[A highly sensitive approach for the analysis of tyrosine phosphoproteome in primary T cells].

Tyrosine phosphorylation, a common post-translational modification process for proteins, is involved in a variety of biological processes. However, the abundance of tyrosine-phosphorylated proteins is very low, making their identification by mass spectrometry (MS) is difficult; thus, milligrams of the starting material are often required for their enrichment. For example, tyrosine phosphorylation plays an important role in T cell signal transduction. However, the number of primary T cells derived from biological tissue samples is very small, and these cells are difficult to culture and expand; thus, the study of T cell signal transduction is usually carried out on immortalized cell lines, which can be greatly expanded. However, the data from immortalized cell lines cannot fully mimic the signal transduction processes observed in the real physiological state, and they usually lead to conclusions that are quite different from those of primary T cells. Therefore, a highly sensitive proteomic method was developed for studying tyrosine phosphorylation modification signals in primary T cells. To address the issue of the limited T cells numbers, a comprehensive protocol was first optimized for the isolation, activation, and expansion of primary T cells from mouse spleen. CD3+ primary T cells were successfully sorted; more than 91% of the T cells collected were well activated on day 2, and the number of T cells expanded to over 7-fold on day 4. Next, to address the low abundance of tyrosine-phosphorylated proteins, we used SH2-superbinder affinity enrichment and immobilized Ti4+affinity chromatography (Ti4+-IMAC) to enrich the tyrosine-phosphorylated polypeptides of primary T cells that were co-stimulated with anti-CD3 and anti-CD28. These polypeptides were resolved using nanoscale liquid chromatography-tandem mass spectrometry (nanoLC-MS/MS). Finally, 282 tyrosine phosphorylation sites were successfully identified in 1 mg of protein, including many tyrosine phosphorylation sites on the immunoreceptor tyrosine-based activation motif (ITAM) in the intracellular region of the T cell receptor membrane protein CD3, as well as the phosphotyrosine sites of ZAP70, LAT, VAV1, and other proteins related to signal transduction under costimulatory conditions. In summary, to solve the technical problems of the limited number of primary cells, low abundance of tyrosine-phosphorylated proteins, and difficulty of detection by MS, we developed a comprehensive proteomic method for the in-depth analysis of tyrosine phosphorylation modification signals in primary T cells. This protocol may be applied to map signal transduction networks that are closely related to physiological states.

Spatial Proteomic Profiling of Colorectal Cancer Revealed Its Tumor Microenvironment Heterogeneity.

Colorectal cancer is a predominant malignancy with a second mortality worldwide. Despite its prevalence, therapeutic options remain constrained and surgical operation is still the most useful therapy. In this regard, a comprehensive spatially resolved quantitative proteome atlas was constructed to explore the functional proteomic landscape of colorectal cancer. This strategy integrates histopathological analysis, laser capture microdissection, and proteomics. Spatial proteome profiling of 200 tissue section samples facilitated by the fully integrated sample preparation technology SISPROT enabled the identification of more than 4000 proteins on the Orbitrap Exploris 240 from 2 mm2 × 10 µm tissue sections. Compared with normal adjacent tissues, we identified a spectrum of cancer-associated proteins and dysregulated pathways across various regions of colorectal cancer including ascending colon, transverse colon, descending colon, sigmoid colon, and rectum. Additionally, we conducted proteomic analysis on tumoral epithelial cells and paracancerous epithelium from early to advanced stages in hallmark rectum cancer and sigmoid colon cancer. Bioinformatics analysis revealed functional proteins and cell-type signatures associated with different regions of colorectal tumors, suggesting potential clinical implications. Overall, this study provides a comprehensive spatially resolved functional proteome landscape of colorectal cancer, serving as a valuable resource for exploring potential biomarkers and therapeutic targets.

Plasma proteomic characterization of colorectal cancer patients with FOLFOX chemotherapy by integrated proteomics technology.

BACKGROUND: Colorectal Cancer (CRC) is a prevalent form of cancer, and the effectiveness of the main postoperative chemotherapy treatment, FOLFOX, varies among patients. In this study, we aimed to identify potential biomarkers for predicting the prognosis of CRC patients treated with FOLFOX through plasma proteomic characterization. METHODS: Using a fully integrated sample preparation technology SISPROT-based proteomics workflow, we achieved deep proteome coverage and trained a machine learning model from a discovery cohort of 90 CRC patients to differentiate FOLFOX-sensitive and FOLFOX-resistant patients. The model was then validated by targeted proteomics on an independent test cohort of 26 patients. RESULTS: We achieved deep proteome coverage of 831 protein groups in total and 536 protein groups in average for non-depleted plasma from CRC patients by using a Orbitrap Exploris 240 with moderate sensitivity. Our results revealed distinct molecular changes in FOLFOX-sensitive and FOLFOX-resistant patients. We confidently identified known prognostic biomarkers for colorectal cancer, such as S100A4, LGALS1, and FABP5. The classifier based on the biomarker panel demonstrated a promised AUC value of 0.908 with 93% accuracy. Additionally, we established a protein panel to predict FOLFOX effectiveness, and several proteins within the panel were validated using targeted proteomic methods. CONCLUSIONS: Our study sheds light on the pathways affected in CRC patients treated with FOLFOX chemotherapy and identifies potential biomarkers that could be valuable for prognosis prediction. Our findings showed the potential of mass spectrometry-based proteomics and machine learning as an unbiased and systematic approach for discovering biomarkers in CRC.

Deep spatial proteomics reveals region-specific features of severe COVID-19-related pulmonary injury.

As a primary target of severe acute respiratory syndrome coronavirus 2, lung exhibits heterogeneous histopathological changes following infection. However, comprehensive insight into their protein basis with spatial resolution remains deficient, which hinders further understanding of coronavirus disease 2019 (COVID-19)-related pulmonary injury. Here, we generate a region-resolved proteomic atlas of hallmark pathological pulmonary structures by integrating histological examination, laser microdissection, and ultrasensitive proteomics. Over 10,000 proteins are quantified across 71 post-mortem specimens. We identify a spectrum of pathway dysregulations in alveolar epithelium, bronchial epithelium, and blood vessels compared with non-COVID-19 controls, providing evidence for transitional-state pneumocyte hyperplasia. Additionally, our data reveal the region-specific enrichment of functional markers in bronchiole mucus plugs, pulmonary fibrosis, airspace inflammation, and alveolar type 2 cells, uncovering their distinctive features. Furthermore, we detect increased protein expression associated with viral entry and inflammatory response across multiple regions, suggesting potential therapeutic targets. Collectively, this study provides a distinct perspective for deciphering COVID-19-caused pulmonary dysfunction by spatial proteomics.

Chemical Proteomic Approach for In-Depth Glycosylation Profiling of Plasma Carcinoembryonic Antigen in Cancer Patients.

Carcinoembryonic antigen (CEA) of human plasma is a biomarker of many cancer diseases, and its N-glycosylation accounts for 60% of molecular mass. It is highly desirable to characterize its glycoforms for providing additional dimension of features to increase its performance in prognosis and diagnosis of cancers. However, to systematically characterize its site-specific glycosylation is challenging because of its low abundance. Here, we developed a highly sensitive strategy for in-depth glycosylation profiling of plasma CEA through chemical proteomics combined with multienzymatic digestion. A trifunctional probe was utilized to generate covalent bond of plasma CEA and its antibody upon UV irradiation. As low as 1 ng/ml CEA in plasma could be captured and digested with trypsin and chymotrypsin for intact glycopeptide characterization. Twenty six of 28 potential N-glycosylation sites were well identified, which were the most comprehensive N-glycosylation site characterization of CEA on intact glycopeptide level as far as we known. Importantly, this strategy was applied to the glycosylation analysis of plasma CEA in cancer patients. Differential site-specific glycoforms of plasma CEA were observed in patients with colorectal cancers (CRCs) and lung cancer. The distributions of site-specific glycoforms were different as the progression of CRC, and most site-specific glycoforms were overexpressed in stage II of CRC. Overall, we established a highly sensitive chemical proteomic method to profile site-specific glycosylation of plasma CEA, which should generally applicable to other well-established cancer glycoprotein biomarkers for improving their cancer diagnosis and monitoring performance.

Kindlin-2 enhances c-Myc translation through association with DDX3X to promote pancreatic ductal adenocarcinoma progression.

Rationale: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive solid tumor, with extremely low survival rates. Identifying key signaling pathways driving PDAC progression is crucial for the development of therapies to improve patient response rates. Kindlin-2, a multi-functional protein, is involved in numerous biological processes including cell proliferation, apoptosis and migration. However, little is known about the functions of Kindlin-2 in pancreatic cancer progression in vivo. Methods: In this study, we employ an in vivo PDAC mouse model to directly investigate the role of Kindlin-2 in PDAC progression. Then, we utilized RNA-sequencing, the molecular and cellular assays to determine the molecular mechanisms by which Kindlin-2 promotes PDAC progression. Results: We show that loss of Kindlin-2 markedly inhibits KrasG12D-driven pancreatic cancer progression in vivo as well as in vitro. Furthermore, we provide new mechanistic insight into how Kindlin-2 functions in this process, A fraction of Kindlin-2 was localized to the endoplasmic reticulum and associated with the RNA helicase DDX3X, a key regulator of mRNA translation. Loss of Kindlin-2 blocked DDX3X from binding to the 5'-untranslated region of c-Myc and inhibited DDX3X-mediated c-Myc translation, leading to reduced c-Myc-mediated glucose metabolism and tumor growth. Importantly, restoration of the expression of either the full-length Kindlin-2 or c-Myc, but not that of a DDX3X-binding-defective mutant of Kindlin-2, in Kindlin-2 deficient PDAC cells, reversed the inhibition of glycolysis and pancreatic cancer progression induced by the loss of Kindlin-2. Conclusion: Our studies reveal a novel Kindlin-2-DDX3X-c-Myc signaling axis in PDAC progression and suggest that inhibition of this signaling axis may provide a promising therapeutic approach to alleviate PDAC progression.

Deciphering intercellular signaling complexes by interaction-guided chemical proteomics.

Indirect cell-cell interactions mediated by secreted proteins and their plasma membrane receptors play essential roles for regulating intercellular signaling. However, systematic profiling of the interactions between living cell surface receptors and secretome from neighboring cells remains challenging. Here we develop a chemical proteomics approach, termed interaction-guided crosslinking (IGC), to identify ligand-receptor interactions in situ. By introducing glycan-based ligation and click chemistry, the IGC approach via glycan-to-glycan crosslinking successfully captures receptors from as few as 0.1 million living cells using only 10 ng of secreted ligand. The unparalleled sensitivity and selectivity allow systematic crosslinking and identification of ligand-receptor complexes formed between cell secretome and surfaceome in an unbiased and all-to-all manner, leading to the discovery of a ligand-receptor interaction between pancreatic cancer cell-secreted urokinase (PLAU) and neuropilin 1 (NRP1) on pancreatic cancer-associated fibroblasts. This approach is thus useful for systematic exploring new ligand-receptor pairs and discovering critical intercellular signaling events.

Comprehensive Evaluation and Optimization of the Data-Dependent LC-MS/MS Workflow for Deep Proteome Profiling.

Data-dependent liquid chromatography-tandem mass spectrometry (LC-MS/MS) is widely used in proteomic analyses. A well-performed LC-MS/MS workflow, which involves multiple procedures and interdependent metrics, is a prerequisite for deep proteome profiling. Researchers have previously evaluated LC-MS/MS performance mainly based on the number of identified peptides and proteins. However, this is not a comprehensive approach. This motivates us to develop MSRefine, which aims to evaluate and optimize the performance of the LC-MS/MS workflow for data-dependent acquisition (DDA) proteomics. It extracts 47 kinds of metrics, scores the metrics, and reports visual results, assisting users in evaluating the workflow, locating problems, and providing optimizing strategies. In this study, we compared and analyzed multiple pairs of datasets spanning different samples, methods, and instruments and demonstrated that the comprehensive visual metrics and scores in MSRefine enable us to evaluate the performance of the various experiments and provide optimal strategies for the identification of more peptides and proteins.

Functional and Clinical Proteomic Exploration of Pancreatic Cancer.

Pancreatic cancer, in most cases being pancreatic ductal adenocarcinoma (PDAC), is one of the most lethal cancers with a median survival time of less than 6 months. Therapeutic options are very limited for patients with PDAC, and surgery is still the most effective treatment, making improvements in early diagnosis critical. One typical characteristic of PDAC is the desmoplastic reaction of its stroma microenvironment, which actively interacts with cancer cells to orchestrate key components in tumorigenesis, metastasis, and chemoresistance. A global exploration of cancer-stroma crosstalk is essential to decipher PDAC biology and design intervention strategies. Over the past decade, the dramatic improvement in proteomics technologies has enabled the profiling of proteins, post-translational modifications (PTMs), and their protein complexes at unprecedented sensitivity and dimensionality. Here, starting with our current understanding of PDAC characteristics, including precursor lesions, progression models, tumor microenvironment, and therapeutic advancements, we describe how proteomics contributes to the functional and clinical exploration of PDAC, providing insights into PDAC carcinogenesis, progression, and chemoresistance. We summarize recent achievements enabled by proteomics to systematically investigate PTMs-mediated intracellular signaling in PDAC, cancer-stroma interactions, and potential therapeutic targets revealed by these functional studies. We also highlight proteomic profiling of clinical tissue and plasma samples to discover and verify useful biomarkers that can aid early detection and molecular classification of patients. In addition, we introduce spatial proteomic technology and its applications in PDAC for deconvolving tumor heterogeneity. Finally, we discuss future prospects of applying new proteomic technologies in comprehensively understanding PDAC heterogeneity and intercellular signaling networks. Importantly, we expect advances in clinical functional proteomics for exploring mechanisms of cancer biology directly by high-sensitivity functional proteomic approaches starting from clinical samples.

Combination of automated sample preparation and micro-flow LC-MS for high-throughput plasma proteomics.

BACKGROUND: Non-invasive detection of blood-based markers is a critical clinical need. Plasma has become the main sample type for clinical proteomics research because it is easy to obtain and contains measurable protein biomarkers that can reveal disease-related physiological and pathological changes. Many efforts have been made to improve the depth of its identification, while there is an increasing need to improve the throughput and reproducibility of plasma proteomics analysis in order to adapt to the clinical large-scale sample analysis. METHODS: We have developed and optimized a robust plasma analysis workflow that combines an automated sample preparation platform with a micro-flow LC-MS-based detection method. The stability and reproducibility of the workflow were systematically evaluated and the workflow was applied to a proof-of-concept plasma proteome study of 30 colon cancer patients from three age groups. RESULTS: This workflow can analyze dozens of samples simultaneously with high reproducibility. Without protein depletion and prefractionation, more than 300 protein groups can be identified in a single analysis with micro-flow LC-MS system on a Orbitrap Exploris 240 mass spectrometer, including quantification of 35 FDA approved disease markers. The quantitative precision of the entire workflow was acceptable with median CV of 9%. The preliminary proteomic analysis of colon cancer plasma from different age groups could be well separated with identification of potential colon cancer-related biomarkers. CONCLUSIONS: This workflow is suitable for the analysis of large-scale clinical plasma samples with its simple and time-saving operation, and the results demonstrate the feasibility of discovering significantly changed plasma proteins and distinguishing different patient groups.

Pixel-Level Classification of Five Histologic Patterns of Lung Adenocarcinoma.

Lung adenocarcinoma is the most common histologic type of lung cancer. The pixel-level labeling of histologic patterns of lung adenocarcinoma can assist pathologists in determining tumor grading with more details than normal classification. We manually annotated a dataset containing a total of 1000 patches (200 patches for each pattern) of 512 × 512 pixels and 420 patches (contains test sets) of 1024 × 1024 pixels according to the morphological features of the five histologic patterns of lung adenocarcinoma (lepidic, acinar, papillary, micropapillary, and solid). To generate an even large amount of data patches, we developed a data stitching strategy as a data augmentation for classification in model training. Stitched patches improve the Dice similarity coefficient (DSC) scores by 24.06% on the whole-slide image (WSI) with the solid pattern. We propose a WSI analysis framework for lung adenocarcinoma pathology, intelligently labeling lung adenocarcinoma histologic patterns at the pixel level. Our framework contains five branches of deep neural networks for segmenting each histologic pattern. We test our framework with 200 unclassified patches. The DSC scores of our results outpace comparing networks (U-Net, LinkNet, and FPN) by up to 10.78%. We also perform results on four WSIs with an overall accuracy of 99.6%, demonstrating that our network framework exhibits better accuracy and robustness in most cases.

Phase separation of SGS3 drives siRNA body formation and promotes endogenous gene silencing.

The generation of small interfering RNA (siRNA) involves many RNA processing components, including SUPPRESSOR OF GENE SILENCING 3 (SGS3), RNA-DEPENDENT RNA POLYMERASE 6 (RDR6), and DICER-LIKE proteins (DCLs). Nonetheless, how these components are coordinated to produce siRNAs is unclear. Here, we show that SGS3 forms condensates via phase separation in vivo and in vitro. SGS3 interacts with RDR6 and drives it to form siRNA bodies in cytoplasm, which is promoted by SGS3-targeted RNAs. Disrupting SGS3 phase separation abrogates siRNA body assembly and siRNA biogenesis, whereas coexpression of SGS3 and RDR6 induces siRNA body formation in tobacco and yeast cells. Dysfunction in translation and mRNA decay increases the number of siRNA bodies, whereas DCL2/4 mutations enhance their size. Purification of SGS3 condensates identifies numerous RNA-binding proteins and siRNA processing components. Together, our findings reveal that SGS3 phase separation-mediated formation of siRNA bodies is essential for siRNA production and gene silencing.

Dynamic Phosphotyrosine-Dependent Signaling Profiling in Living Cells by Two-Dimensional Proximity Proteomics.

Tyrosine phosphorylation (pTyr)-dependent signaling pathways play a vital role in various biological processes, which are spatiotemporally assembled and dynamically regulated on a minute scale by pTyr in living cells. Studying these pTyr-mediated signaling complexes is therefore challenging due to the highly dynamic nature of the protein complexes and the low abundance of pTyr. In this study, we adopted minute-resolution APEX2-based proximity labeling (PL) in living cells and Src SH2 superbinder-based pTyr peptide enrichment for simultaneously profiling these protein complexes and associated pTyr sites from the same affinity-purified sample. Upon different time courses of EGF stimulation of the living cells stably expressing APEX2-FLAG-GRB2, we constructed two-dimensional time-course curves for both interactome and tyrosine phosphoproteome. Well-annotated pTyr signaling complexes in EGFR signaling and located at the endosome were quantified with tightly correlated time-course curves for both interacting proteins and pTyr sites. Importantly, the correlated time-course curves for EGFR and endosomal HGS were well validated by targeted-parallel reaction monitoring (PRM)-MS analysis. Taking advantage of the high sensitivity of the PRM assay, the low-abundant pTyr peptide EGFR pY1110, which cannot be identified in the data-dependent acquisition (DDA) analysis, could be well quantified. Collectively, this two-dimensional proximity proteomic strategy is promising for comprehensively characterizing pTyr-mediated protein complexes with high sensitivity in living cells.

Integrated and High-Throughput Approach for Sensitive Analysis of Tyrosine Phosphoproteome.

Tyrosine phosphorylation (pTyr) regulates various signaling pathways under normal and cancerous states. Due to their low abundance and transient and dynamic natures, systematic profiling of pTyr sites is challenging. Antibody and engineered binding domain-based approaches have been well applied to pTyr peptide enrichment. However, traditional methods have the disadvantage of a long sample preparation process, which makes them unsuitable for processing limited amount of samples, especially in a high-throughput manner. In this study we developed a 96-well microplate-based approach to integrate all the sample preparation steps starting from cell culture to MS-compatible pTyr peptide enrichment in three consecutive 96-well microplates. By assembling an engineered SH2 domain onto a microplate, nonspecific adsorption of phosphopeptides is greatly reduced, which allows us to remove the Ti-IMAC purification and three C18 desalting steps (after digestion, pTyr enrichment, and Ti-IMAC purification) and, therefore, greatly simplifies the entire pTyr peptide enrichment workflow, especially when processing a large number of samples. Starting with 96-well microplate-cultured, pervanadate-stimulated cells, our approach could enrich 21% more pTyr sites than the traditional serial pTyr enrichment approach and showed good sensitivity and reproducibility in the range of 200 ng to 200 µg peptides. Importantly, we applied this approach to profile tyrosine kinase inhibitor-mediated EGFR signaling pathway and could well differentiate the distinct response of different pTyr sites. Collectively, the integrated 96-well microplate-based approach is valuable for profiling pTyr sites from limited biological samples and in a high-throughput manner.

The SWI/SNF chromatin remodeling factor DPF3 regulates metastasis of ccRCC by modulating TGF-ß signaling.

DPF3, a component of the SWI/SNF chromatin remodeling complex, has been associated with clear cell renal cell carcinoma (ccRCC) in a genome-wide association study. However, the functional role of DPF3 in ccRCC development and progression remains unknown. In this study, we demonstrate that DPF3a, the short isoform of DPF3, promotes kidney cancer cell migration both in vitro and in vivo, consistent with the clinical observation that DPF3a is significantly upregulated in ccRCC patients with metastases. Mechanistically, DPF3a specifically interacts with SNIP1, via which it forms a complex with SMAD4 and p300 histone acetyltransferase (HAT), the major transcriptional regulators of TGF-ß signaling pathway. Moreover, the binding of DPF3a releases the repressive effect of SNIP1 on p300 HAT activity, leading to the increase in local histone acetylation and the activation of cell movement related genes. Overall, our findings reveal a metastasis-promoting function of DPF3, and further establish the link between SWI/SNF components and ccRCC.

Kindlin-2 promotes Src-mediated tyrosine phosphorylation of androgen receptor and contributes to breast cancer progression.

Androgen receptor (AR) signaling plays important roles in breast cancer progression. We show here that Kindlin-2, a focal adhesion protein, is critically involved in the promotion of AR signaling and breast cancer progression. Kindlin-2 physically associates with AR and Src through its two neighboring domains, namely F1 and F0 domains, resulting in formation of a Kindlin-2-AR-Src supramolecular complex and consequently facilitating Src-mediated AR Tyr-534 phosphorylation and signaling. Depletion of Kindlin-2 was sufficient to suppress Src-mediated AR Tyr-534 phosphorylation and signaling, resulting in diminished breast cancer cell proliferation and migration. Re-expression of wild-type Kindlin-2, but not AR-binding-defective or Src-binding-defective mutant forms of Kindlin-2, in Kindlin-2-deficient cells restored AR Tyr-534 phosphorylation, signaling, breast cancer cell proliferation and migration. Furthermore, re-introduction of phosphor-mimic mutant AR-Y534D, but not wild-type AR reversed Kindlin-2 deficiency-induced inhibition of AR signaling and breast cancer progression. Finally, using a genetic knockout strategy, we show that ablation of Kindlin-2 from mammary tumors in mouse significantly reduced AR Tyr-534 phosphorylation, breast tumor progression and metastasis in vivo. Our results suggest a critical role of Kindlin-2 in promoting breast cancer progression and shed light on the molecular mechanism through which it functions in this process.

Selective inhibition reveals the regulatory function of DYRK2 in protein synthesis and calcium entry.

The dual-specificity tyrosine phosphorylation-regulated kinase DYRK2 has emerged as a critical regulator of cellular processes. We took a chemical biology approach to gain further insights into its function. We developed C17, a potent small-molecule DYRK2 inhibitor, through multiple rounds of structure-based optimization guided by several co-crystallized structures. C17 displayed an effect on DYRK2 at a single-digit nanomolar IC50 and showed outstanding selectivity for the human kinome containing 467 other human kinases. Using C17 as a chemical probe, we further performed quantitative phosphoproteomic assays and identified several novel DYRK2 targets, including eukaryotic translation initiation factor 4E-binding protein 1 (4E-BP1) and stromal interaction molecule 1 (STIM1). DYRK2 phosphorylated 4E-BP1 at multiple sites, and the combined treatment of C17 with AKT and MEK inhibitors showed synergistic 4E-BP1 phosphorylation suppression. The phosphorylation of STIM1 by DYRK2 substantially increased the interaction of STIM1 with the ORAI1 channel, and C17 impeded the store-operated calcium entry process. These studies collectively further expand our understanding of DYRK2 and provide a valuable tool to pinpoint its biological function.

Generic Plug-and-Play Strategy for High-Throughput Analysis of PTM-Mediated Protein Complexes.

Protein complexes mediated by various post-translational modifications (PTMs) play important roles in almost every aspect of biological processes. PTM-mediated protein complexes often have weak and transient binding properties, which limit their unbiased profiling especially in complex biological samples. Here, we developed a plug-and-play chemical proteomic approach for high-throughput analyis of PTM-mediated protein complexes. Taking advantage of the glutathione-S-transferase (GST) tag, which is the gold standard for protein purification and has wide access to a variety of proteins of interest (POIs), a glutathione (GSH) group- and photo-cross-linking group-containing trifunctional chemical probe was developed to tag POIs and assembled onto a streptavidin-coated 96-well plate for affinity purification, photo-cross-linking, and proteomics sample preparation in a fully integrated manner. Compared with the previously developed photo-pTyr-scaffold strategy, by assembling the tyrosine phosphorylation (pTyr) binding domain through covalent NHS chemistry, the new plug-and-play strategy using a noncovalent GST-GSH interaction has comparable enrichment efficiency for EGF stimulation-dependent pTyr protein complexes. To further prove its feasibility, we additionally assembled four pTyr-binding domains in the 96-well plate and selectively identified their pTyr-dependent interacting proteins. Importantly, we systematically optimized and applied the plug-and-play approach for exploring protein methylation-mediated protein complexes, which are difficult to be characterized due to their weak binding affinity and the lack of efficient enrichment strategies. We explored a comprehensive protein methylation-mediated interaction network assembled by five protein methylation binding domains including the chromo domain of MPP8, tandem tudor domain of KDM4A, full-length CBX1, PHD domain of RAG2, and tandem tudor domain of TP53BP1 and validated the chromo domain- and tudor domain-mediated interaction with histone H3. Collectively, this plug-and-play approach provides a convenient and generic strategy for exploring PTM-dependent protein complexes for any POIs with the GST tag.

Fritted tip capillary column with negligible dead volume facilitated ultrasensitive and deep proteomics.

Insufficient chromatographic performance results in reduced utilization of MS/MS scan capacity of advanced MS instruments. Improvement in peptide separation in liquid chromatography is critical for increasing the sensitivity and quantification performance of LC-MS-based proteomics. However, existing column fabrication methods suffer from slow packing, large dead volume, and band broadening. Herein, we reported that directly pulling emitter tips within short frits after fast packing (termed "filled tip") can minimize the dead volume, improving ionization efficiency and reducing band broadening. Within 10 min, our method can pack over 10 cm for 50 µm I.D. capillary columns under 6-8 MPa and over 50 cm for 75 µm I.D. long capillary columns under 70 MPa. We can identify an average of 3043 protein groups and 33 309 peptide-spectrum matches (PSMs) from 1 ng of HeLa digest using a 50 µm I.D. x 20 cm "filled tip" column, with good reproducibility. The number of protein groups increased by 50% and 96% when compared with a 50 µm I.D. "void tip" column and a 100 µm I.D. column with a manually pulled tip, respectively. We identified an average of 5534 protein groups and 71 769 PSMs from 10 ng of HeLa digest. In addition, using 75 µm I.D. x 50 cm "filled tip" columns, we can identify on average 8829 protein groups and 170 751 PSMs in single-shot data-dependent acquisition analysis from 500 ng of 293T digested peptides. Importantly, good repeatability and reproducibility of "filled tip" method were verified by results from columns fabricated in three batches and by different persons. When compared with conventional columns with "void tips", "filled tip" columns reduced median full peak widths by 19% and alleviated sampling redundancy by 10%. Collectively, we developed an easy-to-use, versatile and robust column fabrication method for both narrow-bore and long capillary columns, which achieved great sensitivity and depth in proteomic analysis.