Project description:High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.
Project description:This work demonstrates the utility of high-throughput nanoLC-MS and label-free quantification (LFQ) for sample-limited bottom-up proteomics analysis, including single-cell proteomics (SCP). Conditions were optimized on a 50 μm internal diameter (I.D.) column operated at 100 nL/min in the direct injection workflow to balance method sensitivity and sample throughput from 24 to 72 samples/day. Multiple data acquisition strategies were also evaluated for proteome coverage, including data-dependent acquisition (DDA), wide-window acquisition (WWA), and wide-window data-independent acquisition (WW-DIA). Analyzing 250 pg HeLa digest with a 10-min LC gradient (72 samples/day) provided >900, >1,800, and >3,000 protein group identifications for DDA, WWA, and WW-DIA, respectively. Total method cycle time was further reduced from 20 to 14.4 min (100 samples/day) by employing a trap-and-elute workflow, enabling 70% mass spectrometer utilization. The method was applied to library-free DIA analysis of single-cell samples, yielding >1,700 protein groups identified. In conclusion, this study provides a high-sensitivity, high-throughput nanoLC-MS configuration for sample-limited proteomics.
Project description:Novel mass spectrometry (MS)-based proteomic tools with extremely high sensitivity and high peak capacity are required for comprehensive characterization of protein molecules in mass-limited samples. We reported a nanoRPLC-CZE-MS/MS system for deep bottom-up proteomics of low micrograms of human cell samples in previous work. In this work, we improved the sensitivity of the nanoRPLC-CZE-MS/MS system drastically via employing bovine serum albumin (BSA)-treated sample vials, improving the nanoRPLC fraction collection procedure, and using a short capillary for fast CZE separation. The improved nanoRPLC-CZE produced a peak capacity of 8500 for peptide separation. The improved system identified 6500 proteins from a MCF7 proteome digest starting with only 500 ng of peptides using a Q-Exactive HF mass spectrometer. The system produced a comparable number of protein identifications (IDs) to our previous system and the two-dimensional (2D) nanoRPLC-MS/MS system developed by Mann's group with 10-fold and 4-fold less sample consumption, respectively. We coupled the single-spot solid phase sample preparation (SP3) method to the improved nanoRPLC-CZE-MS/MS for bottom-up proteomics of 5000 HEK293T cells, resulting in 3689 protein IDs with the consumption of a peptide amount that corresponded to only roughly 1000 cells.
Project description:This is a prospective, multi-centered study to assess whether urine metabolomics can play a role in the screening of colorectal cancer (CRC). Urine samples will be collected from 1000 patients going through an established CRC screening program, and from a further 500 patients who already have a diagnosis of CRC. Using nuclear magnetic resonance (NMR) spectroscopy, the 1H NMR spectrum of urine samples will be analyzed for specific metabolites, and establish the metabolomic signature of colorectal cancer. The results from metabolomic urinalysis of this screening cohort will be compared with results from colonoscopy, histological descriptions, fecal occult blood testing (FOBT), and fecal immune testing (FIT) to assess the accuracy of urine metabolomics in identifying patients with polyps and malignancies. The urine metabolomic results from the colorectal cancer group will be correlated with operative, histological and clinical staging to define the role of urine metabolomics in assessing colorectal cancer type, location and stage. Additionally approximately 300 urine samples from breast cancer patients and 300 from prostate cancer patients will be collected to validate that the colorectal cancer signature is unique.