ESPRESSO: Spatiotemporal omics based on organelle phenotyping

This article has been Reviewed by the following groups

Read the full article See related articles

Listed in

Log in to save this article


Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have been instrumental to improving our understanding of complex biological systems. Despite fast-pace advancements, a crucial dimension is still left to explore: time. To capture this key parameter, we introduce ESPRESSO (Environmental Sensor Phenotyping RElayed by Subcellular Structures and Organelles), a pioneering technique that provides high-dimensional phenotyping resolved in space and time. Through a novel paradigm, ESPRESSO combines fluorescent labeling, advanced microscopy and bioimage and data analysis to extract morphological and functional information of the organelle network unveiling phenotypic changes over time at the single-cell level. In this work, we present ESPRESSO’s methodology and its application across numerous cellular systems, showcasing its ability to discern cell types, stress response, differentiation and immune cells polarization. We then correlate ESPRESSO phenotypic changes with gene expression and demonstrate its applicability to 3D cultures, offering a path to revolutionizing biological exploration, providing invaluable insights into cellular states in both space and time.

Article activity feed

  1. Asphenotypic changes during keratinocyte differentiation span across both space and time, this applicationperfectly showcases the power of ESPRESSO spatiotemporal omics in identifying not only the presenceof distinct phenotypes, but also providing insights about their spatiotemporal evolution.

    Again, I think a baseline here would make this claim more convincing. In other words, what aspects of the differentiation dynamics described here could only be captured by ESPRESSO?

  2. As shown in Figures 1c and 1d, GMM clustering easily identified the cell type-specific phenotypes andallowed the quantification of properties of interest in their organelle networ

    It would be helpful to compare this result to some baseline obtained from an established method like cell painting. In other words, can existing techniques also readily distinguish these cell types?

  3. to increase the acquisition speed 16-fold

    it would be helpful to also provide some absolute measures of throughput here, such as how many FOVs of a given size and resolution can be imaged per unit time.

  4. organelle properties are normalized, selected and reduced in dimensionality byPacMAP35, generating low-dimensional embeddings that encode the high-dimensional organelleproperties of each cell. A Gaussian Mixture Model (GMM36) clustering algorithm is then applied

    It sounds like the clustering was done after the embedding step; that is, using the low-dimensional embeddings from PacMAP, rather than the original feature matrix. If so, I'm worried that this will result in inaccurate clusters, as PacMAP (like all such methods) does not perfectly preserve the relationships between the original high-dimensional feature vectors.