Investigating alveolar macrophages in an human ex vivo precision-cut lung slice model of SARS-CoV-2 infection using Raman spectroscopy—A case study

Max Naumann , Franziska Hornung , Simone Eiserloh , Astrid Tannert , Antje Häder , Rustam R. Guliev , Tim Sandhaus , Stefanie Deinhardt-Emmer , Ute Neugebauer

Clinical and Translational Medicine ›› 2025, Vol. 15 ›› Issue (9) : e70453

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Clinical and Translational Medicine ›› 2025, Vol. 15 ›› Issue (9) : e70453 DOI: 10.1002/ctm2.70453
RESEARCH ARTICLE

Investigating alveolar macrophages in an human ex vivo precision-cut lung slice model of SARS-CoV-2 infection using Raman spectroscopy—A case study

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Abstract

Background: Alveolar macrophages (AMs) are crucial innate immune cells that play important roles during infection with severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). Ex vivo human precision-cut lung slices (PCLSs) are well-suited models to study immune reactions and biochemical changes within host cells as well as to follow functional macrophage phenotype plasticity within complex tissue environment. Raman spectroscopy emerged in recent years as a powerful method for label-free cell characterization.

Methods: Human PCLSs from one donor were infected with either the SARS-CoV-2 delta or omicron variant. Immunofluorescence microscopy localized AMs and virus particles. Cytokine levels of interferon-gamma (IFN-γ) and interleukin 18 (IL-18) were quantified. The lung slice model was optimized for label-free Raman spectroscopic imaging and for the characterization of single AMs within the three-dimensional structure of the PCLS model.

Results: Fluorescence microscopy confirmed the location of AMs and virus particles within the PCLS model. Raman spectroscopic imaging generated false-colour images, revealing distinct spectroscopic differences between AMs in the uninfected control PCLS model and those in PCLS models infected with SARS-CoV-2. These differences included variations in intracellular RNA, carotenoid, triacyl glyceride, and glucose levels, consistent in interpretation with cytokine quantification data. A linear discriminant analysis (LDA) classification model achieved an 83% accuracy in distinguishing cells from infected lung slices from those of the uninfected controls. The LDA loadings pointed to spectral bands that had been previously identified in an in vitro stimulation study of macrophages.

Conclusions: Raman spectroscopy can characterize the cellular immune response and phenotype plasticity of AMs to infection with SARS-CoV-2 within a PCLS model in a label-free and non-invasive manner. The ability to distinguish cells from infected PCLSs from cells of the uninfected control PCLS based on intracellular biochemical changes highlights the potential of Raman spectroscopy as a powerful diagnostic tool in immunology and clinical diagnostics.

Keywords

alveolar macrophages / delta / human precision-cut lung slice model / label-free phenotyping / omicron / phenotype plasticity / principal component analysis / Raman spectroscopy / SARS-CoV-2 / single-cell analysis

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Max Naumann, Franziska Hornung, Simone Eiserloh, Astrid Tannert, Antje Häder, Rustam R. Guliev, Tim Sandhaus, Stefanie Deinhardt-Emmer, Ute Neugebauer. Investigating alveolar macrophages in an human ex vivo precision-cut lung slice model of SARS-CoV-2 infection using Raman spectroscopy—A case study. Clinical and Translational Medicine, 2025, 15(9): e70453 DOI:10.1002/ctm2.70453

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References

[1]

Joshi N, Walter JM, Misharin AV. Alveolar macrophages. Cell Immunol. 2018; 330: 86-90.

[2]

Malainou C, Abdin SM, Lachmann N, Matt U, Herold S. Alveolar macrophages in tissue homeostasis, inflammation, and infection: evolving concepts of therapeutic targeting. J Clin Invest. 2023; 133(19): e170501.

[3]

Pervizaj-Oruqaj L, Ferrero MR, Matt U, Herold S. The guardians of pulmonary harmony: alveolar macrophages orchestrating the symphony of lung inflammation and tissue homeostasis. Eur Respir Rev. 2024; 33: 230263.

[4]

Morales-Nebreda L, Misharin AV, Perlman H, Budinger GRS. The heterogeneity of lung macrophages in the susceptibility to disease. Eur Respir Rev. 2015; 24: 505-509.

[5]

Chen S, Saeed AFUH, Liu Q, et al. Macrophages in immunoregulation and therapeutics. Sig Transduct Target Ther. 2023; 8: 207.

[6]

Lv J, Wang Z, Qu Y, et al. Distinct uptake, amplification, and release of SARS-CoV-2 by M1 and M2 alveolar macrophages. Cell Discov. 2021; 7: 24.

[7]

Wang Z, Li S, Huang B. Alveolar macrophages: Achilles' heel of SARS-CoV-2 infection. Sig Transduct Target Ther. 2022; 7: 242.

[8]

Bain CC, Rossi AG, Lucas CD. Pulmonary macrophages and SARS-Cov2 infection. In: Mariani SA, Cassetta L, Galluzzi L, eds. International Review of Cell and Molecular Biology: One, No One, One Hundred Thousand—The Multifaceted Role of Macrophages in Health and Disease—Part A. Academic Press; 2022: 1-28.

[9]

Byrne HJ. Spectralomics—towards a holistic adaptation of label free spectroscopy. Vib Spectrosc. 2024; 132: 103671.

[10]

Xu J, Morten KJ. Raman micro-spectroscopy as a tool to study immunometabolism. Biochem Soc Trans. 2024; 52: 733-745.

[11]

Hobro AJ, Smith NI. Spontaneous Raman bioimaging—looking to 2050. Vib Spectrosc. 2024; 131: 103668.

[12]

Xu W, Zhu W, Xia Y, et al. Raman spectroscopy for cell analysis: retrospect and prospect. Talanta. 2025; 285: 127283.

[13]

Ribeiro ARB, Silva ECO, Araújo PMC, Souza ST, Da Fonseca EJS, Barreto E. Application of Raman spectroscopy for characterization of the functional polarization of macrophages into M1 and M2 cells. Spectrochim Acta A Mol Biomol Spectrosc. 2022; 265: 120328.

[14]

Naumann M, Arend N, Guliev RR, et al. Label-free characterization of macrophage polarization using Raman spectroscopy. Int J Mol Sci. 2023; 24(1): 824.

[15]

Schultze-Rhonhof L, Marzi J, Carvajal Berrio DA, et al. Human tissue-resident peritoneal macrophages reveal resistance towards oxidative cell stress induced by non-invasive physical plasma. Front Immunol. 2024; 15: 1357340.

[16]

Hornung F, Köse-Vogel N, Le Saux CJ, et al. Uncovering a unique pathogenic mechanism of SARS-CoV-2 omicron variant: selective induction of cellular senescence. Aging. 2023; 15: 13593-13607.

[17]

Hui KPY, Ho JCW, Cheung M, et al. SARS-CoV-2 omicron variant replication in human bronchus and lung ex vivo. Nature. 2022; 603: 715-720.

[18]

Mautner L, Hoyos M, Dangel A, Berger C, Ehrhardt A, Baiker A. Replication kinetics and infectivity of SARS-CoV-2 variants of concern in common cell culture models. Virol J. 2022; 19: 76.

[19]

Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer; 2016. https://cran.r-project.org/web/packages/ggplot2/index.html

[20]

Auguie B, Antonov A. gridExtra: miscellaneous functions for “grid” graphics. 2017. https://cran.r-project.org/web/packages/gridExtra/index.html

[21]

Venables WN, Ripley BD. Modern Applied Statistics With S-PLUS. Springer Science & Business Media; 2002.

[22]

Beleites C, Sergo V. hyperSpec: a package to handle hyperspectral data sets in R. 2020. https://r-hyperspec.github.io/hyperSpec/

[23]

Wickham H, François R, Henry L, Müller K, Vaughan D. Dyplr: a grammar of data manipulation. 2023. https://cran.r-project.org/web/packages/dplyr/index.html

[24]

Bengtsson H. matrixStats: functions that apply to rows and columns of matrices (and to vectors). 2025. https://cran.r-project.org/web/packages/matrixStats/index.html

[25]

Belov A, McManus C, Beleites C, Hanson B, Fuller S. unmixR: hyperspectral unmixing methods. 2016. https://r-hyperspec.github.io/unmixR/

[26]

Ryabchykov O, Bocklitz T, Ramoji A, et al. Automatization of spike correction in Raman spectra of biological samples. Chemometr Intell Lab Syst. 2016; 155: 1-6.

[27]

Ryan CG, Clayton E, Griffin WL, Sie SH, Cousens DR. SNIP: a statistics-sensitive background treatment for the quantitative analysis of PIXE spectra in geoscience applications. Nucl Instr Methods Phys Res B: Beam Interact Mater Atoms. 1988; 34: 396-402.

[28]

Shlens J. A tutorial on principal component analysis. 2014; arXiv:1404.1100.

[29]

Hastie T, Tibshirani R, Friedma J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer; 2009.

[30]

Fisher RA. The use of multiple measurements in taxonomic problems. Ann Eugen. 1936; 7: 179-188.

[31]

Rygula A, Majzner K, Marzec KM, Kaczor A, Pilarczyk M, Baranska M. Raman spectroscopy of proteins: a review. J Raman Spectrosc. 2013; 44: 1061-1076.

[32]

Czamara K, Majzner K, Pacia MZ, Kochan K, Kaczor A, Baranska M. Raman spectroscopy of lipids: a review. J Raman Spectrosc. 2015; 46: 4-20.

[33]

Wiercigroch E, Szafraniec E, Czamara K, et al. Raman and infrared spectroscopy of carbohydrates: a review. Spectrochim Acta A Mol Biomol Spectrosc. 2017; 185: 317-335.

[34]

Dudek M, Zajac G, Szafraniec E, et al. Raman optical activity and Raman spectroscopy of carbohydrates in solution. Spectrochim Acta A Mol Biomol Spectrosc. 2019; 206: 597-612.

[35]

Benevides JM, Overman SA, Thomas GJ. Raman, polarized Raman and ultraviolet resonance Raman spectroscopy of nucleic acids and their complexes. J Raman Spectrosc. 2005; 36: 279-299.

[36]

Pezzotti G. Raman spectroscopy in cell biology and microbiology. J Raman Spectrosc. 2021; 52: 2348-2443.

[37]

Neupane AS, Willson M, Chojnacki AK, et al. Patrolling alveolar macrophages conceal bacteria from the immune system to maintain homeostasis. Cell. 2020; 183: 110-125.

[38]

Liu Y, Xu R, Gu H, et al. Metabolic reprogramming in macrophage responses. Biomark Res. 2021; 9: 1.

[39]

Huot N, Planchais C, Rosenbaum P, et al. SARS-CoV-2 viral persistence in lung alveolar macrophages is controlled by IFN-γ and NK cells. Nat Immunol. 2023; 24: 2068-2079.

[40]

Sefik E, Qu R, Junqueira C, et al. Inflammasome activation in infected macrophages drives COVID-19 pathology. Nature. 2022; 606: 585-593.

[41]

Yao Y, Subedi K, Liu T, et al. Surface translocation of ACE2 and TMPRSS2 upon TLR4/7/8 activation is required for SARS-CoV-2 infection in circulating monocytes. Cell Discov. 2022; 8: 89.

[42]

Grant RA, Morales-Nebreda L, Markov NS, et al. Circuits between infected macrophages and T cells in SARS-CoV-2 pneumonia. Nature. 2021; 590: 635-641.

[43]

McCormack CP, Yan AWC, Brown JC, et al. Modelling the viral dynamics of the SARS-CoV-2 delta and omicron variants in different cell types. J R Soc Interface. 2023; 20: 20230187.

[44]

Kornberg MD. The immunologic Warburg effect: evidence and therapeutic opportunities in autoimmunity. Wiley Interdiscip Rev Syst Biol Med. 2020; 12: e1486.

[45]

Icard P, Lincet H, Wu Z, et al. The key role of Warburg effect in SARS-CoV-2 replication and associated inflammatory response. Biochimie. 2021; 180: 169-177.

[46]

Chew BP, Park JS. Carotenoid action on the immune response. J Nutr. 2004; 134: 257S-261S.

[47]

Saheb Sharif-Askari N, Saheb Sharif-Askari F, Mdkhana B, et al. Upregulation of oxidative stress gene markers during SARS-COV-2 viral infection. Free Radic Biol Med. 2021; 172: 688-698.

[48]

Wieczfinska J, Kleniewska P, Pawliczak R. Oxidative stress-related mechanisms in SARS-CoV-2 infections. Oxid Med Cell Longev. 2022; 2022: 5589089.

[49]

Saeed S, Quintin J, Kerstens HHD, et al. Epigenetic programming of monocyte-to-macrophage differentiation and trained innate immunity. Science. 2014; 345: 1251086.

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2025 The Author(s). Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.

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