Lecture
Roadmap to success in single cell proteomics – from sample preparation to data interpretation
- at -
- ICM Saal 2
- Type: Lecture
Lecture description
Single-cell proteomics (SCP) by mass spectrometry has evolved to a powerful technique for investigation of cellular heterogeneity with increasing coverage and throughput. The success of SCP is tightly connected to technological improvements in
sample preparation as well as in nano-flow liquid chromatography (LC) and to high resolution, high sensitivity mass spectrometry (MS). This talk summarizes optimization steps needed on the way to successful SCP applications.
We finetune label free sample preparation techniques and aim to introduce a robust and sensitive LC-MS setting by benchmarking state of the art chromatographic columns, MS instrumentation and data analysis strategies.
In our recently published workflow (1) combining the One-Pot (2) single cell sample preparation with the Aurora Ultimate 25cm column and the Astral MS we reached unprecedented coverages and quantified 5000 proteins per single human lung cancer
cell. Testing gradient speeds ranging from 30 to 140 samples per day (SPD), we found a sweet spot for optimal coverage and quantitative accuracy at a throughput of 50SPD for this cell type. Depending on cell-type (and size) shorter gradients, yielding sharper hence higher intensity, peaks were shown to be more beneficial though. We further found the adoption of individual parameters, like lowering the resolution of FAIMS filtering can significantly improve peptide identifications by up to 18% and yield
improved quantitative precision of low-load measurements (3).
We are convinced that our workflow is highly valuable to eventually transition SCP from its developmental phase to reliable biologist’s tool. We showcase this by applying the optimized SCP platform to various sample types ranging from large oocytes down to
bacterial cells where we investigate the bacterial heat stress response, focusing on the essential GroELS folding chaperone and the pArg/ClpC proteostasis-system.
Literature:
1. Matzinger M et al., Anal. Chem., 2023 doi:10.1021/acs.analchem.2c05022
2. Bubis JA et al., Nat. Methods., 2025 doi:10.1038/s41592-024-02559-1
3. Hoch DG et al., bioRxiv., 2025 doi:10.1101/2025.08.22.671812
sample preparation as well as in nano-flow liquid chromatography (LC) and to high resolution, high sensitivity mass spectrometry (MS). This talk summarizes optimization steps needed on the way to successful SCP applications.
We finetune label free sample preparation techniques and aim to introduce a robust and sensitive LC-MS setting by benchmarking state of the art chromatographic columns, MS instrumentation and data analysis strategies.
In our recently published workflow (1) combining the One-Pot (2) single cell sample preparation with the Aurora Ultimate 25cm column and the Astral MS we reached unprecedented coverages and quantified 5000 proteins per single human lung cancer
cell. Testing gradient speeds ranging from 30 to 140 samples per day (SPD), we found a sweet spot for optimal coverage and quantitative accuracy at a throughput of 50SPD for this cell type. Depending on cell-type (and size) shorter gradients, yielding sharper hence higher intensity, peaks were shown to be more beneficial though. We further found the adoption of individual parameters, like lowering the resolution of FAIMS filtering can significantly improve peptide identifications by up to 18% and yield
improved quantitative precision of low-load measurements (3).
We are convinced that our workflow is highly valuable to eventually transition SCP from its developmental phase to reliable biologist’s tool. We showcase this by applying the optimized SCP platform to various sample types ranging from large oocytes down to
bacterial cells where we investigate the bacterial heat stress response, focusing on the essential GroELS folding chaperone and the pArg/ClpC proteostasis-system.
Literature:
1. Matzinger M et al., Anal. Chem., 2023 doi:10.1021/acs.analchem.2c05022
2. Bubis JA et al., Nat. Methods., 2025 doi:10.1038/s41592-024-02559-1
3. Hoch DG et al., bioRxiv., 2025 doi:10.1101/2025.08.22.671812
