Benjamin Furtwängler

A big thanks to all the coauthors for this collaborative work!

Finally, Sabrina Richter developed scProtVelo to connect mRNA with protein expression via a translation model. It provided better velocity estimation during erythroid differentiation than RNA velocity and explained protein expression through mRNA better than linear correlation.

To compare mRNA and protein expression during lineage specification, we used the correlation of expression to the fate probability. We found proteins with different profiles compared to mRNA, e.g. B2M covaried with its complex members on protein-, but not on mRNA-level.

We analyzed hematopoietic stem cell (HSC) quiescence by correlating mRNA or protein expression to pseudotime. Expression trends revealed proteins that were not that apparent on mRNA level. Testing these proteins with a CRISPR knockout confirmed their importance for HSC function.

The gradual transitions of cell states led us to a trajectory analysis using CellRank. We found that the joint latent space is the superior input compared to CITE-seq or scp-MS data alone, indicating that the two modalities provide complementary information.

Having this scp-MS dataset, we went a step further to create a single-cell multi-omics dataset containing transcriptomics and proteomics. We integrated scRNA-seq (CITE-seq) with scp-MS, which worked great in our hands and enabled cell annotation of scp-MS with scRNA-seq labels.

We found many biologically relevant proteins driving the separation of HSPCs, which enabled defining the cell states in that system. We also showed that many functional related proteins covary across the populations enabling us to capture a diverse range of biological processes.

We laid the foundation for this project by analyzing over 2,500 human HSPCs with scp-MS. Via FACS, we included a couple of well characterized surface markers. Our scp-MS data recapitulated the human HSPC hierarchy and agreed well with the surface markers.

Mapping early human blood cell differentiation using single-cell proteomics and transcriptomics Single-cell transcriptomics (scRNA-seq) has facilitated the characterization of cell state heterogeneity and recapitulation of differentiation trajectories. However, the exclusive use of mRNA measurem...

We’re excited to present this integrative analysis of single-cell proteomics and transcriptomics of the human HSPC hierarchy. Now published in @science.org together with @fabiantheis.bsky.social, @erwinschoof.bsky.social, @porsebo.bsky.social.
www.science.org/doi/10.1126/...
🧵

The Cell Diversity Lab is well represented at this year's ASMS; please come and talk with Jakob Woessmann and Benjamin Furtwängler to hear all about their latest efforts in improving, and applying single-cell proteomics by MS.

#singlecellproteomics #teammassspec #cellheterogeneity

Informed Data-Independent Acquisition Enables Targeted Quantification of Key Regulatory Proteins in Cell Fate Decision at Single-Cell Resolution Cellular differentiation processes are largely orchestrated by variation in transcription factor (TF) abundance. Since these proteins are usually expressed at extremely low levels, studying TF-driven ...

I am excited to share the first project of my PhD on bioRxiv and just in time for #ASMS2025 !
Increasing the sensitivity in single cell proteomics to quantify transcription factors in single hematopoietic stem and progenitor cells by iDIA.
www.biorxiv.org/content/10.1...