Data-Dependent Acquisition with Precursor Coisolation Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics.

We combined efficient sample preparation and ultra-low-flow liquid chromatography with a newly developed data acquisition and analysis scheme termed wide window acquisition (WWA) to quantify >3,000 proteins from single cells in rapid label-free analyses. WWA employs large isolation windows to intentionally co-isolate and co-fragment adjacent precursors along with the selected precursor. Optimized WWA increased the number of MS2-identified proteins by ≈40 % relative to standard data-dependent acquisition. For a 40-min LC gradient operated at ≈15 nL/min, we identified an average of 3,524 proteins per single-cell-sized aliquot of protein digest. Reducing the active gradient to 20 min resulted in a modest 10 % decrease in proteome coverage. Using this platform, we compared protein expression between single HeLa cells having an essential autophagy gene, atg9a, knocked out, with their isogenic WT parental line. Similar proteome coverage was observed, and 268 proteins were significantly up- or downregulated. Protein upregulation primarily related to innate immunity, vesicle trafficking and protein degradation.

Simplified and Unified Access to Cancer Proteogenomic Data.

Comprehensive cancer data sets recently generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) offer great potential for advancing our understanding of how to combat cancer. These data sets include DNA, RNA, protein, and clinical characterization for tumor and normal samples from large cohorts of many different cancer types. The raw data are publicly available at various Cancer Research Data Commons. However, widespread reuse of these data sets is also facilitated by easy access to the processed quantitative data tables. We have created a data application programming interface (API) to distribute these processed tables, implemented as a Python package called cptac. We implement it such that users who prefer to work in R can easily use our package for data access and then transfer the data into R for analysis. Our package distributes the finalized processed CPTAC data sets in a consistent, up-to-date format. This consistency makes it easy to integrate the data with common graphing, statistical, and machine-learning packages for advanced analysis. Additionally, consistent formatting across all cancer types promotes the investigation of pan-cancer trends. The data API structure of directly streaming data within a programming environment enhances the reproducibility. Finally, with the accompanying tutorials, this package provides a novel resource for cancer research education. View the software documentation at https://paynelab.github.io/cptac/. View the GitHub repository at https://github.com/PayneLab/cptac.

Engineering At-Home Dilution and Filtration Methods to Enable Paper-Based Colorimetric Biosensing in Human Blood with Cell-Free Protein Synthesis.

Diagnostic blood tests can guide the administration of healthcare to save and improve lives. Most clinical biosensing blood tests require a trained technician and specialized equipment to process samples and interpret results, which greatly limits test accessibility. Colorimetric paper-based diagnostics have an equipment-free readout, but raw blood obscures a colorimetric response which has motivated diverse efforts to develop blood sample processing techniques. This work uses inexpensive readily-available materials to engineer user-friendly dilution and filtration methods for blood sample collection and processing to enable a proof-of-concept colorimetric biosensor that is responsive to glutamine in 50 µL blood drop samples in less than 30 min. Paper-based user-friendly blood sample collection and processing combined with CFPS biosensing technology represents important progress towards the development of at-home biosensors that could be broadly applicable to personalized healthcare.

"Just add small molecules" cell-free protein synthesis: Combining DNA template and cell extract preparation into a single fermentation.

Cell-free protein synthesis (CFPS) is a versatile biotechnology platform enabling a broad range of applications including clinical diagnostics, large-scale production of officinal therapeutics, small-scale on-demand production of personal magistral therapeutics, and exploratory research. The shelf stability and scalability of CFPS systems also have the potential to overcome cost and infrastructure challenges for distributing and using essential medical tests at home in both high- and low-income countries. However, CFPS systems are often more time-consuming and expensive to prepare than traditional in vivo systems, limiting their broader use. Much work has been done to lower CFPS costs by optimizing cell extract preparation, small molecule reagent recipes, and DNA template preparation. In order to further reduce reagent cost and preparation time, this work presents a CFPS system that does not require separately purified DNA template. Instead, a DNA plasmid encoding the recombinant protein is transformed into the cells used to make the extract, and the extract preparation process is modified to allow enough DNA to withstand homogenization-induced shearing. The finished extract contains sufficient levels of intact DNA plasmid for the CFPS system to operate. For a 10 mL scale CFPS system expressing recombinant sfGFP protein for a biosensor, this new system reduces reagent cost by more than half. This system is applied to a proof-of-concept glutamine sensor compatible with smartphone quantification to demonstrate its viability for further cost reduction and use in low-resource settings.

Proteogenomic data and resources for pan-cancer analysis.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

Proteogenomic and metabolomic characterization of human glioblastoma.

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment.