Highly Repeatable Tissue Proteomics for Kidney Transplant Pathology: Technical and Biological Validation of Protein Analysis using LC-MS/MS

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июнь 17, 2024

Abstract Accurate pathological assessment of tissue samples is key for diagnosis and optimal treatment decisions. Traditional pathology techniques suffer from subjectivity resulting in inter-observer variability, limitations identifying subtle molecular changes. Omics approaches provide both evidence unbiased classification, which increases the quality reliability final assessment. Here, we focus on mass spectrometry (MS)-based proteomics as a method to reveal biopsy differences. For MS data be useful, information collected formalin fixed paraffin embedding (FFPE) tissues needs consistent quantitatively accurate contain sufficient clinically relevant information. Therefore, developed an MS-based workflow assessed analytical repeatability 36 kidney biopsies, ultimately analysing differences similarities over 5000 proteins per biopsy. Additional 301 transplant biopsies were analysed understand other physical parameters including effects size, standing time autosampler, effect clinical validation. acquired using Data-Independent Acquisition (DIA) provides gigabytes sample form high proteome (and genome) representation, at exquisitely quantitative accuracy. The FFPE-based optimised here coefficient variation below 20%, more than parallel. We also observed that thickness does affect outcome quality: 5 μm sections show same 10 sections. Notably, our reveals excellent agreement relative abundance known protein biomarkers with transplantation lesion scores used diagnostics. findings presented demonstrate ease, speed, robustness method, where wealth minute can assist expand pathology, possibly reduce variability.

Язык: Английский

Regional and longitudinal dynamics of human milk protein components assessed by proteome analysis on a fast and robust micro-flow LC–MS/MS system

Food Chemistry, Год журнала: 2024, Номер 465, С. 141981 - 141981

Опубликована: Ноя. 9, 2024

Язык: Английский

Beta-DIA: Integrating learning-based and function-based feature scores to optimize the proteome profiling of single-shot diaPASEF mass spectrometry data

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 21, 2024

We present a freely available diaPASEF data analysis software, Beta-DIA, that utilizes deep learning methods to score coelution consistency in retention time-ion mobility dimensions and spectrum similarity. Beta-DIA integrates these learning-based scores with traditional function-based scores, enhancing the qualitative performance. In some low detection datasets, identifies twice as many protein groups DIA-NN. The success of has paved another way for application fundamental proteome profiling.

Язык: Английский