Ocean metaproteomics provides valuable insights into the structure and function of marine microbial communities. Yet, ocean samples are challenging due to their extensive biological diversity that results in a very large number of peptides with a large dynamic range. This study characterized the capabilities of data independent acquisition (DIA) mode for use in ocean metaproteomic samples. Spectral libraries were constructed from discovered peptides and proteins using machine learning algorithms to remove incorporation of false positives in the libraries. When compared with 1-dimensional and 2-dimensional data dependent acquisition analyses (DDA), DIA outperformed DDA both with and without gas phase fractionation. We found that larger discovered protein spectral libraries performed better, regardless of the geographic distance between where samples were collected for library generation and where the test samples were collected. Moreover, the spectral library containing all unique proteins present in the Ocean Protein Portal outperformed smaller libraries generated from individual sampling campaigns. However, a spectral library constructed from all open reading frames in a metagenome was found to be too large to be workable, resulting in low peptide identifications due to challenges maintaining a low false discovery rate with such a large database size. Given sufficient sequencing depth and validation studies, spectral libraries generated from previously discovered proteins can serve as a community resource, saving resequencing efforts. The spectral libraries generated in this study are available at the Ocean Protein Portal for this purpose.

Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis might overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells and this review highlights the role of data-independent acquisition MS (DIA-MS) in SCP. One major hurdle in SCP is the limited material in single-cell samples, but DIA-based techniques offer multiple potential solutions for their analysis. Utilizing wide precursor isolation windows to fragment multiple peptides simultaneously, DIA-based methods improve sensitivity, quantitative accuracy, and reproducibility at a cost in data analysis complexity. DIA methods can also be combined with sample multiplexing methods to increase the sample throughput, currently a key limitation in SCP. Challenges remain for interpreting sample multiplexed data from DIA-based SCP experiments, particularly with regards to isobaric tagging methods. Even still, we believe that DIA-based SCP approaches will play a major role in our understanding of systems biology.

Due to their oftentimes ambiguous nature, phosphopeptide positional isomers can present challenges in bottom-up mass spectrometry-based workflows as search engine scores alone are often not enough to confidently distinguish them. Additional scoring algorithms can remedy this by providing confidence metrics in addition to these search results, reducing ambiguity. Here we describe challenges to interpreting phosphoproteomics data and review several different approaches to determine sites of phosphorylation for both data-dependent and data-independent acquisition-based workflows. Finally, we discuss open questions regarding neutral losses, gas-phase rearrangement, and false localization rate estimation experienced by both types of acquisition workflows and best practices for managing ambiguity in phosphosite determination.