- [Resource Hub](/) - [Publication](/?resource-type=publication#library) - Multiomic analysis identifies suppressive myeloid cell populations in human TB granulomas # Multiomic analysis identifies suppressive myeloid cell populations in human TB granulomas Jain N, Ogbonna EC, Maliga Z, Jacobson C, Zhang L, Shih A, et al. bioRxiv . 2025. DOI [10.1101/2025.03.10.642376](https://doi.org/10.1101/2025.03.10.642376). PMID 40161687. PMCID PMC11952478. How to cite ### AMA Jain N, Ogbonna EC, Maliga Z, Jacobson C, Zhang L, Shih A, et al. Multiomic analysis identifies suppressive myeloid cell populations in human TB granulomas. bioRxiv . 2025. doi:10.1101/2025.03.10.642376 ### APA Jain, N., Ogbonna, E. C., Maliga, Z., Jacobson, C., Zhang, L., Shih, A., et al. (2025). Multiomic analysis identifies suppressive myeloid cell populations in human TB granulomas. bioRxiv . https://doi.org/10.1101/2025.03.10.642376 ### BibTeX @article{jain2025multiomic, title = {Multiomic analysis identifies suppressive myeloid cell populations in human TB granulomas}, author = {Jain, Neharika and Ogbonna, Emmanuel C. and Maliga, Zoltan and Jacobson, Connor and Zhang, Liang and Shih, Angela and others}, journal = {bioRxiv}, year = {2025}, doi = {10.1101/2025.03.10.642376}, pmid = {40161687} } Tuberculosis is still one of the world's deadliest infections, and drug-resistant strains make it harder to treat. One proposed add-on therapy targets immune-suppressing cells called MDSCs, which are well studied in cancer. Whether they even gather inside human TB granulomas — the walled-off lung lesions where the bacteria hide — was unknown. The team profiled eighty-four granulomas from three people with active TB, pairing region-level gene readouts with single-cell protein imaging of intact lung tissue. Immune suppression turned out to come not from classical MDSCs but mainly from a kind of dendritic cell that makes the enzyme IDO1. These cells gathered in the more cellular granulomas and sat close to activated regulatory T cells. That points to IDO1-positive dendritic cells, rather than MDSCs, as the suppressive population worth targeting in tuberculosis. [Read publication at bioRxiv](https://pmc.ncbi.nlm.nih.gov/articles/PMC11952478/) ## Key findings - In TB granulomas, immune suppression traced to IDO1+ dendritic cells, not the classical MDSCs known from cancer. Across 84 granulomas from three individuals with active TB, IDO1+ dendritic cells were the most abundant suppressive myeloid population at 2.5% of all cells, while classical monocytic MDSCs made up just 0.2%. - Whole-slide cyclic immunofluorescence phenotyped more than 1.9 million single cells in intact lung tissue. Imaging on the CyteFinder resolved lineage and functional markers — including IDO1, PD-L1, FOXP3 and ICOS — across all 84 granulomas while every cell kept its position on the slide. - Seven recurrent granuloma microenvironments emerged, and the suppressive one placed IDO1+ dendritic cells beside activated regulatory T cells. A nearest-neighbor analysis defined seven microenvironments (GME1–GME7); in the immunosuppressive one, IDO1+ dendritic cells were the most prevalent cell type at 28%, and activated (ICOS+) regulatory T cells sat closer to them than their resting counterparts. ## CyteFinder in the methods “Images were acquired on a CyteFinder slide scanning fluorescence microscope (RareCyte Inc.), using a 20x/0.75 NA objective – with a 2×2 binning.” — Jain et al., bioRxiv (2025), Methods, “t-CyCIF Protocol and Image Acquisition” ## Why it matters for CyteFinder users If you are weighing the CyteFinder for a tissue study, look at the specific job it did here. This was a multiomic study: a region-level transcriptomics arm read which genes were active across granuloma zones, but that measurement cannot say which individual cells carry a protein or where they sit. That is the question the CyteFinder answered. The authors ran cyclic immunofluorescence on intact FFPE lung sections, staining lineage and functional markers — among them CD11b, FOXP3, IDO1, PD-L1 and ICOS — across successive rounds, then scanned each cycle on the instrument and registered them into one whole-slide image. From that image they assigned phenotypes to more than 1.9 million cells across 84 granulomas, singled out IDO1+ dendritic cells as the dominant suppressive population, and measured how closely those cells sat to activated regulatory T cells. For your own work the point is concrete: when a question turns on which cells express a marker and who their neighbors are in tissue you cannot dissociate, whole-slide cyclic imaging is the measurement that answers it, at single-cell resolution across a million-plus cells. 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