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VIBRANT: spectral profiling for single-cell drug responses

Nature Methods volume 21, pages 501–511 (2024)Cite this article

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Abstract

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 https://github.com/MinLabColumbia/VIBRANT.

Code availability

The codes are available at https://github.com/MinLabColumbia/VIBRANT.

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Acknowledgements

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

Author notes

  1. Lixue Shi

    Present address: Shanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, China

Authors and Affiliations

  1. Department of Chemistry, Columbia University, New York, NY, USA

    Xinwen Liu, Lixue Shi, Zhilun Zhao & Wei Min

  2. Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Jian Shu

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Jian Shu

  4. Department of Biomedical Engineering, Columbia University, New York, NY, USA

    Wei Min

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Contributions

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.

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Correspondence to Wei Min.

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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). https://doi.org/10.1038/s41592-024-02185-x

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