The 2022 RSV surge was driven by multiple viral lineages.

The US experienced an early and severe respiratory syncytial virus (RSV) surge in autumn 2022. Despite the pressure this has put on hospitals and care centers, the factors promoting the surge in cases are unknown. To investigate whether viral characteristics contributed to the extent or severity of the surge, we sequenced 105 RSV-positive specimens from symptomatic patients diagnosed with RSV who presented to the Massachusetts General Hospital (MGH) and its outpatient practices in the Greater Boston Area. Genomic analysis of the resulting 77 genomes (54 with >80% coverage, and 23 with >5% coverage) demonstrated that the surge was driven by multiple lineages of RSV-A (91%; 70/77) and RSV-B (9%; 7/77). Phylogenetic analysis of all US RSV-A revealed 12 clades, 4 of which contained Massachusetts and Washington genomes. These clades individually had times to most recent common ancestor (tMRCA) between 2014 and 2017, and together had a tMRCA of 2009, suggesting that they emerged well before the COVID-19 pandemic. Similarly, the RSV-B genomes had a tMRCA between 2016 and 2019. We found that the RSV-A and RSV-B genomes in our sample did not differ statistically from the estimated clock rate of the larger phylogenetic tree (10.6 and 12.4 substitutions per year, respectively). In summary, the polyphyletic nature of viral genomes sequenced in the US during the autumn 2022 surge is inconsistent with the emergence of a single, highly transmissible causal RSV lineage.

Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds.

While gene expression profiling has traditionally been the method of choice for large-scale perturbational profiling studies, proteomics has emerged as an effective tool in this context for directly monitoring cellular responses to perturbations. We previously reported a pilot library containing 3400 profiles of multiple perturbations across diverse cellular backgrounds in the reduced-representation phosphoproteome (P100) and chromatin space (Global Chromatin Profiling, GCP). Here, we expand our original dataset to include profiles from a new set of cardiotoxic compounds and from astrocytes, an additional neural cell model, totaling 5300 proteomic signatures. We describe filtering criteria and quality control metrics used to assess and validate the technical quality and reproducibility of our data. To demonstrate the power of the library, we present two case studies where data is queried using the concept of "connectivity" to obtain biological insight. All data presented in this study have been deposited to the ProteomeXchange Consortium with identifiers PXD017458 (P100) and PXD017459 (GCP) and can be queried at https://clue.io/proteomics .

Avant-garde: an automated data-driven DIA data curation tool.

Several challenges remain in data-independent acquisition (DIA) data analysis, such as to confidently identify peptides, define integration boundaries, remove interferences, and control false discovery rates. In practice, a visual inspection of the signals is still required, which is impractical with large datasets. We present Avant-garde as a tool to refine DIA (and parallel reaction monitoring) data. Avant-garde uses a novel data-driven scoring strategy: signals are refined by learning from the dataset itself, using all measurements in all samples to achieve the best optimization. We evaluate the performance of Avant-garde using benchmark DIA datasets and show that it can determine the quantitative suitability of a peptide peak, and reach the same levels of selectivity, accuracy, and reproducibility as manual validation. Avant-garde is complementary to existing DIA analysis engines and aims to establish a strong foundation for subsequent analysis of quantitative mass spectrometry data.