4 Citations
The richness of our somatosensory experience is reflected in the functional diversity of somatic sensory neurons Single-cell RNA sequencing of sensory neurons has revealed a molecular basis for such diversity However sensory neuron diversity has yet to be captured at the level of the proteome Here we combined electrophysiology with deep visual proteomics to quantify over proteins from phenotypically-defined sensory neurons in mice and identified proteomic markers of sensory neuron subtypes Comparative analysis revealed both concordance and meaningful divergence between transcriptomes and proteomes We further show that up to proteins can be quantified from one-fourth of a single neuron demonstrating ... More
The richness of our somatosensory experience is reflected in the functional diversity of somatic sensory neurons. Single-cell RNA sequencing of sensory neurons has revealed a molecular basis for such diversity1,2,3. However, sensory neuron diversity has yet to be captured at the level of the proteome. Here, we combined electrophysiology with deep visual proteomics 4 to quantify over 6000 proteins from phenotypically-defined sensory neurons in mice and identified proteomic markers of sensory neuron subtypes. Comparative analysis revealed both concordance and meaningful divergence between transcriptomes and proteomes. We further show that up to 3000 proteins can be quantified from one-fourth of a single neuron, demonstrating subset-specific protein signatures. In culture, nociceptive neurons can be acutely sensitized to mechanical stimuli by nerve growth factor (NGF) which normally drives inflammatory pain in vivo5. Indeed, overnight exposure of peptidergic nociceptors to NGF and a protein kinase C (PKC) activator produced functional sensitization associated with proteome changes. Functional knockdown experiments identified the up-regulated B3GNT2 enzyme as a potential effector of nociceptor sensitization. In summary, we present a high-resolution proteomic resource linking molecular identity to function, enabling the discovery of mechanisms underlying somatic sensation and pain sensitization. Less
Mass spectrometry-based proteomics increasingly demands platforms that combine quantitative rigor with the discovery capabilities of accurate mass systems Here we present the ZenoTOF system a compact mass spectrometry system that integrates enhanced ion capture and transmission optics with an optical detection system Zeno trap-enhanced MS MS electron-activated dissociation and scanning quadrupole data-independent acquisition ZT Scan DIA We show that ZT Scan DIA outperforms conventional variable-window DIA Zeno SWATH DIA in both identifications and quantitative reproducibility and demonstrate the platform s versatility across proteomics applications thousands of protein groups from bulk samples at up to samples per day single-cell proteomics yielding ... More
Mass spectrometry-based proteomics increasingly demands platforms that combine quantitative rigor with the discovery capabilities of accurate mass systems. Here we present the ZenoTOF 8600 system, a compact mass spectrometry system that integrates enhanced ion capture and transmission optics with an optical detection system, Zeno trap-enhanced MS/MS, electron-activated dissociation, and scanning quadrupole data-independent acquisition (ZT Scan DIA). We show that ZT Scan DIA outperforms conventional variable-window DIA (Zeno SWATH DIA) in both identifications and quantitative reproducibility, and demonstrate the platform’s versatility across proteomics applications: thousands of protein groups from bulk samples at up to 500 samples per day, single-cell proteomics yielding up to 4,700 proteins, accurate ratio recovery in mixed-species quantitative benchmarks, low-attomole targeted quantitation, and detection of disease-relevant phosphorylation in a Parkinson’s disease cellular model using complementary CID and EAD fragmentation. The instrument’s compact footprint makes it attractive for settings where both analytical breadth and operational robustness are required. Less
Stable carbon and nitrogen isotope ratios are widely used in the life sciences to investigate diet trophic interactions and metabolic fluxes but conventional isotope ratio mass spectrometry requires milligram-scale samples limiting its applicability to small or rare biological specimens Fourier Transform Isotopic Ratio Mass Spectrometry FT IsoR MS enables amino acid resolved isotope analysis in a proteomics-compatible workflow and has previously been demonstrated at the microgram scale Here we assess the lower sample limit of FT IsoR MS by integrating it with single-cell proteomics style sample preparation Using human HeLa cells cultured in C-glucose enriched and control media we show ... More
Stable carbon and nitrogen isotope ratios are widely used in the life sciences to investigate diet, trophic interactions, and metabolic fluxes, but conventional isotope ratio mass spectrometry requires milligram-scale samples, limiting its applicability to small or rare biological specimens. Fourier Transform Isotopic Ratio Mass Spectrometry (FT IsoR MS) enables amino acid–resolved isotope analysis in a proteomics-compatible workflow and has previously been demonstrated at the microgram scale. Here, we assess the lower sample limit of FT IsoR MS by integrating it with single-cell proteomics–style sample preparation. Using human HeLa cells cultured in 13C-glucose–enriched and control media, we show that reliable relative δ13C measurements can be obtained from as few as 50 cells, corresponding to <10 ng of total protein, with a precision of approximately ±9‰. The observed amino acid–specific labeling patterns are metabolically coherent and consistent with bulk measurements, while smaller cell numbers (≤10 cells) do not yield statistically robust results. These findings establish the practical sensitivity threshold of FT IsoR MS at the low-nanogram level and demonstrate its suitability for isotope-resolved analyses of small cell populations, micro-organoids, and other low-input biological samples, thereby extending stable isotope analysis toward single-cell–scale applications. Less
Quantitative proteomics relies on accurate selection of fragment ions for quantification yet most current algorithms apply simple strategies such as median intensity or single quality filters Modern data-independent acquisition DIA searches generate rich features such as fragment ion correlations retention time and many others that could be leveraged to assess fragment quality We introduce QuantSelect a novel strategy to select optimal fragments by systematically integrating these features via self-supervised deep learning QuantSelect uses a regularized weighted-variance loss on intensity traces normalized via our directLFQ algorithm This allows learning a fragment quality score without ground truth labels enabling on-the-fly training on ... More
Quantitative proteomics relies on accurate selection of fragment ions for quantification, yet most current algorithms apply simple strategies such as median intensity or single quality filters. Modern data-independent acquisition (DIA) searches generate rich features such as fragment ion correlations, retention time and many others that could be leveraged to assess fragment quality. We introduce QuantSelect, a novel strategy to select optimal fragments by systematically integrating these features via self-supervised deep learning. QuantSelect uses a regularized, weighted-variance loss on intensity traces normalized via our directLFQ algorithm. This allows learning a fragment quality score without ground truth labels, enabling on-the-fly training on label-free DIA datasets. Integrated within our alphaDIA pipeline, QuantSelect significantly improves quantitative accuracy and in some cases substantially corrects protein intensity estimation. Sensitivity in differential expression improved by 68% in a mixed-species benchmarking dataset and by 18% in single-cell data. QuantSelect provides a practical framework for data-driven fragment selection that improves accuracy, precision and downstream inference in DIA proteomics. Less