Streamlined single-cell proteomics by an integrated microfluidic chip and data-independent acquisition mass spectrometry

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.

Proteomic identification of proliferation and progression markers in human polycythemia vera stem and progenitor cells

Polycythemia vera (PV) is a stem cell disorder characterized by hyperproliferation of the myeloid lineages and the presence of an activating JAK2 mutation. To elucidate mechanisms controlling PV stem and progenitor cell biology, we applied a recently developed highly sensitive data-independent acquisition mass spectrometry workflow to purified hematopoietic stem and progenitor cell (HSPC) subpopulations of patients with chronic and progressed PV. We integrated proteomic data with genomic, transcriptomic, flow cytometry and in vitro colony formation data. Comparative analyses revealed added information gained by proteomic compared with transcriptomic data in 30% of proteins with changed expression in PV patients. Upregulated biological pathways in hematopoietic stem and multipotent progenitor cells (HSC/MPPs) of PV included MTOR, STAT and interferon signaling. We further identified a prominent reduction of clusterin (CLU) protein expression and a corresponding activation of NFĸB signaling in HSC/MPPs of untreated PV patients compared with controls. Reversing the reduction of CLU and inhibiting NFĸB signaling decreased proliferation and differentiation of PV HSC/MPPs in vitro. Upon progression of PV, we identified upregulation of LGALS9 and SOCS2 protein expression in HSC/MPPs. Treatment of patients with hydroxyurea normalized the expression of CLU and NFĸB2, but not of LGALS9 and SOCS2. These findings expand the current understanding of the molecular pathophysiology underlying PV and provide new potential targets (CLU and NFĸB) for antiproliferative therapy in PV patients.

Resolving Missing Protein Problems Using Functional Class Scoring

Despite technological advances in proteomics, incomplete coverage and inconsistency issues persist, resulting in “data holes”. These data holes cause the missing protein problem (MPP), where relevant proteins are persistently unobserved, or sporadically observed across samples. This hinders biomarker and drug discovery from proteomics data. Network-based approaches are powerful: The Functional Class Scoring (FCS) method using protein complexes was able to easily recover missed proteins with weak or partial support. However, there are limitations: The verification approach (in determining missing protein recovery) is potentially biased as the test data was based on relatively outdated Data-Dependent Acquisition (DDA) proteomics and FCS does not provide a scoring scheme for individual protein components (in significant complexes). To address these issues: First, we devised a more rigorous evaluation of FCS based on same-sample technical replicates. And second, we evaluate using data from more recent Data-Independent Acquisition (DIA) technologies (viz. SWATH).Although cross-replicate examination reveals some inconsistencies amongst same-class samples, tissue-differentiating signal is nonetheless strongly conserved. This confirms FCS as a viable method that selects biologically meaningful networks. We also report that predicted missing proteins are statistically significant based on FCS p-values. Although cross-replicate verification rates are not spectacular, the predicted missing proteins as a whole, have higher peptide support than non-predicted proteins. FCS also has the capacity to predict missing proteins that are often lost due to weak specific peptide support. As a yet unresolved limitation, we find that FCS cannot assign meaningful probabilities to individual protein components (no relationship between actual probability of verification and FCS-assigned probability) as it only provides a p-value at the level of complexes.

Gravida Serum POSTN and PAPPA as the Potential Biomarkers for Fetal Congenital Heart Disease Based on Quantitative and Qualitative Proteomics

Background: Fetal congenital heart disease is the most common congenital defect worldwide. It has some missed diagnoses and a lack of disease biomarkers. We aim to seek objective biomarkers for noninvasive prenatal diagnosis of fetal congenital heart disease to reduce the missed diagnosis and explore its mechanism,Methods: This study used data-independent acquisition and parallel reaction monitoring to explore potential protein biomarkers that co-exist in gravida serum and fetal amniotic fluid. Moreover, logistic regression and ROC curve to establish the diagnostic model of fetal congenital heart disease potential biomarkers and molecular biology experiments were performed to validate proteomics results and explore the mechanism. Results: Proteomics and bioinformatics results show that 12 proteins co-exist in gravida serum and amniotic fluid. We identified POSTN and PAPPA in GS as candidate biomarkers and established the diagnostic model with a sensitivity of 100%, a specificity of 95% and the AUC value of 0.968 to diagnose congenital heart disease. In addition, the results of ELISA, IHC, and RT-PCR were consistent with those of proteomics. Moreover, POSTN may involve in fetal congenital heart disease through the TGF-beta signaling pathway. Conclusion: It is the first time to find that POSTN and PAPPA in GS are related to fetal congenital heart disease, contributing to developing a novel noninvasive prenatal method to diagnose fetal congenital heart disease and reduce congenital disabilities.

Integrated transcriptomic and proteomic analysis of Tritipyrum provides insights into the molecular basis of salt tolerance

Background Soil salinity is a major environmental stress that restricts crop growth and yield. Methods Here, crucial proteins and biological pathways were investigated under salt-stress and recovery conditions in Tritipyrum ‘Y1805’ using the data-independent acquisition proteomics techniques to explore its salt-tolerance mechanism. Results In total, 44 and 102 differentially expressed proteins (DEPs) were identified in ‘Y1805’ under salt-stress and recovery conditions, respectively. A proteome-transcriptome-associated analysis revealed that the expression patterns of 13 and 25 DEPs were the same under salt-stress and recovery conditions, respectively. ‘Response to stimulus’, ‘antioxidant activity’, ‘carbohydrate metabolism’, ‘amino acid metabolism’, ‘signal transduction’, ‘transport and catabolism’ and ‘biosynthesis of other secondary metabolites’ were present under both conditions in ‘Y1805’. In addition, ‘energy metabolism’ and ‘lipid metabolism’ were recovery-specific pathways, while ‘antioxidant activity’, and ‘molecular function regulator’ under salt-stress conditions, and ‘virion’ and ‘virion part’ during recovery, were ‘Y1805’-specific compared with the salt-sensitive wheat ‘Chinese Spring’. ‘Y1805’ contained eight specific DEPs related to salt-stress responses. The strong salt tolerance of ‘Y1805’ could be attributed to the strengthened cell walls, reactive oxygen species scavenging, osmoregulation, phytohormone regulation, transient growth arrest, enhanced respiration, transcriptional regulation and error information processing. These data will facilitate an understanding of the molecular mechanisms of salt tolerance and aid in the breeding of salt-tolerant wheat.

Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

Background: Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Methods: We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. Results: We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Conclusions: Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

Integrated Proteomic and Metabolomic Analyses of the Mitochondrial Neurodegenerative Disease MELAS

MELAS (mitochondrial encephalomyopathy, lactic acidosis, stroke-like episodes) is a progressive neurodegenerative disease caused by pathogenic mitochondrial DNA variants. The pathogenic mechanism of MELAS remains enigmatic due to the exceptional clinical heterogeneity and the obscure genotype-phenotype correlation among MELAS patients. To gain insights into the pathogenic signature of MELAS, we designed a comprehensive strategy integrating proteomics and metabolomics in patient-derived dermal fibroblasts harboring the ultra-rare MELAS pathogenic variant m.14453G>A, specifically affecting the mitochondrial respiratory Complex I. Global proteomics was achieved by data-dependent acquisition (DDA) and verified by data-independent acquisition (DIA) using both Spectronaut and the recently launched MaxDIA platforms. Comprehensive metabolite coverage was achieved for both polar and nonpolar metabolites in both reverse phase and HILIC LC-MS/MS analyses. Our proof-of-principle MELAS study with multi-omics integration revealed OXPHOS dysregulation with a predominant deficiency of Complex I subunits, as well as alterations in key bioenergetic pathways, glycolysis, tricarboxylic acid cycle, and fatty acid β-oxidation. The most clinically relevant discovery is the downregulation of the arginine biosynthesis pathway, likely due to blocked argininosuccinate synthase, which is congruent with the MELAS cardinal symptom of stroke-like episodes and its current treatment by arginine infusion. In conclusion, we demonstrated an integrated proteomic and metabolomic strategy for patient-derived fibroblasts, which has great clinical potential to discover therapeutic targets and design personalized interventions after validation with a larger patient cohort in the future.