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RT @BioinfoAdv: @thalexandrov 🧮 Access the tools and resources here:  Web app: R package (S2IsoMEr): https://t.co/….

RT @BioinfoAdv: @thalexandrov This paper introduces two complementary tools that address the challenge of ambiguous metabolite identificati….

RT @BioinfoAdv: 🧫 Now published in Bioinformatics Advances: “Enrichment analysis for spatial and single-cell metabolomics accounting for mo….

RT @slavov_n: The JPR issue on Single-Cell Omics is out. As the editorial highlights, understanding biological systems requires direct mea….

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RT @metaspace2020: 🔍 Did you know that you can select Regions of Interest (ROIs) in METASPACE?. We’ll be sharing more tips and best practic….

RT @metaspace2020: Ready to take your metabolite annotation to the next level?. Join our first FREE webinar on Best Practices for Metabolit….

RT @BrukerMassSpec: Join Bruker's upcoming webinar on Single-Cell Metabolomics: Revealing Cellular Diversity Using Isotope Tracing with Sha….

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RT @thalexandrov: Please welcome HT SpaceM, our new method for high-throughput small-molecule single-cell metabolomics: 👉Delafiori, Shahraz….

And of course I've forgotten to include the title image in the starting tweet 😅, so let me use it as a capstone instead! 🧵👆

Thanks for reading all the way until here 🙂.

We have analyzed 11 cell lines, discovered cell-type specific markers, with all the data publicly available, and we are providing Jupyter notebooks for reproducing the figures and helping computational scientists with re-using the proposed data analysis framework.

Moreover, we are presenting a novel method for building and analyzing single-cell metabolite networks which show metabolite relationships which are missed from bulk analyses and can be used to study metabolic co-regulation.

Adding to the method itself, we are presenting a unified data analysis framework for single-cell metabolomics covering all steps we found to be essential for SCM data analysis: quality control, unsupervised characterization, differential analysis, & functional enrichment analysis

We could detect 100+ metabolites from single-cells, with the majority validated by LC-MS/MS. We present the largest known-to-us SCM dataset with 120K cells from 120 samples, with a large number of replicates demonstrating reproducibility and technical variability (which is low).

Our lab manager Shahraz started working on it, and lo and behold, after several years and most of our team members involved, we have HT SpaceM: a method for small-molecule SCM, which at the same time is high-throughput and reproducible 🤩

However, we realized that SpaceM detect lipids & misses small-molecule metabolites. Others faced similar challenge, with most MALDI-imaging single-cell metabolomics studies reporting lipids mainly, see e.g. review Saunders et al, Curr Opin Chem Biol 2023

How it started? Earlier, we have developed SpaceM, a method for single-cell metabolomics (SCM) Similar to methods from other labs such as MAMS or microMS, we used MALDI-imaging, which is becoming a major technology for SCM.

Please welcome HT SpaceM, our new method for high-throughput small-molecule single-cell metabolomics: 👉Delafiori, Shahraz et al, BioRxiv 🧵👇.