Subnanogram proteomics: Impact of LC column selection, MS instrumentation and data analysis strategy on proteome coverage for trace samples
One of the greatest challenges for mass spectrometry (MS)-based proteomics is the limited ability to analyze small samples. Here we investigate the relative contributions of liquid chromatography (LC), MS instrumentation and data analysis methods with the aim of improving proteome coverage for sample sizes ranging from 0.5 ng to 50 ng. We show that the LC separations utilizing 30-µm-i.d. columns increase signal intensity by >3-fold relative to those using 75-µm-i.d. columns, leading to 32% increase in peptide identifications. The Orbitrap Fusion Lumos mass spectrometer significantly boosted both sensitivity and sequencing speed relative to earlier generation Orbitraps (e.g., LTQ-Orbitrap), leading to a ~3× increase in peptide identifications and 1.7× increase in identified protein groups for 2 ng tryptic digests of bacterial lysate. The Match Between Runs algorithm of open-source MaxQuant software further increased proteome coverage by ~ 95% for 0.5 ng samples and by ~42% for 2 ng samples. The present platform is capable of identifying >3000 protein groups from tryptic digestion of cell lysates equivalent to 50 HeLa cells and 100 THP-1 cells (~10 ng total proteins), respectively, and >950 proteins from subnanogram bacterial and archaeal cell lysates. The present ultrasensitive LC-MS platform is expected to enable deep proteome coverage for subnanogram samples, including single mammalian cells.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States). Environmental Molecular Sciences Laboratory (EMSL)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1430424
- Report Number(s):
- PNNL-SA-125645; 49001; KP1704020
- Journal Information:
- International Journal of Mass Spectrometry, Vol. 427, Issue C; ISSN 1387-3806
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
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