Investigating Toxin Diversity and Abundance in Snake Venom Proteomes

Understanding snake venom proteomes is becoming increasingly important to understand snake venom biology, evolution and especially clinical effects of venoms and approaches to antivenom development. To explore the current state of snake venom proteomics and transcriptomics we investigated venom proteomic methods, associations between methodological and biological variability and the diversity and abundance of protein families. We reviewed available studies on snake venom proteomes from September 2017 to April 2021. This included 81 studies characterising venom proteomes of 79 snake species, providing data on relative toxin abundance for 70 species and toxin diversity (number of different toxins) for 37 species. Methodologies utilised in these studies were summarised and compared. Several comparative studies showed that preliminary decomplexation of crude venom by chromatography leads to increased protein identification, as does the use of transcriptomics. Combining different methodological strategies in venomic approaches appears to maximize proteome coverage. 48% of studies used the RP-HPLC →1D SDS-PAGE →in-gel trypsin digestion → ESI -LC-MS/MS pathway. Protein quantification by MS1-based spectral intensity was used twice as commonly as MS2-based spectral counting (33–15 studies). Total toxin diversity was 25–225 toxins/species, with a median of 48. The relative mean abundance of the four dominant protein families was for elapids; 3FTx–52%, PLA2–27%, SVMP–2.8%, and SVSP–0.1%, and for vipers: 3FTx–0.5%, PLA2–24%, SVMP–27%, and SVSP–12%. Viper venoms were compositionally more complex than elapid venoms in terms of number of protein families making up most of the venom, in contrast, elapid venoms were made up of fewer, but more toxin diverse, protein families. No relationship was observed between relative toxin diversity and abundance. For equivalent comparisons to be made between studies, there is a need to clarify the differences between methodological approaches and for acceptance of a standardised protein classification, nomenclature and reporting procedure. Correctly measuring and comparing toxin diversity and abundance is essential for understanding biological, clinical and evolutionary implications of snake venom composition.

Direct Imaging and Identification of Proteoforms up to 70 kDa from Human Tissue

Imaging of proteoforms in human tissues is hindered by low molecular specificity and limited proteome coverage. Here, we introduce proteoform imaging mass spectrometry (PiMS), which increases the size limit for proteoform detection and identification by 4-fold compared to reported methods, and reveals tissue localization of proteoforms at <80 μm spatial resolution. PiMS advances proteoform imaging by combining liquid sampling (nanospray desorption electrospray ionization, nano-DESI) with ion detection using individual ion mass spectrometry (I2MS). We demonstrate the first proteoform imaging of human kidney, identifying 169 of 400 proteoforms <70 kDa using top-down mass spectrometry and database lookup from the human proteoform atlas, including dozens of key enzymes in primary metabolism. Moreover, PiMS images visualize kidney anatomical structures and cellular neighborhoods in the vasculature versus the medulla or the cortex. The benefits of PiMS are poised to increase proteome coverage for label-free protein imaging of intact tissues.TeaserNano-DESI combined with individual ion mass spectrometry generates images of proteoforms up to 70 kDa.

Analysis of 8000 proteins and reduced carry over significantly increase the throughput of single-shot proteomics

In the field of LC-MS based proteomics, increases in sampling depth and proteome coverage have mainly been accomplished by rapid advances in mass spectrometer technology. The comprehensiveness and quality of data that can be generated do however also depend on the performance provided by nano liquid chromatography (nanoLC) separations. Proper selection of reversed-phase separation columns can be of paramount importance to provide the MS instrument with peptides at the highest possible concentration and separated at the highest possible resolution. As an alternative to traditional packed bed LC column technology that uses beads packed into capillary tubing, we present a novel LC column format based on photolithographic definition and Deep Reactive Ion Etching (DRIE) into silicon wafers. With a next generation pillar array column designed for universal use in bottom-up proteomics, the critical dimensions of the stationary phase support structures have been reduced by a factor of 2 to provide further increases in separation power. To demonstrate the potential for single-shot proteomics workflows, we report on a series of optimization and benchmarking experiments where we combine LC separation on a new generation of pillar array columns using Vanquish Neo UHPLC with fast Orbitrap Tribrid MS data-dependent acquisition (DDA) and High-Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS). In addition to providing superior proteome coverage, robust operation over more than 1 month with a single nanoESI emitter and reduction of the column related sample carry over are additional figures of merit that can help improve proteome research sensitivity, productivity and standardization.

DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation

Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we establish a DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expands the phosphoproteome coverage while maintaining high quantification performance, which leads to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server as well as an offline app to facilitate user access to model training, predictions and library generation.

Single-sample proteome enrichment enables missing protein recovery and phenotype association

Proteomic studies characterize the protein composition of complex biological samples. Despite recent developments in mass spectrometry instrumentation and computational tools, low proteome coverage remains a challenge. To address this, we present Proteome Support Vector Enrichment (PROSE), a fast, scalable, and effective pipeline for scoring protein identifications based on gene co-expression matrices. Using a simple set of observed proteins as input, PROSE gauges the relative importance of proteins in the phenotype. The resultant enrichment scores are interpretable and stable, corresponding well to the source phenotype, thus enabling reproducible recovery of missing proteins. We further demonstrate its utility via reanalysis of the Cancer Cell Line Encyclopedia (CCLE) proteomic data, with prediction of oncogenic dependencies and identification of well-defined regulatory modules. PROSE is available as a user-friendly Python module from https://github.com/bwbio/PROSE.

Increasing the throughput of sensitive proteomics by plexDIA

Current mass-spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. We aimed to increase the throughput of high-sensitivity proteomics while achieving high proteome coverage and quantitative accuracy. We developed a general experimental and computational framework, plexDIA, for simultaneously multiplexing the analysis of both peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. Specifically, plexDIA using 3-plex nonisobaric mass tags enables quantifying 3-fold more protein ratios among nanogram-level samples. Using 1 hour active gradients and first-generation Q Exactive, plexDIA quantified about 8,000 proteins in each sample of labeled 3-plex sets. Furthermore, plexDIA increases the consistency of protein quantification, resulting in over 2-fold reduction of missing data across samples. We applied plexDIA to quantify proteome dynamics during the cell division cycle in cells isolated based on their DNA content. The high sensitivity and accuracy of plexDIA detected many classical cell cycle proteins and discovered new ones. These results establish a general framework for increasing the throughput of highly sensitive and quantitative protein analysis.

DeepPhospho: Accelerate DIA phosphoproteome profiling by Deep Learning

Phosphoproteomics integrating data-independent acquisition (DIA) has enabled deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a novel deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we established a new DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expanded the phosphoproteome coverage while maintaining high quantification performance, which led to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server to facilitate user access to predictions and library generation.