New: Hyperplex Biomarker Panels
Custom Panels for Absolute Protein Quantification
New: Hyperplex Biomarker Panels
Custom Panels for Absolute Protein Quantification
Single-cell proteomics can provide researchers with valuable information by identifying a large number of proteins within individual cells. However, as samples are generally ultra-low input, analysis remains challenging.
In this seminar, we explore why Spectronaut® 17 is the solution of choice for analyzing single-cell proteomics data in DIA mode with presentations from two experts in the field, Christopher Adams (Bruker) and Valdemaras Petrosius (DTU).
Christopher Adams (Bruker)
Single cell proteomics is a rapidly developing field with the potential to understand cellular heterogeneity. Recent enhancements in trapped ion mobility spectrometry (TIMS) coupled to fast and sensitive mass spectrometry established in the timsTOF SCP as well as automated cell sorting and sample preparation with the cellenONE® platform with proteoCHIP®, allows for sensitive proteome analyses at the single cell level. Data Independent Acquisition – Parallel Accumulation and SErial Fragmentation (dia-PASEF) on the timsTOF SCP mass spectrometer was applied to HEK 293 and HeLa single cells obtained from the cellenONE and directly loaded onto Evotips (Evosep) and analyzed using Spectronaut 17. We report high protein and peptide identification with reproducibility and accurate quantification. This demonstrates outstanding performance and greatly aids the needs of single cell research.
Valdemaras Petrosius (DTU)
Mass spectrometry-based single-cell proteomics (scp-MS) can provide valuable insight into distinct cell-states and signalling patterns present in a cell population. However, carrying out proteomic profiling from the limited amount of material encapsulated in a single cell presents a significant challenge. Although great strides forward have been facilitated by extensive efforts to minimize sample losses during sample preparation and clever data acquisition approaches, further development is needed to increase the quantifiable proteome depth. Accordingly, we carried out a comprehensive analysis of orbitrap-based data-independent acquisition (DIA) for limited material proteomics. We found a fundamental difference between optimal DIA methods for high- and low-load samples. To build further upon our findings, we adopted a high-resolution MS1 quantification approach, accommodating long injection times and high resolution, while keeping the scan cycle time low enough to ensure robust quantification. Additionally, the improvements made in Spectronaut 17 had major impact on the quantifiable proteins from single cells. In conclusion, we present a complete experimental scp-MS workflow, combining DIA with accessible single-cell sample preparation and the latest chromatographic and computational advances to showcase our developments by profiling real single cells.
Short Bio Christopher Adams:
Chris Adams is the Business Development Director for Bioinformatics and former West Coast Proteomics Manager for Bruker Daltonics, where he has been since Feb. 2018. Prior to his time at Bruker Daltonics he was the Director of proteomics at the Stanford University Mass Spectrometry (SUMS) Laboratory where he had held increasing roles over 11 years being fortunate to have co-authored with 3 separate Nobel laureates. Chris received his PhD from Uppsala University, Sweden within the department of biological and medical mass spectrometry under the guidance of Professor Roman Zubarev. Prior to graduate school, Chris worked at various research positions with Amgen and Baxter Biosciences.
Short Bio Valdemaras Petrosius:
Valdemaras Petrosius is originally from Lithuania. He received his MSc and PhD from the University of Copenhagen, where his work focused on investigating DNA damage response mechanisms in cancer. Currently, Valdemaras is a postdoctoral researcher at DTU in the Erwin M. Schoof’s Cell Diversity Lab, where he is developing mass spectrometry-based workflows for ultra-low input sample processing and downstream bioinformatic analysis.
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