AI-Assisted Microscopy for Infection Biology: Advances in High-Content Imaging of Host-Pathogen Interactions

Downloads

Authors

  • Juan Alfonso Redondo Institute of Fundamental Technological Research Polish Academy of Sciences, Warsaw, Poland
  • Pawel Paszek Institute of Fundamental Technological Research, Polish Academy of Sciences, Warsaw, Poland / The Lydia Becker Institute of Immunology and Inflammation, University of Manchester, Manchester, UK / Division of Immunology, Immunity to Infection and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK, United Kingdom

Abstract

Advances in high-content microscopy and artificial intelligence (AI) are transforming the quantitative study of infection biology. Automated imaging platforms now enable rapid, large-scale acquisition of host-pathogen interactions across thousands of cells and multiple experimental conditions. When combined with AI-based segmentation, these workflows extract infection-relevant features such as pathogen load, intracellular localization, and host response markers at single-cell resolution. Deep-learning models have proven especially powerful, outperforming classical threshold-based methods under different imaging conditions, reducing reliance on manual annotation, and detecting rare infection outcomes. Beyond robust image analysis, these approaches generate scalable and reproducible datasets that can be integrated with computational modelling and systems biology, providing predictive insight into infection dynamics. This review highlights recent progress in AI-assisted microscopy for bacterial infection and outlines future directions toward multimodal integration, clinical translation, and open-source tool development.

Keywords:

artificial intelligence, machine learning, deep learning, host-pathogen interactions, single-cell biology, cell-to-cell variability, cellular heterogeneity, infection biology

References



  1. S. Al-Ani, H. Guo, S. Fyfe, Z. Long, S. Donnaz, Y. Kim, Effect of training sample size, image resolution and epochs on filamentous and floc-forming bacteria classification using machine learning, Journal of Environmental Management, 379: 124803, 2025, https://doi.org/10.1016/j.jenvman.2025.124803.


  2. J. Augenstreich, A. Poddar, A.T. Belew, N.M. El-Sayed, V. Briken, da_Tracker: Automated workflow for high throughput single cell and single phagosome tracking in infected cells, Biology Open, 13(9): bio060555, 2024, https://doi.org/10.1242/bio.060555.


  3. G. Avital et al., The tempo and mode of gene regulatory programs during bacterial infection, Cell Reports, 41(2): 111477, 2022, https://doi.org/10.1016/j.celrep.2022.111477.


  4. G. Batani et al., Development of a visual Adhesion/Invasion Inhibition Assay to assess the functionality of Shigella-specific antibodies, Frontiers in Immunology, 15: 1374293, 2024, https://doi.org/10.3389/fimmu.2024.1374293.


  5. A. Bilodeau et al., Development of AI-assisted microscopy frameworks through realistic simulation with pySTED, Nature Machine Intelligence, 6(10): 1197–1215, 2024, https://doi.org/10.1038/s42256-024-00903-w.


  6. N. Bossel Ben-Moshe et al., Predicting bacterial infection outcomes using single cell RNA-sequencing analysis of human immune cells, Nature Communications, 10(1): 3266, 2019, https://doi.org/10.1038/s41467-019-11257-y.


  7. D. Bumann, Heterogeneous host-pathogen encounters: Act locally, think globally, Cell Host & Microbe, 17(1): 13–19, 2015, https://doi.org/10.1016/j.chom.2014.12.006.


  8. J.C. Caicedo et al., Data-analysis strategies for image-based cell profiling, Nature Methods, 14(9): 849–863, 2017, https://doi.org/10.1038/nmeth.4397.


  9. J. Cao et al., Joint profiling of chromatin accessibility and gene expression in thousands of single cells, Science, 361(6409): 1380–1385, 2018, https://doi.org/10.1126/science.aau0730.


  10. E.L. Choi et al., Protocol for AI-supported immunofluorescence colocalization analysis in human enteric neurons, STAR Protocols, 6(2): 103828, 2025, https://doi.org/10.1016/j.xpro.2025.103828.


  11. E.S. Chung, P. Kar, M. Kamkaew, A. Amir, B.B. Aldridge, Single-cell imaging of the Mycobacterium tuberculosis cell cycle reveals linear and heterogenous growth, Nat Microbiol, 9(12): 3332–3344, 2024, https://doi.org/10.1038/s41564-024-01846-z.


  12. T.J. Collins, ImageJ for microscopy, Biotechniques, 43(Sup 1): S25–S30, 2007, https://doi.org/10.2144/000112517.


  13. D.A. Cusanovich et al., Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing, Science, 348(6237): 910–914, 2015, https://doi.org/10.1126/science.aab1601.


  14. K.J. Cutler et al., Omnipose: A high-precision morphology-independent solution for bacterial cell segmentation, Nature Methods, 19(11): 1438–1448, 2022, https://doi.org/10.1038/s41592-022-01639-4.


  15. I. Dadole, D. Blaha, N. Personnic, The macrophage-bacterium mismatch in persister formation, Trends in Microbiology, 32(10): 944–956, 2024, https://doi.org/10.1016/j.tim.2024.02.009.


  16. C.L. Eng et al., Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+, Nature, 568(7751): 235–239, 2019, https://doi.org/10.1038/s41586-019-1049-y.


  17. M.J. Fanous, C.M. Seybold, H. Chen, N. Pillar, A. Ozcan, BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images, npj Digital Medicine, 8(1): 506, 2025, https://doi.org/10.1038/s41746-025-01882-x.


  18. L. Feltham et al., Bacterial aggregation facilitates internalin-mediated invasion of Listeria monocytogenes, Frontiers in Cellular and Infection Microbiology, 14, 2024, https://doi.org/10.3389/fcimb.2024.1411124.


  19. D. Fisch et al., Defining host–pathogen interactions employing an artificial intelligence workflow, eLife, 8: e40560, 2019, https://doi.org/10.7554/eLife.40560.


  20. D. Fisch et al., HRMAn 2.0: Next-generation artificial intelligence–driven analysis for broad host–pathogen interactions, Cellular Microbiology, 23(7): e13349, 2021, https://doi.org/10.1111/cmi.13349.


  21. F. Grabowski, M. Kochańczyk, Z. Korwek, M. Czerkies, W. Prus, T. Lipniacki, Antagonism between viral infection and innate immunity at the single-cell level, PLoS Pathogens, 19(9): e1011597, 2023, https://doi.org/10.1371/journal.ppat.1011597.


  22. S. Helaine, A.M. Cheverton, K.G. Watson, L.M. Faure, S.A. Matthews, D.W. Holden, Internalization of Salmonella by macrophages induces formation of nonreplicating persisters, Science, 343(6167): 204–208, 2014, https://doi.org/10.1126/science.1244705.


  23. M. Held et al., CellCognition: Time-resolved phenotype annotation in high-throughput live cell imaging, Nature Methods, 7(9): 747–754, 2010, https://doi.org/10.1038/nmeth.1486.


  24. L.M. Howell, T.P. Newsome, High-throughput Single-cell analysis of vaccinia virus infection, [In:] Z. Yang, P.S. Satheshkumar (Eds.), Vaccinia, Mpox, and Other Poxviruses, Methods in Molecular Biology, Vol. 2860, pp. 229–240, Humana, New York, NY, 2025, https://doi.org/10.1007/978-1-0716-4160-6_15.


  25. A.P. Jones et al., Spatial mapping of immune cell environments in NF2-related schwannomatosis vestibular schwannoma, Nature Communications, 16(1): 2944, 2025, https://doi.org/10.1038/s41467-025-57586-z.


  26. M. Kortebi et al., Listeria monocytogenes switches from dissemination to persistence by adopting a vacuolar lifestyle in epithelial cells, PLoS Pathogens, 13(11): e1006734, 2017, https://doi.org/10.1371/journal.ppat.1006734.


  27. Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature, 521: 436–444, 2015, https://doi.org/10.1038/nature14539.


  28. A.T. López-Jiménez, D. Brokatzky, K. Pillay, T. Williams, G. Özbaykal Güler, S. Mostowy, High-content high-resolution microscopy and deep learning-assisted analysis reveals host and bacterial heterogeneity during Shigella infection, eLife, 13: RP97495, 2025, https://doi.org/10.7554/eLife.97495.


  29. C. McQuin et al., CellProfiler 3.0: Next-generation image processing for biology, PLoS Biology, 16(7): e2005970, 2018, https://doi.org/10.1371/journal.pbio.2005970.


  30. L.A. Meirelles, A. Persat, Decoding host-microbe interactions with engineered human organoids, The EMBO Journal, 44(6): 1569–1573, 2025, https://doi.org/10.1038/s44318-025-00387-3.


  31. E. Moen, D. Bannon, T. Kudo, W. Graf, M. Covert, D. Van Valen, Deep learning for cellular image analysis, Nature Methods, 16(12): 1233–1246, 2019, https://doi.org/10.1038/s41592-019-0403-1.


  32. J. Moran et al., Live-cell imaging reveals single-cell and population-level infection strategies of Listeria monocytogenes in macrophages, Frontiers in Immunology, 14, 2023, https://doi.org/10.3389/fimmu.2023.1235675.


  33. N. Otsu, A threshold selection method from gray-level histograms, IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62–66, 1979, https://doi.org/10.1109/TSMC.1979.4310076.


  34. B. Park, T. Shin, R. Kang, A. Fong, B. McDonogh, S.-C. Yoon, Automated segmentation of foodborne bacteria from chicken rinse with hyperspectral microscope imaging and deep learning methods, Computers and Electronics in Agriculture, 208: 107802, 2023, https://doi.org/10.1016/j.compag.2023.107802.


  35. S. Ragi, M.H. Rahman, J. Duckworth, K. Jawaharraj, P. Chundi, V. Gadhamshetty, Artificial Intelligence-driven image analysis of bacterial cells and biofilms, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 20(1): 174–184, 2023, https://doi.org/10.1109/TCBB.2021.3138304.


  36. Y. Rivenson, T. Liu, Z. Wei, Y. Zhang, K. de Haan, A. Ozcan, PhaseStain: The digital staining of label-free quantitative phase microscopy images using deep learning, Light: Science & Applications, 8: 23, 2019, https://doi.org/10.1038/s41377-019-0129-y.


  37. J. Sauvola, M. Pietikäinen, Adaptive document image binarization, Pattern Recognition, 33(2): 225–236, 2000, https://doi.org/10.1016/S0031-3203(99)00055-2.


  38. S. Sen, I. Vairagare, J. Gosai, A. Shrivastava, RABiTPy: An open-source Python software for rapid, AI-powered bacterial tracking and analysis, BMC Bioinformatics, 26(1): 127, 2025, https://doi.org/10.1186/s12859-025-06145-w.


  39. C. Spahn et al., DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches, Communications Biology, 5: 688, 2022, https://doi.org/10.1038/s42003-022-03634-z.


  40. D.G. Spiller, C.D. Wood, D.A. Rand, M.R. White, Measurement of single-cell dynamics, Nature, 465: 736–745, 2010, https://doi.org/10.1038/nature09232.


  41. D.A.C. Stapels et al., Salmonella persisters undermine host immune defenses during antibiotic treatment, Science, 362(6419): 1156–1160, 2018, https://doi.org/10.1126/science.aat7148.


  42. C. Tao et al., A deep-learning based system for rapid genus identification of pathogens under hyperspectral microscopic images, Cells, 11(14): 2237, 2022, https://doi.org/10.3390/cells11142237.


  43. N. Tsanov et al., smiFISH and FISH-quant – A flexible single RNA detection approach with super-resolution capability, Nucleic Acids Research, 44(22): e165, 2016, https://doi.org/10.1093/nar/gkw784.


  44. L. von Chamier, R.F. Laine, R. Henriques, Artificial intelligence for microscopy: What you should know, Biochemical Society Transactions, 47(4): 1029–1040, 2019, https://doi.org/10.1042/bst20180391.


  45. J. Voznica, C. Gardella, I. Belotserkovsky, A. Dufour, J. Enninga, V. Stévenin, Identification of parameters of host cell vulnerability during Salmonella infection by quantitative image analysis and modeling, Infection and Immunity, 86(1), 2018, https://doi.org/10.1128/iai.00644-17.


  46. C. Xia, J. Fan, G. Emanuel, J. Hao, X. Zhuang, Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression, Proceedings of the National Academy of Sciences of the United States of America, 116(39): 19490–19499, 2019, https://doi.org/10.1073/pnas.1912459116.


  47. F. Zhang et al., Revolutionizing diagnosis of pulmonary Mycobacterium tuberculosis based on CT: A systematic review of imaging analysis through deep learning, Frontiers in Microbiology, 15: 1510026, 2025, https://doi.org/10.3389/fmicb.2024.1510026.


  48. F. Zhang et al., The impact of maximum cross-sectional area of lesion on predicting the early therapeutic response of multidrug-resistant tuberculosis, Journal of Infection and Public Health, 18(2): 102628, 2025, https://doi.org/10.1016/j.jiph.2024.102628.