Labeling approaches using isobaric chemical tags (e.g., isobaric tagging for relative and absolute quantification, iTRAQ and tandem mass tag, TMT) have been widely applied for the quantification of peptides and proteins in bottom-up MS. However, until recently, successful applications of these approaches to top-down proteomics have been limited because proteins tend to precipitate and “crash” out of solution during TMT labeling of complex samples making the quantification of such samples difficult. In this study, we report a top-down TMT MS platform for confidently identifying and quantifying low molecular weight intact proteoforms in complex biological samples. To reduce the sample complexity and remove large proteins from complex samples, we developed a filter-SEC technique that combines a molecular weight cutoff filtration step with high-performance size exclusion chromatography (SEC) separation. No protein precipitation was observed in filtered samples under the intact protein-level TMT labeling conditions. The proposed top-down TMT MS platform enables high-throughput analysis of intact proteoforms, allowing for the identification and quantification of hundreds of intact proteoforms from Escherichia coli cell lysates. To our knowledge, this represents the first high-throughput TMT labeling-based, quantitative, top-down MS analysis suitable for complex biological samples.

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Soil covers most of Earth’s continental surface and is fundamental to life-sustaining processes such as agriculture. Given its rich biodiversity, soil is also a major source for natural product drug discovery from soil microorganisms. However, the study of the soil small molecule profile has been challenging due to the complexity and heterogeneity of this matrix. In this study, we implemented high-resolution liquid chromatography–tandem mass spectrometry and large-scale data analysis tools such as molecular networking to characterize the relative contributions of city, state and regional processes on backyard soil metabolite composition, in 188 soil samples collected from 14 USA States, representing five USA climate regions. We observed that region, state and city of collection all influence the overall soil metabolite profile. However, many metabolites were only detected in unique sites, indicating that uniquely local phenomena also influence the backyard soil environment, with both human-derived and naturally-produced (plant-derived, microbially-derived) metabolites identified. Overall, these findings are helping to define the processes that shape the backyard soil metabolite composition, while also highlighting the need for expanded metabolomic studies of this complex environment.

Isobaric chemical tag labeling (e.g., TMT) is a commonly used approach in quantitative proteomics, and quantification is enabled through detection of low-mass reporter ions generated after MS2 fragmentation. Recently, we have introduced and optimized an intact protein-level TMT labeling platform that demonstrated >90% labeling efficiency in complex samples with top-down proteomics. Higher-energy collisional dissociation (HCD) is commonly utilized for isobaric tag-labeled peptide fragmentation because it produces accurate reporter ion intensities and avoids loss of low mass ions. HCD energies have been optimized for isobaric tag labeled-peptides but have not been systematically evaluated for isobaric tag-labeled intact proteins. In this study, we report a systematic evaluation of normalized HCD fragmentation energies (NCEs) on TMT-labeled HeLa cell lysate using top-down proteomics. Our results suggested that reporter ions often result in higher ion intensities at higher NCEs. Optimal fragmentation of intact proteins for identification, however, required relatively lower NCE. We further demonstrated that a stepped NCE scheme with energies from 30% to 50% resulted in optimal quantification and identification of TMT-labeled HeLa proteins. These parameters resulted in an average reporter ion intensity of ∼4E4 and average proteoform spectrum matches (PrSMs) of >1000 per RPLC-MS/MS run with a 1% false discovery rate (FDR) cutoff.

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