., Galli, S., Nadeau, K., Dekruyff, R., Chinthrajah, S. MOSBY-ELSEVIER. 2023: AB34

  View details for Web of Science ID 000991651900101

• Characterizing N-glycan profiles of DCIS progression using tissue imaging MALDI mass spectrometry Wallace, E. N., Grimsley, G., Strand, S. H., Angelo, R., Colditz, G., Hwang, E., West, R., Marks, J. R., Angel, P. M., Drake, R. R. AMER ASSOC CANCER RESEARCH. 2022

  View details for Web of Science ID 000896891900064

• Characterizing N-glycan profiles of DCIS progression using tissue imaging MALDI mass spectrometry. Wallace, E. N., Grimsley, G., Strand, S. H., Angelo, R., Colditz, G., Hwang, E., West, R., Marks, J. R., Angel, P. M., Drake, R. R. AMER ASSOC CANCER RESEARCH. 2022: 8-9

  View details for Web of Science ID 000920264100022

• A SOX9-B7x axis safeguards dedifferentiated tumor cells from immunosurveillance to enable DCIS progression Wallace, E. N., Grimsley, G., Strand, S. H., Angelo, R., Colditz, G., Hwang, E., West, R., Marks, J. R., Angel, P. M., Drake, R. R. AMER ASSOC CANCER RESEARCH. 2022

  View details for Web of Science ID 000896891900039

• Discrete regulation of the collagen proteome among pathological features in DCIS and invasive breast cancer by mass spectrometry tissue imaging Hulahan, T. S., Wallace, E. N., Strand, S. H., Angelo, R., Colditz, G., Hwang, E., West, R., Spruill, L., Marks, J. R., Drake, R. R., Angel, P. M. AMER ASSOC CANCER RESEARCH. 2022

  View details for Web of Science ID 000929028800054

• Discrete regulation of the collagen proteome among pathological features in DCIS and invasive breast cancer by mass spectrometry tissue imaging Hulahan, T. S., Wallace, E. N., Strand, S. H., Angelo, R., Colditz, G., Hwang, E., West, R., Spruill, L., Marks, J. R., Drake, R. R., Angel, P. M. AMER ASSOC CANCER RESEARCH. 2022

  View details for Web of Science ID 000896891900050

• Gestationally dependent immune organization at the maternal-fetal interface. Cell reports Moore, A. R., Vivanco Gonzalez, N., Plummer, K. A., Mitchel, O. R., Kaur, H., Rivera, M., Collica, B., Goldston, M., Filiz, F., Angelo, M., Palmer, T. D., Bendall, S. C. 2022; 41 (7): 111651

  Abstract

  The immune system and placenta have a dynamic relationship across gestation to accommodate fetal growth and development. High-resolution characterization of this maternal-fetal interface is necessary to better understand the immunology of pregnancy and its complications. We developed a single-cell framework to simultaneously immuno-phenotype circulating, endovascular, and tissue-resident cells at the maternal-fetal interface throughout gestation, discriminating maternal and fetal contributions. Our data reveal distinct immune profiles across the endovascular and tissue compartments with tractable dynamics throughout gestation that respond to a systemic immune challenge in a gestationally dependent manner. We uncover a significant role for the innate immune system where phagocytes and neutrophils drive temporal organization of the placenta through remarkably diverse populations, including PD-L1+ subsets having compartmental and early gestational bias. Our approach and accompanying datasets provide a resource for additional investigations into gestational immunology and evoke a more significant role for the innate immune system in establishing the microenvironment of early pregnancy.

  View details for DOI 10.1016/j.celrep.2022.111651

  View details for PubMedID 36384130

• Molecular classification and biomarkers of clinical outcome in breast ductal carcinoma in situ: Analysis of TBCRC 038 and RAHBT cohorts. Cancer cell Strand, S. H., Rivero-Gutierrez, B., Houlahan, K. E., Seoane, J. A., King, L. M., Risom, T., Simpson, L. A., Vennam, S., Khan, A., Cisneros, L., Hardman, T., Harmon, B., Couch, F., Gallagher, K., Kilgore, M., Wei, S., DeMichele, A., King, T., McAuliffe, P. F., Nangia, J., Lee, J., Tseng, J., Storniolo, A. M., Thompson, A. M., Gupta, G. P., Burns, R., Veis, D. J., DeSchryver, K., Zhu, C., Matusiak, M., Wang, J., Zhu, S. X., Tappenden, J., Ding, D. Y., Zhang, D., Luo, J., Jiang, S., Varma, S., Anderson, L., Straub, C., Srivastava, S., Curtis, C., Tibshirani, R., Angelo, R. M., Hall, A., Owzar, K., Polyak, K., Maley, C., Marks, J. R., Colditz, G. A., Hwang, E. S., West, R. B. 2022

  Abstract

  Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We perform multiscale, integrated molecular profiling of DCIS with clinical outcomes by analyzing 774 DCIS samples from 542 patients with 7.3 years median follow-up from the Translational Breast Cancer Research Consortium 038 study and the Resource of Archival Breast Tissue cohorts. We identify 812 genes associated with ipsilateral recurrence within 5 years from treatment and develop a classifier that predicts DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions are identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome.

  View details for DOI 10.1016/j.ccell.2022.10.021

  View details for PubMedID 36400020

• Single-cell spatial proteomic imaging for human neuropathology. Acta neuropathologica communications Vijayaragavan, K., Cannon, B. J., Tebaykin, D., Bosse, M., Baranski, A., Oliveria, J. P., Bukhari, S. A., Mrdjen, D., Corces, M. R., McCaffrey, E. F., Greenwald, N. F., Sigal, Y., Marquez, D., Khair, Z., Bruce, T., Goldston, M., Bharadwaj, A., Montine, K. S., Angelo, R. M., Montine, T. J., Bendall, S. C. 2022; 10 (1): 158

  Abstract

  Neurodegenerative disorders are characterized by phenotypic changes and hallmark proteopathies. Quantifying these in archival human brain tissues remains indispensable for validating animal models and understanding disease mechanisms. We present a framework for nanometer-scale, spatial proteomics with multiplex ion beam imaging (MIBI) for capturing neuropathological features. MIBI facilitated simultaneous, quantitative imaging of 36 proteins on archival human hippocampus from individuals spanning cognitively normal to dementia. Customized analysis strategies identified cell types and proteopathies in the hippocampus across stages of Alzheimer's disease (AD) neuropathologic change. We show microglia-pathologic tau interactions in hippocampal CA1 subfield in AD dementia. Data driven, sample independent creation of spatial proteomic regions identified persistent neurons in pathologic tau neighborhoods expressing mitochondrial protein MFN2, regardless of cognitive status, suggesting a survival advantage. Our study revealed unique insights from multiplexed imaging and data-driven approaches for neuropathologic analysis and serves broadly as a methodology for spatial proteomic analysis of archival human neuropathology. TEASER: Multiplex Ion beam Imaging enables deep spatial phenotyping of human neuropathology-associated cellular and disease features.

  View details for DOI 10.1186/s40478-022-01465-x

  View details for PubMedID 36333818

• Spatial epitope barcoding reveals clonal tumor patch behaviors. Cancer cell Rovira-Clave, X., Drainas, A. P., Jiang, S., Bai, Y., Baron, M., Zhu, B., Dallas, A. E., Lee, M. C., Chu, T. P., Holzem, A., Ayyagari, R., Bhattacharya, D., McCaffrey, E. F., Greenwald, N. F., Markovic, M., Coles, G. L., Angelo, M., Bassik, M. C., Sage, J., Nolan, G. P. 2022

  Abstract

  Intratumoral heterogeneity is a seminal feature of human tumors contributing to tumor progression and response to treatment. Current technologies are still largely unsuitable to accurately track phenotypes and clonal evolution within tumors, especially in response to genetic manipulations. Here, we developed epitopes for imaging using combinatorial tagging (EpicTags), which we coupled to multiplexed ion beam imaging (EpicMIBI) for in situ tracking of barcodes within tissue microenvironments. Using EpicMIBI, we dissected the spatial component of cell lineages and phenotypes in xenograft models of small cell lung cancer. We observed emergent properties from mixed clones leading to the preferential expansion of clonal patches for both neuroendocrine and non-neuroendocrine cancer cell states in these models. In a tumor model harboring a fraction of PTEN-deficient cancer cells, we observed a non-autonomous increase of clonal patch size in PTEN wild-type cancer cells. EpicMIBI facilitates in situ interrogation of cell-intrinsic and cell-extrinsic processes involved in intratumoral heterogeneity.

  View details for DOI 10.1016/j.ccell.2022.09.014

  View details for PubMedID 36240778

• Role of IDO-signaling blockade in reactivation of TB in latent Rhesus macaques co-infected with SIV Singh, B., Moodley, C., Singh, D., Ganatra, S., McCaffrey, E., Angelo, M., Kaushal, D., Mehra, S. WILEY. 2022: 323

  View details for Web of Science ID 000852720100073

• Combined protein and nucleic acid imaging reveals virus-dependent B cell and macrophage immunosuppression of tissue microenvironments. Immunity Jiang, S., Chan, C. N., Rovira-Clave, X., Chen, H., Bai, Y., Zhu, B., McCaffrey, E., Greenwald, N. F., Liu, C., Barlow, G. L., Weirather, J. L., Oliveria, J. P., Nakayama, T., Lee, I. T., Matter, M. S., Carlisle, A. E., Philips, D., Vazquez, G., Mukherjee, N., Busman-Sahay, K., Nekorchuk, M., Terry, M., Younger, S., Bosse, M., Demeter, J., Rodig, S. J., Tzankov, A., Goltsev, Y., McIlwain, D. R., Angelo, M., Estes, J. D., Nolan, G. P. 2022

  Abstract

  Understanding the mechanisms of HIV tissue persistence necessitates the ability to visualize tissue microenvironments where infected cells reside; however, technological barriers limit our ability to dissect the cellular components of these HIV reservoirs. Here, we developed protein and nucleic acid in situ imaging (PANINI) to simultaneously quantify DNA, RNA, and protein levels within these tissue compartments. By coupling PANINI with multiplexed ion beam imaging (MIBI), we measured over 30 parameters simultaneously across archival lymphoid tissues from healthy or simian immunodeficiency virus (SIV)-infected nonhuman primates. PANINI enabled the spatial dissection of cellular phenotypes, functional markers, and viral events resulting from infection. SIV infection induced IL-10 expression in lymphoid B cells, which correlated with local macrophage M2 polarization. This highlights a potential viral mechanism for conditioning an immunosuppressive tissue environment for virion production. The spatial multimodal framework here can be extended to decipher tissue responses in other infectious diseases and tumor biology.

  View details for DOI 10.1016/j.immuni.2022.03.020

  View details for PubMedID 35447093

• Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification NATURE MACHINE INTELLIGENCE Ghahremani, P., Li, Y., Kaufman, A., Vanguri, R., Greenwald, N., Angelo, M., Hollmann, T. J., Nadeem, S. 2022

  View details for DOI 10.1038/s42256-022-00471-x

  View details for Web of Science ID 000779777200002

• Deep Learning-Inferred Multiplex ImmunoFluorescence for Immunohistochemical Image Quantification. Nature machine intelligence Ghahremani, P., Li, Y., Kaufman, A., Vanguri, R., Greenwald, N., Angelo, M., Hollmann, T. J., Nadeem, S. 2022; 4 (4): 401-412

  Abstract

  Reporting biomarkers assessed by routine immunohistochemical (IHC) staining of tissue is broadly used in diagnostic pathology laboratories for patient care. To date, clinical reporting is predominantly qualitative or semi-quantitative. By creating a multitask deep learning framework referred to as DeepLIIF, we present a single-step solution to stain deconvolution/separation, cell segmentation, and quantitative single-cell IHC scoring. Leveraging a unique de novo dataset of co-registered IHC and multiplex immunofluorescence (mpIF) staining of the same slides, we segment and translate low-cost and prevalent IHC slides to more expensive-yet-informative mpIF images, while simultaneously providing the essential ground truth for the superimposed brightfield IHC channels. Moreover, a new nuclear-envelop stain, LAP2beta, with high (>95%) cell coverage is introduced to improve cell delineation/segmentation and protein expression quantification on IHC slides. By simultaneously translating input IHC images to clean/separated mpIF channels and performing cell segmentation/classification, we show that our model trained on clean IHC Ki67 data can generalize to more noisy and artifact-ridden images as well as other nuclear and non-nuclear markers such as CD3, CD8, BCL2, BCL6, MYC, MUM1, CD10, and TP53. We thoroughly evaluate our method on publicly available benchmark datasets as well as against pathologists' semi-quantitative scoring. The code, the pre-trained models, along with easy-to-run containerized docker files as well as Google CoLab project are available at https://github.com/nadeemlab/deepliif.

  View details for DOI 10.1038/s42256-022-00471-x

  View details for PubMedID 36118303

  View details for PubMedCentralID PMC9477216

• Reproducible, high-dimensional imaging in archival human tissue by multiplexed ion beam imaging by time-of-flight (MIBI-TOF). Laboratory investigation; a journal of technical methods and pathology Liu, C. C., Bosse, M., Kong, A., Kagel, A., Kinders, R., Hewitt, S. M., Varma, S., van de Rijn, M., Nowak, S. H., Bendall, S. C., Angelo, M. 2022

  Abstract

  Multiplexed ion beam imaging by time-of-flight (MIBI-TOF) is a form of mass spectrometry imaging that uses metal labeled antibodies and secondary ion mass spectrometry to image dozens of proteins simultaneously in the same tissue section. Working with the National Cancer Institute's (NCI) Cancer Immune Monitoring and Analysis Centers (CIMAC), we undertook a validation study, assessing concordance across a dozen serial sections of a tissue microarray of 21 samples that were independently processed and imaged by MIBI-TOF or single-plex immunohistochemistry (IHC) over 12 days. Pixel-level features were highly concordant across all 16 targets assessed in both staining intensity (R2=0.94±0.04) and frequency (R2=0.95±0.04). Comparison to digitized, single-plex IHC on adjacent serial sections revealed similar concordance (R2=0.85±0.08) as well. Lastly, automated segmentation and clustering of eight cell populations found that cell frequencies between serial sections yielded an average correlation of R2=0.94±0.05. Taken together, we demonstrate that MIBI-TOF, with well-vetted reagents and automated analysis, can generate consistent and quantitative annotations of clinically relevant cell states in archival human tissue, and more broadly, present a scalable framework for benchmarking multiplexed IHC approaches.

  View details for DOI 10.1038/s41374-022-00778-8

  View details for PubMedID 35351966

• Author Correction: The immunoregulatory landscape of human tuberculosis granulomas. Nature immunology McCaffrey, E. F., Donato, M., Keren, L., Chen, Z., Delmastro, A., Fitzpatrick, M. B., Gupta, S., Greenwald, N. F., Baranski, A., Graf, W., Kumar, R., Bosse, M., Fullaway, C. C., Ramdial, P. K., Forgo, E., Jojic, V., Van Valen, D., Mehra, S., Khader, S. A., Bendall, S. C., van de Rijn, M., Kalman, D., Kaushal, D., Hunter, R. L., Banaei, N., Steyn, A. J., Khatri, P., Angelo, M. 2022

  View details for DOI 10.1038/s41590-022-01178-2

  View details for PubMedID 35277696

• MITI minimum information guidelines for highly multiplexed tissue images. Nature methods Schapiro, D., Yapp, C., Sokolov, A., Reynolds, S. M., Chen, Y., Sudar, D., Xie, Y., Muhlich, J., Arias-Camison, R., Arena, S., Taylor, A. J., Nikolov, M., Tyler, M., Lin, J., Burlingame, E. A., Human Tumor Atlas Network, Chang, Y. H., Farhi, S. L., Thorsson, V., Venkatamohan, N., Drewes, J. L., Pe'er, D., Gutman, D. A., Herrmann, M. D., Gehlenborg, N., Bankhead, P., Roland, J. T., Herndon, J. M., Snyder, M. P., Angelo, M., Nolan, G., Swedlow, J. R., Schultz, N., Merrick, D. T., Mazzili, S. A., Cerami, E., Rodig, S. J., Santagata, S., Sorger, P. K., Abravanel, D. L., Achilefu, S., Ademuyiwa, F. O., Adey, A. C., Aft, R., Ahn, K. J., Alikarami, F., Alon, S., Ashenberg, O., Baker, E., Baker, G. J., Bandyopadhyay, S., Bayguinov, P., Beane, J., Becker, W., Bernt, K., Betts, C. B., Bletz, J., Blosser, T., Boire, A., Boland, G. M., Boyden, E. S., Bucher, E., Bueno, R., Cai, Q., Cambuli, F., Campbell, J., Cao, S., Caravan, W., Chaligne, R., Chan, J. M., Chasnoff, S., Chatterjee, D., Chen, A. A., Chen, C., Chen, C., Chen, B., Chen, F., Chen, S., Chheda, M. G., Chin, K., Cho, H., Chun, J., Cisneros, L., Coffey, R. J., Cohen, O., Colditz, G. A., Cole, K. A., Collins, N., Cotter, D., Coussens, L. M., Coy, S., Creason, A. L., Cui, Y., Zhou, D. C., Curtis, C., Davies, S. R., Bruijn, I., Delorey, T. M., Demir, E., Denardo, D., Diep, D., Ding, L., DiPersio, J., Dubinett, S. M., Eberlein, T. J., Eddy, J. A., Esplin, E. D., Factor, R. E., Fatahalian, K., Feiler, H. S., Fernandez, J., Fields, A., Fields, R. C., Fitzpatrick, J. A., Ford, J. M., Franklin, J., Fulton, B., Gaglia, G., Galdieri, L., Ganesh, K., Gao, J., Gaudio, B. L., Getz, G., Gibbs, D. L., Gillanders, W. E., Goecks, J., Goodwin, D., Gray, J. W., Greenleaf, W., Grimm, L. J., Gu, Q., Guerriero, J. L., Guha, T., Guimaraes, A. R., Gutierrez, B., Hacohen, N., Hanson, C. R., Harris, C. R., Hawkins, W. G., Heiser, C. N., Hoffer, J., Hollmann, T. J., Hsieh, J. J., Huang, J., Hunger, S. P., Hwang, E., Iacobuzio-Donahue, C., Iglesia, M. D., Islam, M., Izar, B., Jacobson, C. A., Janes, S., Jayasinghe, R. G., Jeudi, T., Johnson, B. E., Johnson, B. E., Ju, T., Kadara, H., Karnoub, E., Karpova, A., Khan, A., Kibbe, W., Kim, A. H., King, L. M., Kozlowski, E., Krishnamoorthy, P., Krueger, R., Kundaje, A., Ladabaum, U., Laquindanum, R., Lau, C., Lau, K. S., LeBoeuf, N. R., Lee, H., Lenburg, M., Leshchiner, I., Levy, R., Li, Y., Lian, C. G., Liang, W., Lim, K., Lin, Y., Liu, D., Liu, Q., Liu, R., Lo, J., Lo, P., Longabaugh, W. J., Longacre, T., Luckett, K., Ma, C., Maher, C., Maier, A., Makowski, D., Maley, C., Maliga, Z., Manoj, P., Maris, J. M., Markham, N., Marks, J. R., Martinez, D., Mashl, J., Masilionis, I., Massague, J., Mazurowski, M. A., McKinley, E. T., McMichael, J., Meyerson, M., Mills, G. B., Mitri, Z. I., Moorman, A., Mudd, J., Murphy, G. F., Deen, N. N., Navin, N. E., Nawy, T., Ness, R. M., Nevins, S., Nirmal, A. J., Novikov, E., Oh, S. T., Oldridge, D. A., Owzar, K., Pant, S. M., Park, W., Patti, G. J., Paul, K., Pelletier, R., Persson, D., Petty, C., Pfister, H., Polyak, K., Puram, S. V., Qiu, Q., Villalonga, A. Q., Ramirez, M. A., Rashid, R., Reeb, A. N., Reid, M. E., Remsik, J., Riesterer, J. L., Risom, T., Ritch, C. C., Rolong, A., Rudin, C. M., Ryser, M. D., Sato, K., Sears, C. L., Semenov, Y. R., Shen, J., Shoghi, K. I., Shrubsole, M. J., Shyr, Y., Sibley, A. B., Simmons, A. J., Sinha, A., Sivagnanam, S., Song, S., Southar-Smith, A., Spira, A. E., Cyr, J. S., Stefankiewicz, S., Storrs, E. P., Stover, E. H., Strand, S. H., Straub, C., Street, C., Su, T., Surrey, L. F., Suver, C., Tan, K., Terekhanova, N. V., Ternes, L., Thadi, A., Thomas, G., Tibshirani, R., Umeda, S., Uzun, Y., Vallius, T., Van Allen, E. R., Vandekar, S., Vega, P. N., Veis, D. J., Vennam, S., Verma, A., Vigneau, S., Wagle, N., Wahl, R., Walle, T., Wang, L., Warchol, S., Washington, M. K., Watson, C., Weimer, A. K., Wendl, M. C., West, R. B., White, S., Windon, A. L., Wu, H., Wu, C., Wu, Y., Wyczalkowski, M. A., Xu, J., Yao, L., Yu, W., Zhang, K., Zhu, X. 2022; 19 (3): 262-267

  View details for DOI 10.1038/s41592-022-01415-4

  View details for PubMedID 35277708

• High-Dimensional Tissue Profiling by Multiplexed Ion Beam Imaging. Methods in molecular biology (Clifton, N.J.) Elhanani, O., Keren, L., Angelo, M. 2022; 2386: 147-156

  Abstract

  Multiplexed Ion Beam Imaging by Time of Flight (MIBI-TOF) enables high-dimensional imaging in situ of clinical specimens at single-cell resolution. In MIBI-TOF, tissue sections are stained with dozens of metal-labeled antibodies, whose abundance and location are read by secondary ionization mass spectrometry. The result is a multi-dimensional image, depicting sub-cellular expression and localization for dozens of distinct proteins in situ. Here, we describe the staining and imaging procedures of a MIBI-TOF experiment.

  View details for DOI 10.1007/978-1-0716-1771-7_10

  View details for PubMedID 34766270

• The immunoregulatory landscape of human tuberculosis granulomas. Nature immunology McCaffrey, E. F., Donato, M., Keren, L., Chen, Z., Delmastro, A., Fitzpatrick, M. B., Gupta, S., Greenwald, N. F., Baranski, A., Graf, W., Kumar, R., Bosse, M., Fullaway, C. C., Ramdial, P. K., Forgó, E., Jojic, V., Van Valen, D., Mehra, S., Khader, S. A., Bendall, S. C., van de Rijn, M., Kalman, D., Kaushal, D., Hunter, R. L., Banaei, N., Steyn, A. J., Khatri, P., Angelo, M. 2022

  Abstract

  Tuberculosis (TB) in humans is characterized by formation of immune-rich granulomas in infected tissues, the architecture and composition of which are thought to affect disease outcome. However, our understanding of the spatial relationships that control human granulomas is limited. Here, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) to image 37 proteins in tissues from patients with active TB. We constructed a comprehensive atlas that maps 19 cell subsets across 8 spatial microenvironments. This atlas shows an IFN-γ-depleted microenvironment enriched for TGF-β, regulatory T cells and IDO1+ PD-L1+ myeloid cells. In a further transcriptomic meta-analysis of peripheral blood from patients with TB, immunoregulatory trends mirror those identified by granuloma imaging. Notably, PD-L1 expression is associated with progression to active TB and treatment response. These data indicate that in TB granulomas, there are local spatially coordinated immunoregulatory programs with systemic manifestations that define active TB.

  View details for DOI 10.1038/s41590-021-01121-x

  View details for PubMedID 35058616

• Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma. Cell Risom, T., Glass, D. R., Averbukh, I., Liu, C. C., Baranski, A., Kagel, A., McCaffrey, E. F., Greenwald, N. F., Rivero-Gutiérrez, B., Strand, S. H., Varma, S., Kong, A., Keren, L., Srivastava, S., Zhu, C., Khair, Z., Veis, D. J., Deschryver, K., Vennam, S., Maley, C., Hwang, E. S., Marks, J. R., Bendall, S. C., Colditz, G. A., West, R. B., Angelo, M. 2022; 185 (2): 299-310.e18

  Abstract

  Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand the changes in the tumor microenvironment (TME) accompanying transition to IBC, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to interrogate 79 clinically annotated surgical resections using machine learning tools for cell segmentation, pixel-based clustering, and object morphometrics. Comparison of normal breast with patient-matched DCIS and IBC revealed coordinated transitions between four TME states that were delineated based on the location and function of myoepithelium, fibroblasts, and immune cells. Surprisingly, myoepithelial disruption was more advanced in DCIS patients that did not develop IBC, suggesting this process could be protective against recurrence. Taken together, this HTAN Breast PreCancer Atlas study offers insight into drivers of IBC relapse and emphasizes the importance of the TME in regulating these processes.

  View details for DOI 10.1016/j.cell.2021.12.023

  View details for PubMedID 35063072

• Single-synapse analyses of Alzheimer's disease implicate pathologic tau, DJ1, CD47, and ApoE. Science advances Phongpreecha, T., Gajera, C. R., Liu, C. C., Vijayaragavan, K., Chang, A. L., Becker, M., Fallahzadeh, R., Fernandez, R., Postupna, N., Sherfield, E., Tebaykin, D., Latimer, C., Shively, C. A., Register, T. C., Craft, S., Montine, K. S., Fox, E. J., Poston, K. L., Keene, C. D., Angelo, M., Bendall, S. C., Aghaeepour, N., Montine, T. J. 1800; 7 (51): eabk0473

  Abstract

  [Figure: see text].

  View details for DOI 10.1126/sciadv.abk0473

  View details for PubMedID 34910503

• Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging. Nature methods Hickey, J. W., Neumann, E. K., Radtke, A. J., Camarillo, J. M., Beuschel, R. T., Albanese, A., McDonough, E., Hatler, J., Wiblin, A. E., Fisher, J., Croteau, J., Small, E. C., Sood, A., Caprioli, R. M., Angelo, R. M., Nolan, G. P., Chung, K., Hewitt, S. M., Germain, R. N., Spraggins, J. M., Lundberg, E., Snyder, M. P., Kelleher, N. L., Saka, S. K. 2021

  Abstract

  Tissues and organs are composed of distinct cell types that must operate in concert to perform physiological functions. Efforts to create high-dimensional biomarker catalogs of these cells have been largely based on single-cell sequencing approaches, which lack the spatial context required to understand critical cellular communication and correlated structural organization. To probe in situ biology with sufficient depth, several multiplexed protein imaging methods have been recently developed. Though these technologies differ in strategy and mode of immunolabeling and detection tags, they commonly utilize antibodies directed against protein biomarkers to provide detailed spatial and functional maps of complex tissues. As these promising antibody-based multiplexing approaches become more widely adopted, new frameworks and considerations are critical for training future users, generating molecular tools, validating antibody panels, and harmonizing datasets. In this Perspective, we provide essential resources, key considerations for obtaining robust and reproducible imaging data, and specialized knowledge from domain experts and technology developers.

  View details for DOI 10.1038/s41592-021-01316-y

  View details for PubMedID 34811556

• Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nature biotechnology Greenwald, N. F., Miller, G., Moen, E., Kong, A., Kagel, A., Dougherty, T., Fullaway, C. C., McIntosh, B. J., Leow, K. X., Schwartz, M. S., Pavelchek, C., Cui, S., Camplisson, I., Bar-Tal, O., Singh, J., Fong, M., Chaudhry, G., Abraham, Z., Moseley, J., Warshawsky, S., Soon, E., Greenbaum, S., Risom, T., Hollmann, T., Bendall, S. C., Keren, L., Graf, W., Angelo, M., Van Valen, D. 2021

  Abstract

  A principal challenge in the analysis of tissue imaging data is cell segmentation-the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, a dataset for training segmentation models that contains more than 1million manually labeled cells, an order of magnitude more than all previously published segmentation training datasets. We used TissueNet to train Mesmer, a deep-learning-enabled segmentation algorithm. We demonstrated that Mesmer is more accurate than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We then adapted Mesmer to harness cell lineage information in highly multiplexed datasets and used this enhanced version to quantify cell morphology changes during human gestation. All code, data and models are released as a community resource.

  View details for DOI 10.1038/s41587-021-01094-0

  View details for PubMedID 34795433

• Multiplexed Ion Beam Imaging: Insights into Pathobiology. Annual review of pathology Liu, C. C., McCaffrey, E. F., Greenwald, N. F., Soon, E., Risom, T., Vijayaragavan, K., Oliveria, J., Mrdjen, D., Bosse, M., Tebaykin, D., Bendall, S. C., Angelo, M. 2021

  Abstract

  Next-generation tools for multiplexed imaging have driven a new wave of innovation in understanding how single-cell function and tissue structure are interrelated. In previous work, we developed multiplexed ion beam imaging by time of flight, a highly multiplexed platform that uses secondary ion mass spectrometry to image dozens of antibodies tagged with metal reporters. As instrument throughput has increased, the breadth and depth of imaging data have increased as well. To extract meaningful information from these data, we have developed tools for cell identification, cell classification, and spatial analysis. In this review, we discuss these tools and provide examples of their application in various contexts, including ductal carcinoma in situ, tuberculosis, and Alzheimer's disease. We hope the synergy between multiplexed imaging and automated image analysis will drive a new era in anatomic pathology and personalized medicine wherein quantitative spatial signatures are used routinely for more accurate diagnosis, prognosis, and therapeutic selection. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease, Volume 17 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

  View details for DOI 10.1146/annurev-pathmechdis-030321-091459

  View details for PubMedID 34752710

• Multiplexed imaging reveals an IFN-γ-driven inflammatory state in nivolumab-associated gastritis. Cell reports. Medicine Ferrian, S., Liu, C. C., McCaffrey, E. F., Kumar, R., Nowicki, T. S., Dawson, D. W., Baranski, A., Glaspy, J. A., Ribas, A., Bendall, S. C., Angelo, M. 2021; 2 (10): 100419

  Abstract

  Immune checkpoint blockade using PD-1 inhibition is an effective approach for treating a wide variety of cancer subtypes. While lower gastrointestinal (GI) side effects are more common, upper gastrointestinal adverse events are rarely reported. Here, we present a case of nivolumab-associated autoimmune gastritis. To elucidate the immunology underlying this condition, we leverage multiplexed ion beam imaging by time-of-flight (MIBI-TOF) to identify the presence and proportion of infiltrating immune cells from a single section of biopsy specimen. Using MIBI-TOF, we analyze formalin-fixed, paraffin-embedded human gastric tissue with 28 labels simultaneously. Our analyses reveal a gastritis characterized by severe mucosal injury, interferon gamma (IFN-γ)-producing gastric epithelial cells, and mixed inflammation that includes CD8 and CD4 T cell infiltrates with reduced expression of granzyme B and FOXP3, respectively. Here, we provide a comprehensive multiplexed histopathological mapping of gastric tissue, which identifies IFN-γ-producing epithelial cells as possible contributors to the nivolumab-associated gastritis.

  View details for DOI 10.1016/j.xcrm.2021.100419

  View details for PubMedID 34755133

  View details for PubMedCentralID PMC8561237

• Multiplexed imaging reveals an IFN-gamma-driven inflammatory state in nivolumab-associated gastritis CELL REPORTS MEDICINE Ferrian, S., Liu, C. C., McCaffrey, E. F., Kumar, R., Nowicki, T. S., Dawson, D. W., Baranski, A., Glaspy, J. A., Ribas, A., Bendall, S. C., Angelo, M. 2021; 2 (10)

  View details for DOI 10.1016/j.xcrm.2021.100419

  View details for Web of Science ID 000709853700005

• Multiplexed imaging analysis of the tumor-immune microenvironment reveals predictors of outcome in triple-negative breast cancer. Communications biology Patwa, A., Yamashita, R., Long, J., Risom, T., Angelo, M., Keren, L., Rubin, D. L. 2021; 4 (1): 852

  Abstract

  Triple-negative breast cancer, the poorest-prognosis breast cancer subtype, lacks clinically approved biomarkers for patient risk stratification and treatment management. Prior literature has shown that interrogation of the tumor-immune microenvironment may be a promising approach to fill these gaps. Recently developed high-dimensional tissue imaging technology, such as multiplexed ion beam imaging, provide spatial context to protein expression in the microenvironment, allowing in-depth characterization of cellular processes. We demonstrate that profiling the functional proteins involved in cell-to-cell interactions in the microenvironment can predict recurrence and overall survival. We highlight the immunological relevance of the immunoregulatory proteins PD-1, PD-L1, IDO, and Lag3 by tying interactions involving them to recurrence and survival. Multivariate analysis reveals that our methods provide additional prognostic information compared to clinical variables. In this work, we present a computational pipeline for the examination of the tumor-immune microenvironment using multiplexed ion beam imaging that produces interpretable results, and is generalizable to other cancer types.

  View details for DOI 10.1038/s42003-021-02361-1

  View details for PubMedID 34244605

• Multiplexed Ion Beam Imaging Readout of Single-Cell Immunoblotting. Analytical chemistry Lomeli, G., Bosse, M., Bendall, S. C., Angelo, M., Herr, A. E. 2021

  Abstract

  Improvements in single-cell protein analysis are required to study the cell-to-cell variation inherent to diseases, including cancer. Single-cell immunoblotting (scIB) offers proteoform detection specificity, but often relies on fluorescence-based readout and is therefore limited in multiplexing capability. Among rising multiplexed imaging methods is multiplexed ion beam imaging by time-of-flight (MIBI-TOF), a mass spectrometry imaging technology. MIBI-TOF employs metal-tagged antibodies that do not suffer from spectral overlap to the same degree as fluorophore-tagged antibodies. We report for the first-time MIBI-TOF of single-cell immunoblotting (scIB-MIBI-TOF). The scIB assay subjects single-cell lysate to protein immunoblotting on a microscale device consisting of a 50- to 75-mum thick hydrated polyacrylamide (PA) gel matrix for protein immobilization prior to in-gel immunoprobing. We confirm antibody-protein binding in the PA gel with indirect fluorescence readout of metal-tagged antibodies. Since MIBI-TOF is a layer-by-layer imaging technique, and our protein target is immobilized within a 3D PA gel layer, we characterize the protein distribution throughout the PA gel depth by fluorescence confocal microscopy and confirm that the highest signal-to-noise ratio is achieved by imaging the entirety of the PA gel depth. Accordingly, we report the required MIBI-TOF ion dose strength needed to image varying PA gel depths. Lastly, by imaging 42% of PA gel depth with MIBI-TOF, we detect two isoelectrically separated TurboGFP (tGFP) proteoforms from individual glioblastoma cells, demonstrating that highly multiplexed mass spectrometry-based readout is compatible with scIB.

  View details for DOI 10.1021/acs.analchem.1c01050

  View details for PubMedID 34106685

• MAUI (MBI Analysis User Interface)-An image processing pipeline for Multiplexed Mass Based Imaging. PLoS computational biology Baranski, A., Milo, I., Greenbaum, S., Oliveria, J., Mrdjen, D., Angelo, M., Keren, L. 2021; 17 (4): e1008887

  Abstract

  Mass Based Imaging (MBI) technologies such as Multiplexed Ion Beam Imaging by time of flight (MIBI-TOF) and Imaging Mass Cytometry (IMC) allow for the simultaneous measurement of the expression levels of 40 or more proteins in biological tissue, providing insight into cellular phenotypes and organization in situ. Imaging artifacts, resulting from the sample, assay or instrumentation complicate downstream analyses and require correction by domain experts. Here, we present MBI Analysis User Interface (MAUI), a series of graphical user interfaces that facilitate this data pre-processing, including the removal of channel crosstalk, noise and antibody aggregates. Our software streamlines these steps and accelerates processing by enabling real-time and interactive parameter tuning across multiple images.

  View details for DOI 10.1371/journal.pcbi.1008887

  View details for PubMedID 33872301

• The human tumor atlas network (HTAN) breast pre cancer atlas: A multi-omic integrative analysis of ductal carcinoma in situ (DCIS) and correlation with clinical outcomes Hwang, S., Strand, S. H., Rivero, B., King, L., Risom, T., Harmon, B., Couch, F., Gallagher, K., Kilgore, M., Wei, S., DeMichele, A., King, T., McAuliffe, P., Nangia, J., Storniolo, A., Thompson, A., Gupta, G., Lee, J., Tseng, J., Burns, R., Zhu, C., Matusiak, M., Zhu, S. X., Wang, J., Seoane, J., Tappenden, J., Ding, D., Zhang, D., Luo, J., Vennam, S., Varma, S., Simpson, L., Cisneros, L., Hardman, T., Anderson, L., Straub, C., Srivastava, S., Veis, D. J., Curtis, C., Tibshirani, R., Angelo, R., Hall, A., Owzar, K., Polyak, K., Maley, C., Marks, J., Colditz, G., West, R. B. AMER ASSOC CANCER RESEARCH. 2021

  View details for Web of Science ID 000618737700218

• Mapping the tumor and microenvironmental evolution underlying DCIS progression throughmultiplexed ion beam imaging Angelo, M. AMER ASSOC CANCER RESEARCH. 2021

  View details for Web of Science ID 000618737700217

• Evaluation of Geuenich et al.: Targeting a crucial bottleneck for analyzing single-cell multiplexed imaging data. Cell systems Averbukh, I., Greenwald, N. F., Liu, C. C., Angelo, M. 2021; 12 (12): 1121-1123

  Abstract

  One snapshot of the peer review process for "Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data" (Geuenich et al., 2021).

  View details for DOI 10.1016/j.cels.2021.11.003

  View details for PubMedID 34914901

• Multiplexed ion beam imaging to describe tumor-immune microenvironment and tumor heterogeneity in neuroblastoma. Kammersgaard, M. B., Bosse, M., Martinez, D., Bosse, K. R., Maris, J. M., Mackall, C. L., Angelo, R. M., Davis, K. L. AMER ASSOC CANCER RESEARCH. 2020

  View details for Web of Science ID 000587913100053

• Mapping the tumor and microenvironmental evolution underlying DCIS progression through multiplexed ion beam imaging. Risom, T., Rivero, B., Liu, C., Baranski, A., Strand, S., Greenwald, N., McCaffrey, E., Varma, S., Keren, L., Srivastava, S., Zhu, C., Vennam, S., Hwang, S., Colditz, G., Bendall, S., West, R., Angelo, M. AMER ASSOC CANCER RESEARCH. 2020

  View details for Web of Science ID 000587913100120

• The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution. Cell Rozenblatt-Rosen, O., Regev, A., Oberdoerffer, P., Nawy, T., Hupalowska, A., Rood, J. E., Ashenberg, O., Cerami, E., Coffey, R. J., Demir, E., Ding, L., Esplin, E. D., Ford, J. M., Goecks, J., Ghosh, S., Gray, J. W., Guinney, J., Hanlon, S. E., Hughes, S. K., Hwang, E. S., Iacobuzio-Donahue, C. A., Jane-Valbuena, J., Johnson, B. E., Lau, K. S., Lively, T., Mazzilli, S. A., Pe'er, D., Santagata, S., Shalek, A. K., Schapiro, D., Snyder, M. P., Sorger, P. K., Spira, A. E., Srivastava, S., Tan, K., West, R. B., Williams, E. H., Human Tumor Atlas Network, Aberle, D., Achilefu, S. I., Ademuyiwa, F. O., Adey, A. C., Aft, R. L., Agarwal, R., Aguilar, R. A., Alikarami, F., Allaj, V., Amos, C., Anders, R. A., Angelo, M. R., Anton, K., Ashenberg, O., Aster, J. C., Babur, O., Bahmani, A., Balsubramani, A., Barrett, D., Beane, J., Bender, D. E., Bernt, K., Berry, L., Betts, C. B., Bletz, J., Blise, K., Boire, A., Boland, G., Borowsky, A., Bosse, K., Bott, M., Boyden, E., Brooks, J., Bueno, R., Burlingame, E. A., Cai, Q., Campbell, J., Caravan, W., Cerami, E., Chaib, H., Chan, J. M., Chang, Y. H., Chatterjee, D., Chaudhary, O., Chen, A. A., Chen, B., Chen, C., Chen, C., Chen, F., Chen, Y., Chheda, M. G., Chin, K., Chiu, R., Chu, S., Chuaqui, R., Chun, J., Cisneros, L., Coffey, R. J., Colditz, G. A., Cole, K., Collins, N., Contrepois, K., Coussens, L. M., Creason, A. L., Crichton, D., Curtis, C., Davidsen, T., Davies, S. R., de Bruijn, I., Dellostritto, L., De Marzo, A., Demir, E., DeNardo, D. G., Diep, D., Ding, L., Diskin, S., Doan, X., Drewes, J., Dubinett, S., Dyer, M., Egger, J., Eng, J., Engelhardt, B., Erwin, G., Esplin, E. D., Esserman, L., Felmeister, A., Feiler, H. S., Fields, R. C., Fisher, S., Flaherty, K., Flournoy, J., Ford, J. M., Fortunato, A., Frangieh, A., Frye, J. L., Fulton, R. S., Galipeau, D., Gan, S., Gao, J., Gao, L., Gao, P., Gao, V. R., Geiger, T., George, A., Getz, G., Ghosh, S., Giannakis, M., Gibbs, D. L., Gillanders, W. E., Goecks, J., Goedegebuure, S. P., Gould, A., Gowers, K., Gray, J. W., Greenleaf, W., Gresham, J., Guerriero, J. L., Guha, T. K., Guimaraes, A. R., Guinney, J., Gutman, D., Hacohen, N., Hanlon, S., Hansen, C. R., Harismendy, O., Harris, K. A., Hata, A., Hayashi, A., Heiser, C., Helvie, K., Herndon, J. M., Hirst, G., Hodi, F., Hollmann, T., Horning, A., Hsieh, J. J., Hughes, S., Huh, W. J., Hunger, S., Hwang, S. E., Iacobuzio-Donahue, C. A., Ijaz, H., Izar, B., Jacobson, C. A., Janes, S., Jane-Valbuena, J., Jayasinghe, R. G., Jiang, L., Johnson, B. E., Johnson, B., Ju, T., Kadara, H., Kaestner, K., Kagan, J., Kalinke, L., Keith, R., Khan, A., Kibbe, W., Kim, A. H., Kim, E., Kim, J., Kolodzie, A., Kopytra, M., Kotler, E., Krueger, R., Krysan, K., Kundaje, A., Ladabaum, U., Lake, B. B., Lam, H., Laquindanum, R., Lau, K. S., Laughney, A. M., Lee, H., Lenburg, M., Leonard, C., Leshchiner, I., Levy, R., Li, J., Lian, C. G., Lim, K., Lin, J., Lin, Y., Liu, Q., Liu, R., Lively, T., Longabaugh, W. J., Longacre, T., Ma, C. X., Macedonia, M. C., Madison, T., Maher, C. A., Maitra, A., Makinen, N., Makowski, D., Maley, C., Maliga, Z., Mallo, D., Maris, J., Markham, N., Marks, J., Martinez, D., Mashl, R. J., Masilionais, I., Mason, J., Massague, J., Massion, P., Mattar, M., Mazurchuk, R., Mazutis, L., Mazzilli, S. A., McKinley, E. T., McMichael, J. F., Merrick, D., Meyerson, M., Miessner, J. R., Mills, G. B., Mills, M., Mondal, S. B., Mori, M., Mori, Y., Moses, E., Mosse, Y., Muhlich, J. L., Murphy, G. F., Navin, N. E., Nawy, T., Nederlof, M., Ness, R., Nevins, S., Nikolov, M., Nirmal, A. J., Nolan, G., Novikov, E., Oberdoerffer, P., O'Connell, B., Offin, M., Oh, S. T., Olson, A., Ooms, A., Ossandon, M., Owzar, K., Parmar, S., Patel, T., Patti, G. J., Pe'er, D., Pe'er, I., Peng, T., Persson, D., Petty, M., Pfister, H., Polyak, K., Pourfarhangi, K., Puram, S. V., Qiu, Q., Quintanal-Villalonga, A., Raj, A., Ramirez-Solano, M., Rashid, R., Reeb, A. N., Regev, A., Reid, M., Resnick, A., Reynolds, S. M., Riesterer, J. L., Rodig, S., Roland, J. T., Rosenfield, S., Rotem, A., Roy, S., Rozenblatt-Rosen, O., Rudin, C. M., Ryser, M. D., Santagata, S., Santi-Vicini, M., Sato, K., Schapiro, D., Schrag, D., Schultz, N., Sears, C. L., Sears, R. C., Sen, S., Sen, T., Shalek, A., Sheng, J., Sheng, Q., Shoghi, K. I., Shrubsole, M. J., Shyr, Y., Sibley, A. B., Siex, K., Simmons, A. J., Singer, D. S., Sivagnanam, S., Slyper, M., Snyder, M. P., Sokolov, A., Song, S., Sorger, P. K., Southard-Smith, A., Spira, A., Srivastava, S., Stein, J., Storm, P., Stover, E., Strand, S. H., Su, T., Sudar, D., Sullivan, R., Surrey, L., Suva, M., Tan, K., Terekhanova, N. V., Ternes, L., Thammavong, L., Thibault, G., Thomas, G. V., Thorsson, V., Todres, E., Tran, L., Tyler, M., Uzun, Y., Vachani, A., Van Allen, E., Vandekar, S., Veis, D. J., Vigneau, S., Vossough, A., Waanders, A., Wagle, N., Wang, L., Wendl, M. C., West, R., Williams, E. H., Wu, C., Wu, H., Wu, H., Wyczalkowski, M. A., Xie, Y., Yang, X., Yapp, C., Yu, W., Yuan, Y., Zhang, D., Zhang, K., Zhang, M., Zhang, N., Zhang, Y., Zhao, Y., Zhou, D. C., Zhou, Z., Zhu, H., Zhu, Q., Zhu, X., Zhu, Y., Zhuang, X. 2020; 181 (2): 236–49

  Abstract

  Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

  View details for DOI 10.1016/j.cell.2020.03.053

  View details for PubMedID 32302568

• Mapping cell phenotypes in breast cancer. Nature cancer Keren, L., Angelo, M. 2020; 1 (2): 156-157

  View details for DOI 10.1038/s43018-020-0031-9

  View details for PubMedID 35122012

• The Society for Immunotherapy in Cancer statement on best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) staining and validation. Journal for immunotherapy of cancer Taube, J. M., Akturk, G. n., Angelo, M. n., Engle, E. L., Gnjatic, S. n., Greenbaum, S. n., Greenwald, N. F., Hedvat, C. V., Hollmann, T. J., Juco, J. n., Parra, E. R., Rebelatto, M. C., Rimm, D. L., Rodriguez-Canales, J. n., Schalper, K. A., Stack, E. C., Ferreira, C. S., Korski, K. n., Lako, A. n., Rodig, S. J., Schenck, E. n., Steele, K. E., Surace, M. J., Tetzlaff, M. T., von Loga, K. n., Wistuba, I. I., Bifulco, C. B. 2020; 8 (1)

  Abstract

  The interaction between the immune system and tumor cells is an important feature for the prognosis and treatment of cancer. Multiplex immunohistochemistry (mIHC) and multiplex immunofluorescence (mIF) analyses are emerging technologies that can be used to help quantify immune cell subsets, their functional state, and their spatial arrangement within the tumor microenvironment.The Society for Immunotherapy of Cancer (SITC) convened a task force of pathologists and laboratory leaders from academic centers as well as experts from pharmaceutical and diagnostic companies to develop best practice guidelines for the optimization and validation of mIHC/mIF assays across platforms.Representative outputs and the advantages and disadvantages of mIHC/mIF approaches, such as multiplexed chromogenic IHC, multiplexed immunohistochemical consecutive staining on single slide, mIF (including multispectral approaches), tissue-based mass spectrometry, and digital spatial profiling are discussed.mIHC/mIF technologies are becoming standard tools for biomarker studies and are likely to enter routine clinical practice in the near future. Careful assay optimization and validation will help ensure outputs are robust and comparable across laboratories as well as potentially across mIHC/mIF platforms. Quantitative image analysis of mIHC/mIF output and data management considerations will be addressed in a complementary manuscript from this task force.

  View details for DOI 10.1136/jitc-2019-000155

  View details for PubMedID 32414858

• Single-cell metabolic profiling of human cytotoxic T cells. Nature biotechnology Hartmann, F. J., Mrdjen, D. n., McCaffrey, E. n., Glass, D. R., Greenwald, N. F., Bharadwaj, A. n., Khair, Z. n., Verberk, S. G., Baranski, A. n., Baskar, R. n., Graf, W. n., Van Valen, D. n., Van den Bossche, J. n., Angelo, M. n., Bendall, S. C. 2020

  Abstract

  Cellular metabolism regulates immune cell activation, differentiation and effector functions, but current metabolic approaches lack single-cell resolution and simultaneous characterization of cellular phenotype. In this study, we developed an approach to characterize the metabolic regulome of single cells together with their phenotypic identity. The method, termed single-cell metabolic regulome profiling (scMEP), quantifies proteins that regulate metabolic pathway activity using high-dimensional antibody-based technologies. We employed mass cytometry (cytometry by time of flight, CyTOF) to benchmark scMEP against bulk metabolic assays by reconstructing the metabolic remodeling of in vitro-activated naive and memory CD8+ T cells. We applied the approach to clinical samples and identified tissue-restricted, metabolically repressed cytotoxic T cells in human colorectal carcinoma. Combining our method with multiplexed ion beam imaging by time of flight (MIBI-TOF), we uncovered the spatial organization of metabolic programs in human tissues, which indicated exclusion of metabolically repressed immune cells from the tumor-immune boundary. Overall, our approach enables robust approximation of metabolic and functional states in individual cells.

  View details for DOI 10.1038/s41587-020-0651-8

  View details for PubMedID 32868913

• Mass spectroscopy-based highly multiplexed super-resolution imaging method for fine details of tumor microenvironment monitoring and tumor-immune cell interactions Bai, Y., Zhu, B., Angelo, M., Zhao, Y., Jiang, S., Clave, X., Nolan, G. BMC. 2019

  View details for Web of Science ID 000496473200452

• Combining Multiplexed Ion Beam Imaging (MIBI) with Convolutional Neural Networks to accurately segment cells in human tissue Greenwald, N., Keren, L., Greenbaum, S., Fong, M., Chaudhry, G., Abraham, Z., Moseley, J., Van Valen, D., Angelo, M. BMC. 2019

  View details for Web of Science ID 000496473200232

• Gastric toxicity associated with PD-1 blockade therapy revealed by Multiplexed Ion Beam Imaging Ferrian, S., Nowicki, T., Dawson, D., Baranski, A., Glaspy, J., Ribas, A., Angelo, M. BMC. 2019

  View details for Web of Science ID 000496473200408

• Multiplexed Imaging for the simultaneous detection of nucleic acids and proteins to dissect the tissue immune landscape and microenvironment of viral diseases Jiang, S., Clave, X., Chan, C., Zhu, B., Bai, Y., Bosse, M., McIlwain, D., Bendall, S., Angelo, M., Estes, J., Nolan, G. BMC. 2019

  View details for Web of Science ID 000496473200465

• Mapping the spatial architecture of acute myeloid leukemia in the bone marrow microenvironment by multiplexed ion beam imaging Rovira-Clave, X., Jiang, S., Bai, Y., Zhu, B., Bosse, M., Angelo, M., Banz, Y., Schurch, C., Nolan, G. BMC. 2019

  View details for Web of Science ID 000496473200424

• MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure. Science advances Keren, L., Bosse, M., Thompson, S., Risom, T., Vijayaragavan, K., McCaffrey, E., Marquez, D., Angoshtari, R., Greenwald, N. F., Fienberg, H., Wang, J., Kambham, N., Kirkwood, D., Nolan, G., Montine, T. J., Galli, S. J., West, R., Bendall, S. C., Angelo, M. 2019; 5 (10): eaax5851

  Abstract

  Understanding tissue structure and function requires tools that quantify the expression of multiple proteins while preserving spatial information. Here, we describe MIBI-TOF (multiplexed ion beam imaging by time of flight), an instrument that uses bright ion sources and orthogonal time-of-flight mass spectrometry to image metal-tagged antibodies at subcellular resolution in clinical tissue sections. We demonstrate quantitative, full periodic table coverage across a five-log dynamic range, imaging 36 labeled antibodies simultaneously with histochemical stains and endogenous elements. We image fields of view up to 800 mum * 800 mum at resolutions down to 260 nm with sensitivities approaching single-molecule detection. We leverage these properties to interrogate intrapatient heterogeneity in tumor organization in triple-negative breast cancer, revealing regional variability in tumor cell phenotypes in contrast to a structured immune response. Given its versatility and sample back-compatibility, MIBI-TOF is positioned to leverage existing annotated, archival tissue cohorts to explore emerging questions in cancer, immunology, and neurobiology.

  View details for DOI 10.1126/sciadv.aax5851

  View details for PubMedID 31633026

• Glucose Metabolism Drives Histone Acetylation Landscape Transitions that Dictate Muscle Stem Cell Function. Cell reports Yucel, N., Wang, Y. X., Mai, T., Porpiglia, E., Lund, P. J., Markov, G., Garcia, B. A., Bendall, S. C., Angelo, M., Blau, H. M. 2019; 27 (13): 3939

  Abstract

  The impact of glucose metabolism on muscle regeneration remains unresolved. We identify glucose metabolism as a crucial driver of histone acetylation and myogenic cell fate. We use single-cell mass cytometry (CyTOF) and flow cytometry to characterize the histone acetylation and metabolic states of quiescent, activated, and differentiating muscle stem cells (MuSCs). We find glucose is dispensable for mitochondrial respiration in proliferating MuSCs, so that glucose becomes available for maintaining high histone acetylation via acetyl-CoA. Conversely, quiescent and differentiating MuSCs increase glucose utilization for respiration and have consequently reduced acetylation. Pyruvate dehydrogenase (PDH) activity serves as a rheostat for histone acetylation and must be controlled for muscle regeneration. Increased PDH activity in proliferation increases histone acetylation and chromatin accessibility at genes that must be silenced for differentiation to proceed, and thus promotes self-renewal. These results highlight metabolism as a determinant of MuSC histone acetylation, fate, and function during muscle regeneration.

  View details for DOI 10.1016/j.celrep.2019.05.092

  View details for PubMedID 31242425

• Mass synaptometry: High-dimensional multi parametric assay for single synapses JOURNAL OF NEUROSCIENCE METHODS Gajera, C. R., Fernandez, R., Postupna, N., Montine, K. S., Fox, E. J., Tebaykin, D., Angelo, M., Bendall, S. C., Keene, C., Montine, T. J. 2019; 312: 73–83

  View details for DOI 10.1016/j.jneumeth.2018.11.008

  View details for Web of Science ID 000456220900009

• Mass synaptometry: High-dimensional multi parametric assay for single synapses. Journal of neuroscience methods Gajera, C. R., Fernandez, R., Postupna, N., Montine, K. S., Fox, E. J., Tebaykin, D., Angelo, M., Bendall, S. C., Keene, C. D., Montine, T. J. 2018

  Abstract

  BACKGROUND: Synaptic alterations, especially presynaptic changes, are cardinal features of neurodegenerative diseases and strongly correlate with cognitive decline.NEW METHOD: We report "Mass Synaptometry" for the high-dimensional analysis of individual human synaptosomes, enriched nerve terminals from brain. This method was adapted from cytometry by time-of-flight mass spectrometry (CyTOF), which is commonly used for single-cell analysis of immune and blood cells.RESULT: Here we overcome challenges for single synapse analysis by optimizing synaptosome preparations, generating a 'SynTOF panel,' recalibrating acquisition settings, and applying computational analyses. Through the analysis of 390,000 individual synaptosomes, we also provide proof-of principle validation by characterizing changes in synaptic diversity in Lewy Body Disease (LBD), Alzheimer's disease and normal brain.COMPARISON WITH EXISTING METHOD(S): Current imaging methods to study synapses in humans are capable of analyzing a limited number of synapses, and conventional flow cytometric techniques are typically restricted to fewer than 6 parameters. Our method allows for the simultaneous detection of 34 parameters from tens of thousands of individual synapses.CONCLUSION: We applied Mass Synaptometry to analyze 34 parameters simultaneously on more than 390,000 synaptosomes from 13 human brain samples. This new approach revealed regional and disease-specific changes in synaptic phenotypes, including validation of this method with the expected changes in the molecular composition of striatal dopaminergic synapses in Lewy body disease and Alzheimer's disease. Mass synaptometry enables highly parallel molecular profiling of individual synaptic terminals.

  View details for PubMedID 30465796

• A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging. Cell Keren, L., Bosse, M., Marquez, D., Angoshtari, R., Jain, S., Varma, S., Yang, S., Kurian, A., Van Valen, D., West, R., Bendall, S. C., Angelo, M. 2018; 174 (6): 1373

  Abstract

  The immune system is critical in modulating cancer progression, but knowledge of immune composition, phenotype, and interactions with tumor is limited. We used multiplexed ion beam imaging by time-of-flight (MIBI-TOF) to simultaneously quantify in situ expression of 36 proteins covering identity, function, and immune regulation at sub-cellular resolution in 41 triple-negative breast cancer patients. Multi-step processing, including deep-learning-based segmentation, revealed variability in the composition of tumor-immune populations across individuals, reconciled by overall immune infiltration and enriched co-occurrence of immune subpopulations and checkpoint expression. Spatial enrichment analysis showed immune mixed and compartmentalized tumors, coinciding with expression of PD1, PD-L1, and IDO in a cell-type- and location-specific manner. Ordered immune structures along the tumor-immune border were associated with compartmentalization and linked to survival. These data demonstrate organization in the tumor-immune microenvironment that is structured in cellular composition, spatial arrangement, and regulatory-protein expression and provide a framework to apply multiplexed imaging to immune oncology.

  View details for PubMedID 30193111

• A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging CELL Keren, L., Bosse, M., Marquez, D., Angoshtari, R., Jain, S., Varma, S., Yang, S., Kurian, A., Van Valen, D., West, R., Bendall, S. C., Angelo, M. 2018; 174 (6): 1373-+

  View details for DOI 10.1016/j.cell.2018.08.039

  View details for Web of Science ID 000443841000009

• Immunohistochemistry and mass spectrometry for highly multiplexed cellular molecular imaging LABORATORY INVESTIGATION Levenson, R. M., Borowsky, A. D., Angelo, M. 2015; 95 (4): 397-405

  Abstract

  The role of immunohistochemistry (IHC) in the management of cancer has expanded to provide improved diagnostic classification, as well as guidance on disease prognosis, therapy, and relapse. These new tasks require evaluation of an increasing number of protein targets; however, conventional multiplexing, usually achieved using serial tissue sections stained for a single analyte per slide, can exhaust small biopsy specimens, complicate slide-to-slide protein expression correlation, and leave insufficient material for additional molecular assays. A new approach, mass spectrometry immunohistochemistry (MSIHC), compatible with high levels of target multiplexing and suitable for use on formalin-fixed, paraffin-embedded samples can circumvent many of these issues. The strategy employs antibodies that are labeled with elemental mass tags, such as isotopically pure lanthanides not typically found in biological specimens, rather than with typical fluorophores or chromogens. The metal-labeled antibodies are then detected in tissue using lasers or ion beams to liberate the tags for subsequent mass spectrometry detection. Within a given multiplexed IHC panel, the metal labels are selected so that their respective masses do not overlap. More than 30 antibodies have been imaged simultaneously, and up to 100 antibodies could potentially be detected at once if the full available mass spectrum is deployed. MSIHC has a number of advantages over conventional IHC techniques. Background due to autofluorescence is absent and the dynamic range is 10(5), exceeding immunofluorescence and chromogenic IHC by 100-fold and 1000-fold, respectively. Detection of labeled primary antibodies improves assay linearity over both chromogenic and fluorescent IHC. Multiplexed mass-tagged antibodies incubated simultaneously with tissue do not appear to cross-interfere, and because the mass tags do not degrade, samples are stable indefinitely. The imaging resolution of multiplexed ion-beam imaging can be better than light microscopy. With appropriate instrumentation, MSIHC has the potential to transform research and clinical pathology practice.

  View details for DOI 10.1038/labinvest.2015.2

  View details for Web of Science ID 000352208600004

  View details for PubMedID 25730370

• Multiplexed ion beam imaging of human breast tumors. Nature medicine Angelo, M., Bendall, S. C., Finck, R., Hale, M. B., Hitzman, C., Borowsky, A. D., Levenson, R. M., Lowe, J. B., Liu, S. D., Zhao, S., Natkunam, Y., Nolan, G. P. 2014; 20 (4): 436-442

  Abstract

  Immunohistochemistry (IHC) is a tool for visualizing protein expression that is employed as part of the diagnostic workup for the majority of solid tissue malignancies. Existing IHC methods use antibodies tagged with fluorophores or enzyme reporters that generate colored pigments. Because these reporters exhibit spectral and spatial overlap when used simultaneously, multiplexed IHC is not routinely used in clinical settings. We have developed a method that uses secondary ion mass spectrometry to image antibodies tagged with isotopically pure elemental metal reporters. Multiplexed ion beam imaging (MIBI) is capable of analyzing up to 100 targets simultaneously over a five-log dynamic range. Here, we used MIBI to analyze formalin-fixed, paraffin-embedded human breast tumor tissue sections stained with ten labels simultaneously. The resulting data suggest that MIBI can provide new insights into disease pathogenesis that will be valuable for basic research, drug discovery and clinical diagnostics.

  View details for DOI 10.1038/nm.3488

  View details for PubMedID 24584119

• Multiplexed ion beam imaging of human breast tumors. Nature medicine Angelo, M., Bendall, S. C., Finck, R., Hale, M. B., Hitzman, C., Borowsky, A. D., Levenson, R. M., Lowe, J. B., Liu, S. D., Zhao, S., Natkunam, Y., Nolan, G. P. 2014; 20 (4): 436-442

  Abstract

  Immunohistochemistry (IHC) is a tool for visualizing protein expression that is employed as part of the diagnostic workup for the majority of solid tissue malignancies. Existing IHC methods use antibodies tagged with fluorophores or enzyme reporters that generate colored pigments. Because these reporters exhibit spectral and spatial overlap when used simultaneously, multiplexed IHC is not routinely used in clinical settings. We have developed a method that uses secondary ion mass spectrometry to image antibodies tagged with isotopically pure elemental metal reporters. Multiplexed ion beam imaging (MIBI) is capable of analyzing up to 100 targets simultaneously over a five-log dynamic range. Here, we used MIBI to analyze formalin-fixed, paraffin-embedded human breast tumor tissue sections stained with ten labels simultaneously. The resulting data suggest that MIBI can provide new insights into disease pathogenesis that will be valuable for basic research, drug discovery and clinical diagnostics.

  View details for DOI 10.1038/nm.3488

  View details for PubMedID 24584119