High-throughput Single-cell Proteomics Enabled by an Integrated Hyperplexing and Automatic Labelling Approach without Fractionation
Yang, Y.; Zhang, H.; Zeng, Q.; Jin, K.; Huang, C.; Liu, Y.; Liu, X.; Yang, J.; Ma, H.; He, F.
Show abstract
Single-cell proteomics (scProteomics) enables comprehensive analysis of protein composition, expression and functions at the single-cell level. While label-free techniques have generated promising results, achieving high-throughput scProteomics remains a substantial challenge. In this study, we present an approach that increases scProteomics throughput by approximately 30-fold through the true integration of isobaric tags (IBT) with tandem mass tags (TMT), a method we term Integral-Hyperplex. Due to remarkable differences in signal response between TMT16 and IBT16 reporter ions, we implemented two normalization strategies to ensure accurate and integrated quantification: (1) the inclusion of a normalization channel in both IBT and TMT groups to serve as internal standards; and (2) the generation of protein-specific conversion factors between IBT16 and TMT16 signals. We validated the quantification accuracy of the Integral-Hyperplex method by labeling HeLa digests at varying ratios. Furthermore, we demonstrated fully automated labeling on the active-matrix digital microfluidics chip, consuming only 12 nL of labeling reagent per single cell and with a reaction volume as low as 20 nL. Our approach was validated through proof-of-principle quantification experiments across three types of single cells. Approximately 2,000 protein groups were quantified per cell, and the three cell types can be well clustered, confirming the reliability of the Integral-Hyperplex approach. Approximately 300 samples can be analyzed per day due to insufficient resolution of timsTOF SCP. Throughput can be further enhanced using TMT32, additional labeling reagents, and more advanced mass spectrometers such as Orbitrap Astral and timsTOF Ultra 2, and our approach is theoretically capable of analyzing up to [~]2,000 single cells per day.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Contaminant Spot Check and Removal Assay (ContamSPOT) for Mass Spectrometry Analysis 98%
- Patch-Clamp Proteomics of Single Neuronal Somas in Tissue Using Electrophysiology and Subcellular Capillary Electrophoresis Mass Spectrometry 97%
- Expanding the depth and sensitivity of cross-link identification by differential ion mobility using FAIMS 97%
Similar papers in this journal
- Robust collection and processing for label-free single voxel proteomics 98%
- High-throughput and high-efficiency sample preparation for single-cell proteomics using a nested nanowell chip 98%
- Single Cell Proteomics Using a Trapped Ion Mobility Time-of-Flight Mass Spectrometer Provides Insight into the Post-translational Modification Landscape of Individual Human Cells 97%
Similar papers in this journal
- Inserting Pre-Analytical Chromatographic Priming Runs Significantly Improves Targeted Pathway Proteomics With Sample Multiplexing 98%
- Optimized Time-segmented Acquisition Expands Peptide and Protein Identification in TIMS-TOF Pro Mass Spectrometry 97%
- Peak identification and quantification by proteomic mass spectrogram decomposition 97%
Similar papers in this journal
- High Spatial Resolution Ambient Ionization Mass Spectrometry Imaging Using Microscopy Image Fusion Determines Tumor Margins 96%
- Segmented MS/MS acquisition of a1 ion-based strategy for in-depth proteome quantitation 96%
- SPPUSM: An MS/MS spectra merging strategy for improved low-input and single-cell proteome identification 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.