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VIBRANT: spectral profiling for single-cell drug responses


High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses. Three infrared-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. The resulting spectral profile is highly sensitive to phenotypic changes under drug perturbation. Using this property, we built a machine learning classifier to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with new mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic screening.

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Fig. 1: VIBRANT workflow.
Fig. 2: 3D scatter plots on three metabolic activities of cells treated by various drugs.
Fig. 3: Comparison between multiplexed probing approach and label-free approach.
Fig. 4: Accurate prediction of drug MoA at single-cell level.
Fig. 5: Identify test compounds with new MoA.
Fig. 6: Discriminating drug combinations.

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Data availability

The raw FTIR imaging data generated in this work are available from the corresponding author on reasonable request. The processed single-cell FTIR spectrum data are available at

Code availability

The codes are available at


  1. Kaddurah-Daouk, R., Kristal, B. S. & Weinshilboum, R. M. Metabolomics: a global biochemical approach to drug response and disease. Annu. Rev. Pharmacol. Toxicol. 48, 653–683 (2008).

    Article  CAS  PubMed  Google Scholar 

  2. Chandrasekaran, S. N., Ceulemans, H., Boyd, J. D. & Carpenter, A. E. Image-based profiling for drug discovery: due for a machine-learning upgrade? Nat. Rev. Drug Discov. 20, 145–159 (2021).

    Article  CAS  PubMed  Google Scholar 

  3. Wang, Y.-Y. et al. CeDR Atlas: a knowledgebase of cellular drug response. Nucleic Acids Res. 50, D1164–D1171 (2022).

    Article  CAS  PubMed  Google Scholar 

  4. Zhao, Y. et al. Measurement methods of single cell drug response. Talanta 239, 123035 (2022).

    Article  CAS  PubMed  Google Scholar 

  5. Fritzsch, F. S. O., Dusny, C., Frick, O. & Schmid, A. Single-cell analysis in biotechnology, systems biology, and biocatalysis. Annu. Rev. Chem. Biomol. Eng. 3, 129–155 (2012).

    Article  CAS  PubMed  Google Scholar 

  6. Walsh, A. J. et al. Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer. Cancer Res. 74, 5184–5194 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Loo, L.-H., Wu, L. F. & Altschuler, S. J. Image-based multivariate profiling of drug responses from single cells. Nat. Methods 4, 445–453 (2007).

    Article  CAS  PubMed  Google Scholar 

  8. Bray, M.-A. et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat. Protoc. 11, 1757–1774 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Haghighi, M., Caicedo, J. C., Cimini, B. A., Carpenter, A. E. & Singh, S. High-dimensional gene expression and morphology profiles of cells across 28,000 genetic and chemical perturbations. Nat. Methods 19, 1550–1557 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Spitzer, M. H. & Nolan, G. P. Mass cytometry: single cells, many features. Cell 165, 780–791 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  12. Heath, J. R., Ribas, A. & Mischel, P. S. Single-cell analysis tools for drug discovery and development. Nat. Rev. Drug Discov. 15, 204–216 (2016).

    Article  CAS  PubMed  Google Scholar 

  13. Jamieson, L. E. & Byrne, H. J. Vibrational spectroscopy as a tool for studying drug-cell interaction: could high throughput vibrational spectroscopic screening improve drug development? Vib. Spectrosc. 91, 16–30 (2017).

    Article  CAS  Google Scholar 

  14. Zhao, Z., Chen, C., Xiong, H., Ji, J. & Min, W. Metabolic activity phenotyping of single cells with multiplexed vibrational probes. Anal. Chem. 92, 9603–9612 (2020).

    Article  CAS  PubMed  Google Scholar 

  15. Tipping, W. J., Lee, M., Serrels, A., Brunton, V. G. & Hulme, A. N. Stimulated Raman scattering microscopy: an emerging tool for drug discovery. Chem. Soc. Rev. 45, 2075–2089 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Ma, J., Pazos, I. M., Zhang, W., Culik, R. M. & Gai, F. Site-specific infrared probes of proteins. Annu. Rev. Phys. Chem. 66, 357–377 (2015).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. Mignolet, A. & Goormaghtigh, E. High throughput absorbance spectra of cancerous cells: a microscopic investigation of spectral artifacts. Analyst 140, 2393–2401 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  18. Mignolet, A., Mathieu, V. & Goormaghtigh, E. HTS-FTIR spectroscopy allows the classification of polyphenols according to their differential effects on the MDA-MB-231 breast cancer cell line. Analyst 142, 1244–1257 (2017).

    Article  ADS  CAS  PubMed  Google Scholar 

  19. Flower, K. R. et al. Synchrotron FTIR analysis of drug treated ovarian A2780 cells: an ability to differentiate cell response to different drugs? Analyst 136, 498–507 (2011).

    Article  ADS  CAS  PubMed  Google Scholar 

  20. Denbigh, J. L. et al. Probing the action of a novel anti-leukaemic drug therapy at the single cell level using modern vibrational spectroscopy techniques. Sci. Rep. 7, 2649 (2017).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  21. Shi, L. et al. Mid-infrared metabolic imaging with vibrational probes. Nat. Methods 17, 844–851 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Liu, X. et al. Towards mapping mouse metabolic tissue atlas by mid-infrared imaging with heavy water labeling. Adv. Sci. 9, e2105437 (2022).

    Article  Google Scholar 

  23. Büttner, F. H. Cell-based assays for high-throughput screening. Expert Opin. Drug Discov. 1, 373–378 (2006).

    Article  PubMed  Google Scholar 

  24. Costa-Mattioli, M. & Walter, P. The integrated stress response: From mechanism to disease. Science 368, eaat5314 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Blay, V., Tolani, B., Ho, S. P. & Arkin, M. R. High-throughput screening: today’s biochemical and cell-based approaches. Drug Discov. Today 25, 1807–1821 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Stirling, D. R. et al. CellProfiler 4: improvements in speed, utility and usability. BMC Bioinf. 22, 433 (2021).

    Article  Google Scholar 

  27. Perlman, Z. E. et al. Multidimensional drug profiling by automated microscopy. Science 306, 1194–1198 (2004).

    Article  ADS  CAS  PubMed  Google Scholar 

  28. Taymaz-Nikerel, H., Karabekmez, M. E., Eraslan, S. & Kırdar, B. Doxorubicin induces an extensive transcriptional and metabolic rewiring in yeast cells. Sci. Rep. 8, 13672 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  29. Jamdade, V. S. et al. Therapeutic targets of triple-negative breast cancer: a review. Br. J. Pharmacol. 172, 4228–4237 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kummar, S. et al. Advances in using PARP inhibitors to treat cancer. BMC Med. 10, 25 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Caulfield, S. E., Davis, C. C. & Byers, K. F. Olaparib: a novel therapy for metastatic breast cancer in patients with a BRCA1/2 mutation. J. Adv. Pract. Oncol. 10, 167–174 (2019).

    PubMed  PubMed Central  Google Scholar 

  32. Johannessen, C. M., Clemons, P. A. & Wagner, B. K. Integrating phenotypic small-molecule profiling and human genetics: the next phase in drug discovery. Trends Genet. 31, 16–23 (2015).

    Article  CAS  PubMed  Google Scholar 

  33. Moffat, J. G., Rudolph, J. & Bailey, D. Phenotypic screening in cancer drug discovery - past, present and future. Nat. Rev. Drug Discov. 13, 588–602 (2014).

    Article  CAS  PubMed  Google Scholar 

  34. Rana, S. et al. A multichannel nanosensor for instantaneous readout of cancer drug mechanisms. Nat. Nanotechnol. 10, 65–69 (2015).

    Article  ADS  CAS  PubMed  Google Scholar 

  35. Vincent, F. et al. Phenotypic drug discovery: recent successes, lessons learned and new directions. Nat. Rev. Drug Discov. 21, 899–914 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Liu, F. T., Ting, K. M. & Zhou, Z.-H. Isolation forest. In Proc. 2008 Eighth IEEE International Conference on Data Mining (eds Giannotti, F. et al.) (IEEE, 2008).

  37. Liang, H. & Tan, A. R. Iniparib, a PARP1 inhibitor for the potential treatment of cancer, including triple-negative breast cancer. IDrugs 13, 646–656 (2010).

    CAS  PubMed  Google Scholar 

  38. Mateo, J., Ong, M., Tan, D. S. P., Gonzalez, M. A. & de Bono, J. S. Appraising iniparib, the PARP inhibitor that never was—what must we learn? Nat. Rev. Clin. Oncol. 10, 688–696 (2013).

    Article  CAS  PubMed  Google Scholar 

  39. Bayat Mokhtari, R. et al. Combination therapy in combating cancer. Oncotarget 8, 38022–38043 (2017).

    Article  PubMed  Google Scholar 

  40. Chalakur-Ramireddy, N. K. R. & Pakala, S. B. Combined drug therapeutic strategies for the effective treatment of triple negative breast cancer. Biosci. Rep. 38, BSR20171357 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Ianevski, A., Giri, A. K. & Aittokallio, T. SynergyFinder 2.0: visual analytics of multi-drug combination synergies. Nucleic Acids Res. 48, W488–W493 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Seydel, C. Single-cell metabolomics hits its stride. Nat. Methods 18, 1452–1456 (2021).

    Article  CAS  PubMed  Google Scholar 

  43. Qian, W. W. et al. Batch equalization with a generative adversarial network. Bioinformatics 36, i875–i883 (2020).

    Article  CAS  PubMed  Google Scholar 

  44. Ando, D. M., McLean, C. Y. & Berndl, M. Improving phenotypic measurements in high-content imaging screens. Preprint at bioRxiv (2017).

  45. Yeh, K., Kenkel, S., Liu, J.-N. & Bhargava, R. Fast infrared chemical imaging with a quantum cascade laser. Anal. Chem. 87, 485–493 (2015).

    Article  CAS  PubMed  Google Scholar 

  46. Zhang, Y. et al. Single-shot volumetric chemical imaging by mid-infrared photothermal Fourier light field microscopy. Preprint at bioRxiv (2022).

  47. Zhang, L., Zhang, Y. D., Zhao, P. & Huang, S.-M. Predicting drug-drug interactions: an FDA perspective. AAPS J. 11, 300–306 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Vlachogiannis, G. et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 359, 920–926 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hannon, G. J. RNA interference. Nature 418, 244–251 (2002).

    Article  ADS  CAS  PubMed  Google Scholar 

  50. Chen, S. et al. Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160, 1246–1260 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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W.M. acknowledges support from National Institutes of Health (grant no. R01 EB029523) and Chan Zuckerberg Initiative (Dynamic Imaging grant no. 2023-321166). We also thank L. Sun for help with the partial data analysis.

Author information

Authors and Affiliations



X.L. performed the cell culture and drug treatment experiments, FTIR imaging collection and data analysis. L.S., Z.Z. and J.S. provided advice in experiments and data analysis. X.L. and W.M. conceived the concept and wrote the paper with input from all authors.

Corresponding author

Correspondence to Wei Min.

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Competing interests

The authors declare no competing interests. Columbia University has filed a provisional patent application based on this work.

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Nature Methods thanks Malgorzata Baranska, Erik Goormaghtigh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Rita Strack, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 Average single-cell IR spectrum of individual vibrational probe or control.

(a) azido-PA. (b) 13C-AA. (c) d34-OA. (d) control without any labeling.

Extended Data Fig. 2

Single-cell image segmentation of MDA-MB-231 cells using CellProfiler.

Extended Data Fig. 3 Batch effects evaluation of VIBRANT with multiplexed vibrational probes.

3D scatter plots on three metabolic activities of cells treated by drugs were used for visualization. (a) Dactolisib, (b) Doxorubicin, and (c) Triacsin-C were selected as representatives. It can be observed that the shifts of the three metabolic activities between different batches are minimal. For (a) dactolisib, batch1 contains 495 cells, batch2 contains 693 cells and batch3 contains 700 cells. For (b) doxorubicin, batch1 contains 509 cells, batch2 contains 154 cells and batch 3 contains 443 cells. For (c) triacsin-C, batch 1 contains 534 cells, batch2 contains 427 cells and batch 3 contains 953 cells.

Extended Data Fig. 4 Dose responses of the vibrational probes of MDA-MB-231 cells after cycloheximide and daunorubicin treatments.

Error bar represents mean ± SD in 3 groups. For cycloheximide that specifically inhibits protein synthesis, only 13C-AA showed dose-response curve. The IC50 value from 13C-AA is 0.51 μM, which is around 2.5 times smaller than the IC50 value from the cell viability assay. For daunorubicin, the drug that intercalates DNA, signals from all three vibrational probes demonstrated dose responses. This is consistent with its mechanism to interfere with macromolecule metabolism. For the IC50 values, the IC50 from 13C-AA is 4.79 μM; from azido-PA is 1.97 μM; from d34-OA is 5.5 μM. Compared to the one measured in cell viability assay (1.28 μM), these IC50 values are slightly larger but on the same magnitude.

Extended Data Fig. 5 Feature importance ranking from Random Forest classifier.

The ranking was projected on IR spectrum with color gradient coding, where red color indicates more important features. This result further supports that features from metabolic labeling are highly important in predicting drug-perturbed cell phenotypes.

Extended Data Fig. 6 Prediction performance of drug MoAs using label-free FTIR imaging data.

Drugs belong to 6 MoAs were tested. MDA-MB-231 cells were treated by drugs (anisomycin, cycloheximide, MG-132, bortezomib, dactolisib, everolimus, doxorubicin, epirubicin, triacsin-c) at their IC50 concentrations without any labeling. The confusion matrix of the LDA classifier performance based on (a) data from the same batch (5,749 cells) and (b) data from different batches (2,982 cells) are presented. It can be observed that the accuracy dropped dramatically after including data from different batches using label-free measurements.

Extended Data Fig. 7

Test compounds’ Mahalanobis distance of annotated referenced drug MoA groups.

Supplementary information

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Supplementary Figs. 1–5 and Tables 1–5.

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Liu, X., Shi, L., Zhao, Z. et al. VIBRANT: spectral profiling for single-cell drug responses. Nat Methods 21, 501–511 (2024).

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