Evosep webinar

Single Cell Proteomics

Available on Demand

Single cell proteomics is a field in rapid development, enabled by significant technological improvements over the last years and it presents a key contribution to answer fundamental biological questions about heterogeneity within complex systems.



Gaining biological insights with an automated single-cell proteomics workflow

Talk by Marvin Thielert, PHD Student at Research Department Proteomics and Signal Tranduction, Max Planck Institute of Biochemistry

Single-cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. Despite recent technological advances, it still lacks behind in proteomic depth, throughput and reproducibility. We therefore developed a single-cell proteomics workflow using miniaturized sample preparation, low-flow chromatography and dia-PASEF. We show that MS raw signal intensity correlates with the cell cycle stage and that single cells have a stable core proteome upon their cell cycle. Additionally, we implemented automation on a pipetting robot to improve robustness including downstream loading of peptide packages on Evotips. We enhanced the chromatographic reproducibility by using the ionOpticks column together with the 40 samples per day method and applied this to Deep Visual Proteomics.


The ProteoChip – increasing the throughput for label-free single-cell proteomics sample preparation

Talk by Claudia Ctortecka, Postdoc at Broad Institute of MIT and Harvard

Despite advances in sample-preparation and instrumentation the characterization of large but low-input cohorts is still challenged by throughput and reproducibility. We have therefore adapted our label-free proteoCHIP design to directly interface with the Evosep One for single-cell sample preparation and chromatographic separation without error-prone manual handling. This reproducibly increases identifications on average by >30% and in conjunction with the IonOpticks Aurora column we further reduce chromatographic separation time by 50%. Even though this improved throughput yields 30% lower peptide identifications in discovery mode we preserve a comparable dynamic range across extensive sample cohorts.