Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob

Multi-omics dataset to decipher the complexity of drug resistance in diffuse large B-cell lymphoma

The prognosis of patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) remains unsatisfactory and, despite major advances in genomic studies, the biological mechanisms underlying chemoresistance are still poorly understood. We conducted for the first time a large-scale differential multi-omics investigation on DLBCL patient’s samples in order to identify new biomarkers that could early identify patients at risk of R/R disease and to identify new targets that could determine chemorefractoriness. We compared a well-characterized cohort of R/R versus chemosensitive DLBCL patients by combining label-free quantitative proteomics and targeted RNA sequencing performed on the same tissues samples. The cross-section of both data levels allowed extracting a sub-list of 22 transcripts/proteins pairs whose expression levels significantly differed between the two groups of patients. In particular, we identified significant targets related to tumor metabolism (Hexokinase 3), microenvironment (IDO1, CXCL13), cancer cells proliferation, migration and invasion (S100 proteins) or BCR signaling pathway (CD79B). Overall, this study revealed several extremely promising biomarker candidates related to DLBCL chemorefractoriness and highlighted some new potential therapeutic drug targets. The complete datasets have been made publically available and should constitute a valuable resource for the future research.

Developing a CO2 bicarbonation absorber for promoting microalgal growth rates with an improved photosynthesis pathway

In order to solve the problems of the short residence time and low utilization efficiency of carbon dioxide (CO₂) gas added directly to a raceway pond, a CO₂ bicarbonation absorber (CBA) was proposed to efficiently convert CO₂ gas and sodium carbonate (Na₂CO₃) solution to sodium bicarbonate (NaHCO₃), which was dissolved easily in the culture medium and left to promote the microalgal growth rate. The CO₂ gas reacted with the Na₂CO₃ solution (initial concentration = 200 mM L⁻¹ and volume ratio in CBA = 60%) for 90 min at 0.3 MPa to give the optimized molar proportion (92%) of NaHCO₃ product in total inorganic carbon and increase the microalgal growth rate by 5.0 times. Quantitative label-free protein analysis showed that the expression levels of the photosystem II (PSII) reaction centre protein (PsbH) and PSII cytochrome (PsbV2) in the photosynthesis pathway increased by 4.8 and 3.4 times, respectively, while that of the RuBisCO enzyme (rbcL) in the carbon fixation pathway increased by 3.5 times in Arthrospira platensis cells cultivated with the NaHCO₃ product in the CBA at 0.3 MPa.

To increase the residence time of CO₂ gas added directly to the raceway pond, a CO₂ bicarbonation absorber was proposed to convert CO₂ gas and Na₂CO₃ to NaHCO₃, which was dissolved easily in the solution and left to promote the biomass growth rate.

Updates on resources, software tools, and databases for plant proteomics in 2016-2017

Proteomics data processing, annotation, and analysis can often lead to major hurdles in large-scale high-throughput bottom-up proteomics experiments. Given the recent rise in protein-based big datasets being generated, efforts in in silico tool development occurrences have had an unprecedented increase; so much so, that it has become increasingly difficult to keep track of all the advances in a particular academic year. However, these tools benefit the plant proteomics community in circumventing critical issues in data analysis and visualization, as these continually developing open-source and community-developed tools hold potential in future research efforts. This review will aim to introduce and summarize more than 50 software tools, databases, and resources developed and published during 2016-2017 under the following categories: tools for data pre-processing and analysis, statistical analysis tools, peptide identification tools, databases and spectral libraries, and data visualization and interpretation tools. Intended for a well-informed proteomics community, finally, efforts in data archiving and validation datasets for the community will be discussed as well. Additionally, the author delineates the current and most commonly used proteomics tools in order to introduce novice readers to this -omics discovery platform.