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Analysis of cicada wing surface constituents by comprehensive multidimensional gas chromatography for species differentiation
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Solid phase extraction chromatography-based radiochemical isolation of cyclotron-produced 51Mn from enriched 54Fe targets
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Thermoset materials characterization by thermal desorption or pyrolysis based gas chromatography-mass spectrometry methods
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Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography–Mass Spectrometry for High-Throughput Single-Cell Proteomics
Single-cell proteomics can provide critical biological insight into the cellular heterogeneity that is masked by bulk-scale analysis. Here, we have developed a nanoPOTS (nanodroplet processing in one pot for trace samples) platform and demonstrated its broad applicability for single-cell proteomics. However, because of nanoliter-scale sample volumes, the nanoPOTS platform is not compatible with automated LC-MS systems, which significantly limits sample throughput and robustness. To address this challenge, we have developed a nanoPOTS autosampler allowing fully automated sample injection from nanowells to LC-MS systems. We also developed a sample drying, extraction, and loading workflow to enable reproducible and reliable sample injection. The sequential analysis of 20 samples containing 10 ng tryptic peptides demonstrated high reproducibility with correlation coefficients of >0.995 between any two samples. The nanoPOTS autosampler can provide analysis throughput of 9.6, 16, and 24 single cells per day using 120, 60, and 30 min LC gradients, respectively. As a demonstration for single-cell proteomics, the autosampler was first applied to profiling protein expression in single MCF10A cells using a label-free approach. At a throughput of 24 single cells per day, an average of 256 proteins was identified from each cell and the number was increased to 731 when the Match Between Runs algorithm of MaxQuant was used. Using a multiplexed isobaric labeling approach (TMT-11plex), ~77 single cells could be analyzed per day. We analyzed 152 cells from three acute myeloid leukemia cell lines, resulting in a total of 2558 identified proteins with 1465 proteins quantifiable (70% valid values) across the 152 cells. These data showed quantitative single-cell proteomics can cluster cells to distinct groups and reveal functionally distinct differences.
Burn Intensity Drives the Alteration of Phenolic Lignin to (Poly) Aromatic Hydrocarbons as Revealed by Pyrolysis Gas Chromatography–Mass Spectrometry (Py-GC/MS)
High-intensity wildfires alter the chemical composition of organic matter, which is expected to be distinctly different from low-intensity prescribed fires. In this study, we used pyrolysis gas chromatography/mass spectrometry (Py-GC/MS), in conjunction with solid-state 13 C nuclear magnetic resonance (NMR) and Fourier transform infrared (FT-IR) spectroscopy, to assess chemical alterations from three wildfires and a long-term frequent prescribed fire site. Our results showed that black ash formed under moderate intensity burns contained less aromatic (ArH), polyaromatic hydrocarbon (PAH), and nitrogen-containing compounds (Ntg) but more lignin (LgC) and phenol compounds (PhC), compared to white ash formed under high intensity burns. Both 13 C NMR and FT-IR confirmed a higher relative percentage of carboxyl carbon in white ash, indicating the potential for higher water solubility and more mobile carbon, relative to black ash. Compared to wildfires, ash from low-intensity prescribed fire contained less ArH, PAH, and Ntg and more LgC and PhC. Controlled laboratory burning trials indicated that organic matter alteration was sensitive to the burn temperature, but not related to the fuel type (pine vs fir) nor oxygen absence/presence at high burn temperatures. This study concludes that higher burn temperatures resulted in higher (poly)aromatic carbon/nitrogen and lower lignin/phenol compounds.