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ESPRESSO: spatiotemporal omics based on organelle phenotyping

Nature Methods volume 22, pages 2349–2361 (2025)Cite this article

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Abstract

Omics technologies such as genomics, transcriptomics, proteomics and metabolomics methods, have been instrumental in improving our understanding of complex biological systems by providing high-dimensional phenotypes of cell populations and single cells. Despite fast-paced advancements, these methods are limited in their ability to include a temporal dimension. Here, we introduce ESPRESSO (Environmental Sensor Phenotyping RElayed by Subcellular Structures and Organelles), a technique that provides single-cell, high-dimensional phenotyping resolved in space and time. ESPRESSO combines fluorescent labeling, advanced microscopy and image and data analysis methods to extract morphological and functional information from organelles at the single-cell level. We validate ESPRESSO’s methodology and its application across numerous cellular systems for the analysis of cell type, stress response, differentiation and immune cell polarization. We show that ESPRESSO can correlate phenotype changes with gene expression, and demonstrate its applicability to 3D cultures, offering a path to improved spatially and temporally resolved biological exploration of cellular states.

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Fig. 1: ESPRESSO workflow and organelle phenotypes of diverse cell lines.
Fig. 2: Differential ESPRESSO phenotype response to stressor exposure.
Fig. 3: ESPRESSO phenotype evolution during keratinocyte differentiation.
Fig. 4: Correlation of ESPRESSO phenotype evolution during macrophage M1 polarization with gene expression.
Fig. 5: ESPRESSO and scRNA-seq: multiomics phenotype characterization of keratinocyte differentiation.
Fig. 6: ESPRESSO in 3D tumor spheroids: phenotype evolution and response to stressors.

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Data availability

Demonstration data are available from Zenodo: https://zenodo.org/records/12737891 (ref. 79). Due to data size constraints, imaging data can be requested from Lorenzo Scipioni (lorenzo.scipioni@inserm.fr).

Code availability

Demonstration code is available from Zenodo: https://zenodo.org/records/12737891, scRNA-seq analysis can be found at https://github.com/yl-jia/ESPRESSO.

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Acknowledgements

This work was supported by funds from the Fondation Toulouse Cancer Santé (Chaire Oncobreast to L.S.), the National Science Foundation grants CBET2134916 to S.X.A. and Y.Y.J. and 1847005 to M.A.D., and the Allen Distinguished Investigator Award, a Paul G. Allen Frontiers Group advised grant of the Paul G. Allen Family Foundation (to J.A.P. and M.A.D.). The authors acknowledge the support of the Laboratory for Fluorescence Dynamics (P41GM103540), the American Italian Cancer Foundation (to M.D.B.), the Chao Family Comprehensive Cancer Center Genomics High-Throughput Facility Shared Resource, supported by the NCI of the NIH under award no. P30CA062203, and the UCI Skin Biology Resource Center supported by NIAMS under award no. P30AR075047.

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Author notes

  1. These authors contributed equally: Lorenzo Scipioni, Giulia Tedeschi.

Authors and Affiliations

  1. Centre de Recherche en Cancérologie de Toulouse, Toulouse, France

    Lorenzo Scipioni, Giulia Tedeschi & Songning Zhu

  2. Department of Biomedical Engineering, Laboratory for Fluorescence Dynamics, University of California, Irvine, Irvine, CA, USA

    Lorenzo Scipioni, Giulia Tedeschi, Enrico Gratton & Michelle A. Digman

  3. Department of Chemistry, University of California, Irvine, CA, USA

    Mariana X. Navarro & Jennifer A. Prescher

  4. Department of Developmental and Cell Biology, University of California, Irvine, CA, USA

    Yunlong Y. Jia & Scott X. Atwood

  5. Department of Dermatology, University of California, Irvine, CA, USA

    Yunlong Y. Jia & Scott X. Atwood

  6. Department of Pharmaceutical Sciences, University of California, Irvine, CA, USA

    Lila P. Halbers & Jennifer A. Prescher

  7. Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Melody Di Bona

  8. Department of Molecular Biology & Biochemistry, University of California, Irvine, CA, USA

    Jennifer A. Prescher

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Contributions

L.S. conceived the idea, performed experiments and wrote code. G.T. conceived the idea and performed experiments. M.X.N. generated the RAW246.7-NOS2 cell line. Y.Y.J. performed and analyzed the scRNA-seq experiments. Y.Y.J. and S.X.A. advised on the keratinocytes experiments and interpretation. S.Z. performed the cell cycle experiment. L.P.H. performed the viability assays. M.D.B. provided reagents and helped with interpretation. S.X.A., J.A.P., E.G. and M.A.D. provided support and supervised the project. L.S. took the lead in writing the manuscript. All authors provided critical feedback and helped shape the research, analysis and manuscript.

Corresponding authors

Correspondence to Lorenzo Scipioni or Michelle A. Digman.

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Nature Methods thanks Sang-Hee Shim and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Madhura Mukhopadhyay, in collaboration with the Nature Methods team.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–13, Table 1.

Reporting Summary

Supplementary Video 1

ESPRESSO Phenotype evolution in keratinocytes – Control Conditions. PacMAP for each individual time point (left) for the keratinocyte differentiation experiment (Control condition) shown in Fig. 3. Each point represents a cell detected in the corresponding frame (right). Points in the scatter plot (left) and cell masks in the image (right) are color-coded according to the cluster they belong to, following the same color-code as in Fig. 3.

Supplementary Video 2

ESPRESSO Phenotype evolution in keratinocytes – Calcium-induced differentiation. PacMAP for each individual time point (left) for the keratinocyte differentiation experiment (Calcium-induced differentiation condition) shown in Fig. 3. Each point represents a cell detected in the corresponding frame (right). Points in the scatter plot (left) and cell masks in the image (right) are color-coded according to the cluster they belong to, following the same color-code as in Fig. 3.

Supplementary Code

Code details.

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Scipioni, L., Tedeschi, G., Navarro, M.X. et al. ESPRESSO: spatiotemporal omics based on organelle phenotyping. Nat Methods 22, 2349–2361 (2025). https://doi.org/10.1038/s41592-025-02863-4

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