Standard Flow Multiplexed Proteomics (SFloMPro)—An Accessible Alternative to NanoFlow Based Shotgun Proteomics

Multiplexed proteomics using isobaric tagging allows for simultaneously comparing the proteomes of multiple samples. In this technique, digested peptides from each sample are labeled with a chemical tag prior to pooling sample for LC-MS/MS with nanoflow chromatography (NanoLC). The isobaric nature of the tag prevents deconvolution of samples until fragmentation liberates the isotopically labeled reporter ions. To ensure efficient peptide labeling, large concentrations of labeling reagents are included in the reagent kits to allow scientists to use high ratios of chemical label per peptide. The increasing speed and sensitivity of mass spectrometers has reduced the peptide concentration required for analysis, leading to most of the label or labeled sample to be discarded. In conjunction, improvements in the speed of sample loading, reliable pump pressure, and stable gradient construction of analytical flow HPLCs has continued to improve the sample delivery process to the mass spectrometer. In this study we describe a method for performing multiplexed proteomics without the use of NanoLC by using offline fractionation of labeled peptides followed by rapid “standard flow” HPLC gradient LC-MS/MS. Standard Flow Multiplexed Proteomics (SFloMPro) enables high coverage quantitative proteomics of up to 16 mammalian samples in about 24 h. In this study, we compare NanoLC and SFloMPro analysis of fractionated samples. Our results demonstrate that comparable data is obtained by injecting 20 µg of labeled peptides per fraction with SFloMPro, compared to 1 µg per fraction with NanoLC. We conclude that, for experiments where protein concentration is not strictly limited, SFloMPro is a competitive approach to traditional NanoLC workflows with improved up-time, reliability and at a lower relative cost per sample.

Next-Generation Proteomics Reveals a Greater Antioxidative Response to Drought in Coffea arabica Than in Coffea canephora

Drought is a major threat to coffee, compromising the quality and quantity of its production. We have analyzed the core proteome of 18 Coffea canephora cv. Conilon Clone 153 and C. arabica cv. Icatu plants and assessed their responses to moderate (MWD) and severe (SWD) water deficits. Label-free quantitative shotgun proteomics identified 3000 proteins in both genotypes, but less than 0.8% contributed to ca. 20% of proteome biomass. Proteomic changes were dependent on the severity of drought, being stronger under SWD and with an enrolment of different proteins, functions, and pathways than under MWD. The two genotypes displayed stress-responsive proteins under SWD, but only C. arabica showed a higher abundance of proteins involved in antioxidant detoxification activities. Overall, the impact of MWD was minor in the two genotypes, contrary to previous studies. In contrast, an extensive proteomic response was found under SWD, with C. arabica having a greater potential for acclimation/resilience than C. canephora. This is likely supported by a wider antioxidative response and an ability to repair photosynthetic structures, being crucial to develop new elite genotypes that assure coffee supply under water scarcity levels.

Comparison of Different Label-Free Techniques for the Semi-Absolute Quantification of Protein Abundance

In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allow protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

A Study of the Protective Effect of Bushen Huoxue Prescription on Cerebral Microvascular Endothelia Based on Proteomics and Bioinformatics

Diabetic cognitive dysfunction is a serious complication of type 2 diabetes mellitus (T2DM), which can cause neurological and microvascular damage in the brain. At present, there is no effective treatment for this complication. Bushen Huoxue prescription (BSHX) is a newly formulated compound Chinese medicine containing 7 components. Previous research indicated that BSHX was neuroprotective against advanced glycosylation end product (AGE)-induced PC12 cell insult; however, the effect of BSHX on AGE-induced cerebral microvascular endothelia injury has not been studied. In the current research, we investigated the protective effects of BSHX on AGE-induced injury in bEnd.3 cells. Our findings revealed that BSHX could effectively protect bEnd.3 cells from apoptosis. Moreover, we analyzed the network regulation effect of BSHX on AGE-induced bEnd.3 cells injury at the proteomic level. The LC-MS/MS-based shotgun proteomics analysis showed BSHX negatively regulated multiple AGE-elicited proteins. Bioinformatics analysis revealed these differential proteins were involved in multiple processes, such as Foxo signaling pathway. Further molecular biology analysis confirmed that BSHX could downregulate the expression of FoxO1/3 protein and inhibit its nuclear transfer and inhibit the expression of downstream apoptotic protein Bim and the activation of caspase, so as to play a protective role in AGE-induced bEnd.3 injury. Taken together, these findings demonstrated the role of BSHX in the management of diabetic cerebral microangiopathy and provide some insights into the proteomics-guided pharmacological mechanism study of traditional Chinese Medicine.

Proteogenomic Analysis Reveals Proteins Involved in the First Step of Adipogenesis in Human Adipose-Derived Stem Cells

Background. Obesity is characterized as a disease that directly affects the whole-body metabolism and is associated with excess fat mass and several related comorbidities. Dynamics of adipocyte hypertrophy and hyperplasia play an important role in health and disease, especially in obesity. Human adipose-derived stem cells (hASC) represent an important source for understanding the entire adipogenic differentiation process. However, little is known about the triggering step of adipogenesis in hASC. Here, we performed a proteogenomic approach for understanding the protein abundance alterations during the initiation of the adipogenic differentiation process. Methods. hASC were isolated from adipose tissue of three donors and were then characterized and expanded. Cells were cultured for 24 hours in adipogenic differentiation medium followed by protein extraction. We used shotgun proteomics to compare the proteomic profile of 24 h-adipogenic, differentiated, and undifferentiated hASC. We also used our previous next-generation sequencing data (RNA-seq) of the total and polysomal mRNA fractions of hASC to study posttranscriptional regulation during the initial steps of adipogenesis. Results. We identified 3420 proteins out of 48,336 peptides, of which 92 proteins were exclusively identified in undifferentiated hASC and 53 proteins were exclusively found in 24 h-differentiated cells. Using a stringent criterion, we identified 33 differentially abundant proteins when comparing 24 h-differentiated and undifferentiated hASC (14 upregulated and 19 downregulated, respectively). Among the upregulated proteins, we shortlisted several adipogenesis-related proteins. A combined analysis of the proteome and the transcriptome allowed the identification of positive correlation coefficients between proteins and mRNAs. Conclusions. These results demonstrate a specific proteome profile related to adipogenesis at the beginning (24 hours) of the differentiation process in hASC, which advances the understanding of human adipogenesis and obesity. Adipogenic differentiation is finely regulated at the transcriptional, posttranscriptional, and posttranslational levels.

Comparison of Different Label-free Techniques for the Semi-absolute Quantification of Protein Abundance

In proteomics, it is essential to quantify proteins in absolute terms if we wish compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allows protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the “gold standard” for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

Biochemical Mapping of Pyrodinium bahamense Unveils Molecular Underpinnings behind Organismal Processes

Proteins, lipids, and carbohydrates from the harmful algal bloom (HAB)-causing organism Pyrodinium bahamense were characterized to obtain insights into the biochemical processes in this environmentally relevant dinoflagellate. Shotgun proteomics using label-free quantitation followed by proteome mapping using the P. bahamense transcriptome and translated protein databases of Marinovum algicola, Alexandrium sp., Cylindrospermopsis raciborskii, and Symbiodinium kawagutii for annotation enabled the characterization of the proteins in P. bahamense. The highest number of annotated hits were obtained from M. algicola and highlighted the contribution of microorganisms associated with P. bahamense. Proteins involved in dimethylsulfoniopropionate (DMSP) degradation such as propionyl CoA synthethase and acryloyl-CoA reductase were identified, suggesting the DMSP cleavage pathway as the preferred route in this dinoflagellate. Most of the annotated proteins were involved in amino acid biosynthesis and carbohydrate degradation and metabolism, indicating the active roles of these molecules in the vegetative stage of P. bahamense. This characterization provides baseline information on the cellular machinery and the molecular basis of the ecophysiology of P. bahamense.

Identification of cerebrospinal fluid biomarkers for parkinsonism using a proteomics approach

The aim of our study was to investigate cerebrospinal fluid (CSF) tryptic peptide profiles as potential diagnostic biomarkers for the discrimination of parkinsonian disorders. CSF samples were collected from individuals with parkinsonism, who had an uncertain diagnosis at the time of inclusion and who were followed for up to 12 years in a longitudinal study. We performed shotgun proteomics to identify tryptic peptides in CSF of Parkinson’s disease (PD, n = 10), multiple system atrophy patients (MSA, n = 5) and non-neurological controls (n = 10). We validated tryptic peptides with differential levels between PD and MSA using a newly developed selected reaction monitoring (SRM) assay in CSF of PD (n = 46), atypical parkinsonism patients (AP; MSA, n = 17; Progressive supranuclear palsy; n = 8) and non-neurological controls (n = 39). We identified 191 tryptic peptides that differed significantly between PD and MSA, of which 34 met our criteria for SRM development. For 14/34 peptides we confirmed differences between PD and AP. These tryptic peptides discriminated PD from AP with moderate-to-high accuracy. Random forest modelling including tryptic peptides plus either clinical assessments or other CSF parameters (neurofilament light chain, phosphorylated tau protein) and age improved the discrimination of PD vs. AP. Our results show that the discovery of tryptic peptides by untargeted and subsequent validation by targeted proteomics is a suitable strategy to identify potential CSF biomarkers for PD versus AP. Furthermore, the tryptic peptides, and corresponding proteins, that we identified as differential biomarkers may increase our current knowledge about the disease-specific pathophysiological mechanisms of parkinsonism.