Despite advancements of data-independent acquisition mass spectrometry (DIA-MS) to provide comprehensive and reproducible proteome profiling, its utility in very low-input samples is limited. Due to different proteome complexities and corresponding peptide ion abundances, the conventional LC−MS/MS acquisition and widely used large-scale DIA libraries may not be suitable for the micro-nanogram samples. In this study, we report a sample size-comparable library-based DIA approach to enhance the proteome coverage of low-input nanoscale samples (i.e., nanogram cells, ∼5−50 cells). By constructing sample sizecomparable libraries, 2380 and 3586 protein groups were identified from as low as 0.75 (∼5 cells) and 1.5 ng (∼10 cells), respectively, highlighting one of the highest proteome coverage with good reproducibility (86%−99% in triplicate results). For the 0.75 ng sample (∼5 cells), significantly superior identification (2380 proteins) was achieved by small-size library-based DIA, compared to 1908, 1749, and 107 proteins identified from medium-size and large-size libraries and a lung cancer resource spectral library, respectively. A similar trend was observed using a different instrument and data analysis pipeline, indicating the generalized conclusion of the approach. Furthermore, the small-size library uniquely identified 518 (22%) proteins in the low-abundant region and spans over a 5-order dynamic range. Spectral similarity analysis revealed that the fragmentation ion pattern in the DIA-MS/MS spectra of the dataset and spectral library play crucial roles for mapping low abundant proteins. With these spectral libraries made freely available, the optimized library-based DIA strategy and DIA digital map will advance quantitative proteomics applications for mass-limited samples.

The long-term stability of affinity-based protein labeling probes is crucial to obtain reproducible protein labeling results. However, highly stable probes generally suffer from low protein labeling efficiency and pose significant challenges when labeling low abundance native proteins in living cells. In this paper, we report that protein labeling probes based on an ortho-difluorophenyl ester reactive module exhibit long-term stability in DMSO stock solution and aqueous buffer, yet they can undergo rapid and selective labeling of native proteins. This novel electrophile can be customized with a wide range of different protein ligands and is particularly well-suited for the labeling and imaging of transmembrane proteins. With this probe design, the identity and relative levels of basal and hypoxia-induced transmembrane carbonic anhydrases were revealed by live cell imaging and in-gel fluorescence analysis. We believe that the extension of this difluorophenyl ester reactive module would allow for the specific labeling of various endogenous membrane proteins, facilitating in-depth studies of their distribution and functions in biological processes.

Motivation Native top-down proteomics (nTDP) integrates native mass spectrometry (nMS) with top-down proteomics (TDP) to provide comprehensive analysis of protein complexes together with proteoform identification and characterization. Despite significant advances in nMS and TDP software developments, a unified and user-friendly software package for analysis of nTDP data remains lacking. Results We have developed MASH Native to provide a unified solution for nTDP to process complex datasets with database searching capabilities in a user-friendly interface. MASH Native supports various data formats and incorporates multiple options for deconvolution, database searching, and spectral summing to provide a “one-stop shop” for characterizing both native protein complexes and proteoforms. Availability and implementation The MASH Native app, video tutorials, written tutorials and additional documentation are freely available for download at https://labs.wisc.edu/gelab/MASH_Explorer/MASHSoftware.php. All data files shown in user tutorials are included with the MASH Native software in the download .zip file. Supplementary information Supplementary data are available at Bioinformatics online.

Native top-down proteomics (nTDP) integrates native mass spectrometry (nMS) with top-down proteomics (TDP) to provide comprehensive analysis of protein complexes together with proteoform identification and characterization. Despite significant advances in nMS and TDP software developments, a unified and user-friendly software package for analysis of nTDP data remains lacking. Herein, we have developed MASH Native to provide a unified solution for nTDP to process complex datasets with database searching capabilities in a user-friendly interface. MASH Native supports various data formats and incorporates multiple options for deconvolution, database searching, and spectral summing to provide a one-stop shop for characterizing both native protein complexes and proteoforms. The MASH Native app, video tutorials, written tutorials and additional documentation are freely available for download athttps://labs.wisc.edu/gelab/MASH_Explorer/MASHNativeSoftware.php. All data files shown in user tutorials are included with the MASH Native software in the download .zip file.

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