Establishing and maintaining the computational infrastructure that is needed to effectively analyze proteomics data can be challenging for some labs. Cloud computing offers a flexible, scalable, and low-cost solution for proteomics data analysis, helping proteomics researchers conduct high-throughput proteomics studies at scale.

Several factors should be considered when choosing proteomics software. Tools to assess post-acquisition QC, including inspection of the chromatograms and spectras, to examine the raw signal to troubleshoot issues and tune settings, and tools to compare injections across time to assess LCMS performance, are useful. Additionally, tools to perform post-acquisition analysis, such as peptide/protein identification and quantification, and tools to gain biological insights are also important. However, for both post-acquisition QC and analysis, proteomics data analysis software should be scalable, easy-to-use, and high-performing, and should enable automation and support data storage/management.

Through next generation sequencing (NGS), massively parallel sequencing has enabled high-throughput, scalable, and fast generation of genomic information from DNA and RNA (i.e., four bases: A, C, T, and G) to measure the human genome. While NGS has revealed important findings, proteomics must be considered to gain a comprehensive understanding of human biology.

For example, when we compare transcriptomic data with proteomic data in the same biosample, we see a low or modest correlation between. This relatively weak correlation between the transcriptome and the proteome supports the importance and added value of studying biology using proteins. Much like NGS enables the almost complete coverage of the genome, a next generation proteomics (NGP) technology that enables almost complete coverage of the proteome through unbiased and deep coverage will be an important advancement and milestone in understanding human biology.

Proteogenomics is an area of research that combines proteomics and genomics to better understand the flow of genetic information between DNA, RNA, and proteins.

Coefficient of Variation (CV) is a statistic used to compare the extent of variation from one set of data to another (i.e., reproducibility), even if the means are very different. CV is a measure of the relative distribution of data points around the average (ratio of standard deviation divided by the mean). Factors that may contribute to variability include technical variability (operator, protein preparation method, instrument, ionization efficiency, or software choice) and biological variability (genetic background, disease state, age of subject, or gender). Recent studies have shown ~20-40% intensity CVs are expected.

Seer’s engineered nanoparticles (NP) consist of a magnetic core and a surface with unique physiochemical properties. When NPs are introduced into a biofluid, such as blood plasma, a selective and reproducible protein corona is formed at the nano-bio interface, driven by a combination of protein-NP affinity, protein abundance, and protein-protein interactions. In a process called the Vroman effect, there is competition between proteins for the binding surface on the NP, which results in the binding of high-affinity and low-abundant proteins. Specifically, pre-equilibrium, the protein corona composition is based mainly on proteins that are in close proximity to the NP, commonly high-abundant proteins. At equilibrium, high-abundant and low-affinity proteins are displaced by low-abundant and high-affinity proteins, resulting in the sampling of proteins across the wide dynamic range. NPs can be tuned with different functionalizations to enhance and differentiate protein selectivity.

Analyses performed at the protein-level (i.e., protein-centric) involve understanding biology using proteins, however, it is possible that protein-centric analyses conceal important distinctions between protein forms, or proteoforms, which can arise from alternative splicing (protein isoforms), allelic variation (protein variants), or post-translational modifications, which provide mechanistic insights underlying complex traits and disease. Analyses performed at the peptide-level (i.e., peptide-centric) are high-resolution and enable researchers to zoom in on the peptide sequences to identify proteoforms and better understand biology.

Depletion is a method used in proteomics research to access low abundant biomarkers.
It helps reduce the complexity of biological samples – serum, plasma, or biofluids – by removing the more abundant proteins and enhancing the detection of lower abundant proteins in both discovery and targeted proteomic analyses.

The top 22 most abundant proteins account for approximately 99% of the total protein mass and plasma, yet the many thousands of less abundant proteins found in the other 1% have significant impact on biology and health. The main challenge in proteomic research today is many of the current approaches for studying proteins are limited in depth, and, therefore, limited in their ability to access that 1%. Techniques, such as depletion, exist to try to reduce the signal from the most abundant proteins, however, these workflows can be costly, complex, and time-consuming.

Yes, there can be. Upstream workflows are complex, time-consuming, and require technical expertise. There can be challenges with protein solubility, the proteomic dynamic range (quantifying low abundant proteins), proteome complexity, data analysis, proteoform identification, and throughput.