Biology subjects

Spencer, S. E.

Publications and source records attributed to Spencer, S. E..

3 recordsLinked to original sources

bioRxiv2024

Effectiveness and equity of vaccination strategies against Rift Valley fever in a heterogeneous landscape

Spatio-temporal variations in environment and socio-agricultural factors create heterogeneity in livestock disease transmission risk, raising challenges in identifying populations most at risk and how this risk changes over time. Consequently, prioritising control strategies, such as vaccination, to achieve optimal or equitable outcomes across regions impedes the design of an effective vaccination strategy. We developed a metapopulation model for Rift Valley fever transmission in livestock across the Comoros archipelago which incorporates livestock vaccination in addition to heterogeneity in viral transmission rates and animal movements. We used the model to evaluate three vaccine allocation strategies-proportional allocation, optimal allocation for maximising total infections averted across the archipelago, and optimal allocation for more equitable outcomes across islands--under different vaccination coverage levels and animal identification scenarios. We report that (i) both archipelago-wide and island-specific strategy effectiveness were impacted by vaccination rate, allocation strategy, and animal identification approach, (ii) optimally allocating vaccines improved strategy effectiveness compared with proportional allocation but resulted in inequitable outcomes between islands, and (iii) tagging animals post-vaccination boosted overall strategy effectiveness for all vaccination rates.

ecology↗

bioRxiv2024

Data Independent Acquisition to Inform the Development of Targeted Proteomics Assays Using a Triple Quadrupole Mass Spectrometer

Mass spectrometry based targeted proteomics methods provide sensitive and high-throughput analysis of selected proteins. To develop a targeted bottom-up proteomics assay, peptides must be evaluated as proxies for the measurement of a protein or proteoform in a biological matrix. Candidate peptide selection typically relies on predetermined biochemical properties, data from semi-stochastic sampling, or by empirical measurements. These strategies require extensive testing and method refinement due to the difficulties associated with prediction of peptide response in the biological matrix of interest. Gas-phase fractionated (GPF) narrow window data-independent acquisition (DIA) aids in the development of reproducible selected reaction monitoring (SRM) assays by providing matrix-specific information on peptide detectability and quantification by mass spectrometry. To demonstrate the suitability of DIA data for selecting peptide targets, we reimplement a portion of an existing assay to measure 98 Alzheimers disease proteins in cerebrospinal fluid (CSF). Peptides were selected from GPF-DIA based on signal intensity and reproducibility. The resulting SRM assay exhibits similar quantitative precision to published data, despite the inclusion of different peptides between the assays. This workflow enables development of new assays without additional up-front data acquisition, demonstrated here through generation of a separate assay for an unrelated set of proteins in CSF from the same dataset.

biochemistry↗

bioRxiv2024

A framework for quality control in quantitative proteomics

A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share vignettes applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible. Data are available on Panorama Public and on ProteomeXchange under the identifier PXD051318.

biochemistry↗