Global bottom-up mass spectrometry (MS)-based proteomics is widely used for protein identification and quantification to achieve a comprehensive understanding of the composition, structure, and function of the proteome. However, traditional sample preparation methods are timeconsuming, typically including overnight tryptic digestion, extensive sample cleanup to remove MS-incompatible surfactants, and offline sample fractionation to reduce proteome complexity prior to online liquid chromatography−tandem mass spectrometry (LC-MS/MS) analysis. Thus, there is a need for a fast, robust, and reproducible method for protein identification and quantification from complex proteomes. Herein, we developed an ultrafast bottom-up proteomics method enabled by Azo, a photocleavable, MS-compatible surfactant that effectively solubilizes proteins and promotes rapid tryptic digestion, combined with the Bruker timsTOF Pro, which enables deeper proteome coverage through trapped ion mobility spectrometry (TIMS) and parallel accumulation−serial fragmentation (PASEF) of peptides. We applied this method to analyze the complex human cardiac proteome and identified nearly 4000 protein groups from as little as 1 mg of human heart tissue in a single one-dimensional LC-TIMS-MS/MS run with high reproducibility. Overall, we anticipate this ultrafast, robust, and reproducible bottom-up method empowered by both Azo and the timsTOF Pro will be generally applicable and greatly accelerate the throughput of large-scale quantitative proteomic studies. Raw data are available via the MassIVE repository with identifier MSV000087476.

Antibody-drug conjugates (ADCs) are one of the fastest growing classes of anticancer therapies. Combining the high targeting specificity of monoclonal antibodies (mAbs) with cytotoxic small molecule drugs, ADCs are complex molecular entities that are intrinsically heterogeneous. Primary sequence variants, varied drug-toantibody ratio (DAR) species, and conformational changes in the starting mAb structure upon drug conjugation must be monitored to ensure the safety and efficacy of ADCs. Herein, we have developed a high-throughput method for the analysis of cysteine-linked ADCs using trapped ion mobility spectrometry (TIMS) combined with top-down mass spectrometry (MS) on a Bruker timsTOF Pro. This method can analyze ADCs (∼150 kDa) by TIMS followed by a three-tiered top-down MS characterization strategy for multi-attribute analysis. First, the charge state distribution and DAR value of the ADC are monitored (MS1). Second, the intact mass of subunits dissociated from the ADC by lowenergy collision-induced dissociation (CID) is determined (MS2). Third, the primary sequence for the dissociated subunits is characterized by CID fragmentation using elevated collisional energies (MS3). We further automate this workflow by directly injecting the ADC and using MS segmentation to obtain all three tiers of MS information in a single 3-min run. Overall, this work highlights a multi-attribute top-down MS characterization method that possesses unparalleled speed for high-throughput characterization of ADCs.

Exosomes are small extracellular vesicles (EVs) secreted by all cells and found in biological fluids, which can serve as minimally invasive liquid biopsies with extremely high therapeutic and diagnostic potential. Mass spectrometry (MS)-based proteomics is a powerful technique to profile and quantify the protein content in exosomes, but the current methods require laborious and time-consuming multistep sample preparation that significantly limit throughput. Herein, we report a one-pot exosome proteomics method enabled by a photocleavable surfactant, Azo, to simplify exosomal lysis, effectively extract proteins, and expedite digestion. We have applied this method to exosomes derived from isolated mammary fibroblasts and confidently identified 3466 proteins and quantified 2288 proteins using a reversed-phase liquid chromatography coupled to trapped ion mobility spectrometry (TIMS) quadrupole time-of-flight mass spectrometer. Here, 3166 (91%) of the identified proteins are annotated in the exosome/EVs databases, ExoCarta and Vesiclepedia, including important exosomal markers, CD63, PDCD6IP, and SDCBP. This method is fast, simple, and highly effective at extracting exosomal proteins with high reproducibility for deep exosomal proteome coverage. We envision that this method could be generally applicable for exosome proteomics applications in biomedical research, therapeutic interventions, and clinical diagnostics.

Global bottom-up mass spectrometry (MS)-based proteomics is widely used for protein identification and quantification to achieve a comprehensive understanding of the composition, structure, and function of the proteome. However, traditional sample preparation methods are time-consuming, typically including overnight tryptic digestion, extensive sample clean-up to remove MS-incompatible surfactants, and offline sample fractionation to reduce proteome complexity prior to online liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Thus, there is a need for a fast, robust, and reproducible method for protein identification and quantification from complex proteomes. Herein, we developed an ultrafast bottom-up proteomics method enabled by Azo, a photocleavable, MS-compatible surfactant that effectively solubilizes proteins and promotes rapid tryptic digestion, combined with the Bruker timsTOF Pro, which enables deeper proteome coverage through trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF) of peptides. We applied this method to analyze the complex human cardiac proteome and identified nearly 4,000 protein groups from as little as 1 mg of human heart tissue in a single one-dimensional LC-TIMS-MS/MS run with high reproducibility. Overall, we anticipate this ultrafast, robust, and reproducible bottom-up method empowered by both Azo and the timsTOF Pro will be generally applicable and greatly accelerate the throughput of large-scale quantitative proteomic studies. Raw data are available via the MassIVE repository with identifier MSV000087476.

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