[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.

Fully Integrated Online Strategy for Highly Sensitive Proteome Profiling.

Low-input proteomics, which treats tens to hundreds of mammalian cells, is the gap between standard proteomics and single-cell proteomics. Low-input proteomics is widely applicable and needs special sample preparation methods to achieve deep proteome profiling. This chapter describes protocols for the preparation and application of an easy-to-use and scalable device for processing low-input samples. Protein preconcentration, impurity removal, reduction, alkylation, digestion, and desalting are fully integrated into this workflow, and the device can be directly connected to online nanoLC-MS to avoid sample transfer.

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.

High-throughput drug target discovery using a fully automated proteomics sample preparation platform.

Drug development is plagued by inefficiency and high costs due to issues such as inadequate drug efficacy and unexpected toxicity. Mass spectrometry (MS)-based proteomics, particularly isobaric quantitative proteomics, offers a solution to unveil resistance mechanisms and unforeseen side effects related to off-targeting pathways. Thermal proteome profiling (TPP) has gained popularity for drug target identification at the proteome scale. However, it involves experiments with multiple temperature points, resulting in numerous samples and considerable variability in large-scale TPP analysis. We propose a high-throughput drug target discovery workflow that integrates single-temperature TPP, a fully automated proteomics sample preparation platform (autoSISPROT), and data independent acquisition (DIA) quantification. The autoSISPROT platform enables the simultaneous processing of 96 samples in less than 2.5 hours, achieving protein digestion, desalting, and optional TMT labeling (requires an additional 1 hour) with 96-channel all-in-tip operations. The results demonstrated excellent sample preparation performance with >94% digestion efficiency, >98% TMT labeling efficiency, and >0.9 intra- and inter-batch Pearson correlation coefficients. By automatically processing 87 samples, we identified both known targets and potential off-targets of 20 kinase inhibitors, affording over a 10-fold improvement in throughput compared to classical TPP. This fully automated workflow offers a high-throughput solution for proteomics sample preparation and drug target/off-target identification.

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.

DeKinomics pulse-chases kinase functions in living cells.

Cellular context is crucial for understanding the complex and dynamic kinase functions in health and disease. Systematic dissection of kinase-mediated cellular processes requires rapid and precise stimulation ('pulse') of a kinase of interest, as well as global and in-depth characterization ('chase') of the perturbed proteome under living conditions. Here we developed an optogenetic 'pulse-chase' strategy, termed decaging kinase coupled proteomics (DeKinomics), for proteome-wide profiling of kinase-driven phosphorylation at second-timescale in living cells. We took advantage of the 'gain-of-function' feature of DeKinomics to identify direct kinase substrates and further portrayed the global phosphorylation of understudied receptor tyrosine kinases under native cellular settings. DeKinomics offered a general activation-based strategy to study kinase functions with high specificity and temporal resolution under living conditions.

Improved in situ characterization of protein complex dynamics at scale with thermal proximity co-aggregation.

Cellular activities are carried out vastly by protein complexes but large repertoire of protein complexes remains functionally uncharacterized which necessitate new strategies to delineate their roles in various cellular processes and diseases. Thermal proximity co-aggregation (TPCA) is readily deployable to characterize protein complex dynamics in situ and at scale. We develop a version termed Slim-TPCA that uses fewer temperatures increasing throughputs by over 3X, with new scoring metrics and statistical evaluation that result in minimal compromise in coverage and detect more relevant complexes. Less samples are needed, batch effects are minimized while statistical evaluation cost is reduced by two orders of magnitude. We applied Slim-TPCA to profile K562 cells under different duration of glucose deprivation. More protein complexes are found dissociated, in accordance with the expected downregulation of most cellular activities, that include 55S ribosome and respiratory complexes in mitochondria revealing the utility of TPCA to study protein complexes in organelles. Protein complexes in protein transport and degradation are found increasingly assembled unveiling their involvement in metabolic reprogramming during glucose deprivation. In summary, Slim-TPCA is an efficient strategy for characterization of protein complexes at scale across cellular conditions, and is available as Python package at https://pypi.org/project/Slim-TPCA/ .

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.

Scaled-Down Thermal Profiling and Coaggregation Analysis of the Proteome for Drug Target and Protein Interaction Analysis.

Thermal proteome profiling (TPP), an experimental technique combining the cellular thermal shift assay (CETSA) with quantitative protein mass spectrometry (MS), identifies interactions of drugs and chemicals with endogenous proteins. Thermal proximity coaggregation (TPCA) profiling extended TPP to study the intracellular dynamics of protein complexes. In TPP and TPCA, samples are subjected to multiple denaturing temperatures, each requiring over 100 µg of proteins, which restricts their applications for rare cells and precious clinical samples. We developed a workflow termed STASIS (scaled-down thermal profiling and coaggregation analysis with SISPROT) that scales down the required protein to as low as 1 µg per temperature. This is achieved by heating and centrifugation using the same PCR tube, processing samples with the SISPROT technology (simple and integrated spintip-based proteomics technology), and tip-based manual fractionation of TMT-labeled peptides. We evaluate the STASIS workflow with starting protein quantities of 10, 5, and 1 µg per temperature prior to heating, identifying between 4000 and 5000 proteins with 6 h of acquisition time. Importantly, we observed a high correlation in the Tm of proteins with minimal difference in TPCA performance for predicting protein complexes. Moreover, STASIS could identify the targets of methotrexate and panobinostat with high precision with 1 µg of proteins per temperature. In conclusion, STASIS is a robust cost-effective technique for target deconvolution and extended TPCA to rare primary cells and precious clinical samples for the analysis of protein complexes.

N-linked glycoproteins and host proteases are involved in swine acute diarrhea syndrome coronavirus entry.

IMPORTANCE: Gaining insight into the cell-entry mechanisms of swine acute diarrhea syndrome coronavirus (SADS-CoV) is critical for investigating potential cross-species infections. Here, we demonstrated that pretreatment of host cells with tunicamycin decreased SADS-CoV attachment efficiency, indicating that N-linked glycosylation of host cells was involved in SADS-CoV entry. Common N-linked sugars Neu5Gc and Neu5Ac did not interact with the SADS-CoV S1 protein, suggesting that these molecules were not involved in SADS-CoV entry. Additionally, various host proteases participated in SADS-CoV entry into diverse cells with different efficiencies. Our findings suggested that SADS-CoV may exploit multiple pathways to enter cells, providing insights into intervention strategies targeting the cell entry of this virus.

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.

Target deconvolution with matrix-augmented pooling strategy reveals cell-specific drug-protein interactions.

Target deconvolution is a crucial but costly and time-consuming task that hinders large-scale profiling for drug discovery. We present a matrix-augmented pooling strategy (MAPS) which mixes multiple drugs into samples with optimized permutation and delineates targets of each drug simultaneously with mathematical processing. We validated this strategy with thermal proteome profiling (TPP) testing of 15 drugs concurrently, increasing experimental throughput by 60x while maintaining high sensitivity and specificity. Benefiting from the lower cost and higher throughput of MAPS, we performed target deconvolution of the 15 drugs across 5 cell lines. Our profiling revealed that drug-target interactions can differ vastly in targets and binding affinity across cell lines. We further validated BRAF and CSNK2A2 as potential off-targets of bafetinib and abemaciclib, respectively. This work represents the largest thermal profiling of structurally diverse drugs across multiple cell lines to date.

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.

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.

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.

Integrated mass spectrometry strategy for functional protein complex discovery and structural characterization.

The discovery of functional protein complex and the interrogation of the complex structure-function relationship (SFR) play crucial roles in the understanding and intervention of biological processes. Affinity purification-mass spectrometry (AP-MS) has been proved as a powerful tool in the discovery of protein complexes. However, validation of these novel protein complexes as well as elucidation of their molecular interaction mechanisms are still challenging. Recently, native top-down MS (nTDMS) is rapidly developed for the structural analysis of protein complexes. In this review, we discuss the integration of AP-MS and nTDMS in the discovery and structural characterization of functional protein complexes. Further, we think the emerging artificial intelligence (AI)-based protein structure prediction is highly complementary to nTDMS and can promote each other. We expect the hybridization of integrated structural MS with AI prediction to be a powerful workflow in the discovery and SFR investigation of functional protein complexes.

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.

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.