Single-cell proteomics can reveal cellular phenotypic heterogeneity and cell-specific functional networks underlying biological processes. Here, we present a streamlined workflow combining microfluidic chips for all-in-one proteomic sample preparation and data-independent acquisition (DIA) mass spectrometry (MS) for proteomic analysis down to the single-cell level. The proteomics chips enable multiplexed and automated cell isolation/counting/imaging and sample processing in a single device. Combining chip-based sample handling with DIA-MS using project-specific mass spectral libraries, we profile on average ~1,500 protein groups across 20 single mammalian cells. Applying the chip-DIA workflow to profile the proteomes of adherent and non-adherent malignant cells, we cover a dynamic range of 5 orders of magnitude with good reproducibility and <16% missing values between runs. Taken together, the chip-DIA workflow offers all-in-one cell characterization, analytical sensitivity and robustness, and the option to add additional functionalities in the future, thus providing a basis for advanced single-cell proteomics applications.

The data‐independent acquisition mass spectrometry (DIA‐MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA‐MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA‐based proteomics profiling. Here, we review the evolution of the DIA‐MS techniques, from earlier proof‐of‐principle of parallel fragmentation of all‐ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH‐MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA‐MS. We further summarize recent applications of DIA‐MS and experimentally‐derived as well as in silico spectra library resources for large‐scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next‐generation DIA‐MS for clinical proteomics, we outline the challenges in processing multi‐dimensional DIA data set and large‐scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

Membrane proteins are crucial targets for cancer biomarker discovery and drug development. However, in addition to the inherent challenges of hydrophobicity and low abundance, complete membrane proteome coverage of clinical specimen is usually hindered by the requirement of large amount of starting materials. Toward comprehensive membrane proteomic profiling for small amounts of samples (10 μg), we developed high-pH reverse phase (Hp-RP) combined with stop-and-go extraction tip (StageTip) technique, as a fast (∼15 min.), sensitive, reproducible, high-resolution and multiplexed fractionation method suitable for accurate quantification of the membrane proteome. This approach provided almost 2-fold enhanced detection of peptides encompassing transmembrane helix (TMH) domain, as compared with strong anion exchange (SAX) and strong cation exchange (SCX) StageTip techniques. Almost 5000 proteins (∼60% membrane proteins) can be identified in only 10 μg of membrane protein digests, showing the superior sensitivity of the Hp-RP StageTip approach. The method allowed up to 9- and 6-fold increase in the identification of unique hydrophobic and hydrophilic peptides, respectively. The Hp-RP StageTip method enabled in-depth membrane proteome profiling of 11 lung cancer cell lines harboring different EGFR mutation status, which resulted in the identification of 3983 annotated membrane proteins. This provides the largest collection of reference peptide spectral data for lung cancer membrane subproteome. Finally, relative quantification of membrane proteins between Gefitinib-resistant and -sensitive lung cancer cell lines revealed several up-regulated membrane proteins with key roles in lung cancer progression.