ASAP─Automated Sonication-Free Acid-Assisted Proteomes─from Cells and FFPE Tissues

Abstract

Formalin-fixed, paraffin-embedded (FFPE) tissues are an invaluable resource for retrospective studies, but protein extraction and subsequent sample processing steps have been shown to be challenging for mass spectrometry (MS) analysis. Streamlined high-throughput sample preparation workflows are essential for efficient peptide extraction from complex clinical specimens such as fresh frozen tissues or FFPE. Overall, proteome analysis has gained significant improvements in the instrumentation, acquisition methods, sample preparation workflows, and analysis pipelines, yet even the most recent FFPE workflows remain complex and are not readily scalable. Here, we present an optimized workflow for automated sonication-free acid-assisted proteome (ASAP) extraction from FFPE sections. ASAP enables efficient protein extraction from FFPE specimens, achieving similar proteome coverage as established methods using expensive sonicators, resulting in reduced sample processing time. The broad applicability of ASAP on archived pediatric tumor FFPE specimens resulted in high-quality data with increased proteome coverage and quantitative reproducibility. Our study demonstrates the practicality and superiority of the ASAP workflow as a streamlined, time- and cost-effective pipeline for high-throughput FFPE proteomics of clinical specimens.

Targeted proteomic quantitation of NRF2 signaling and predictive biomarkers in HNSCC

“…5B). These yields and overall peptide characteristics were similar to those of prior FFPE proteomic studies [46–49].…”

Proteomics and personalized patient-derived xenograft models identify treatment opportunities for a progressive malignancy within a clinically actionable timeframe and change care

“…Immediately following the biopsy, the anatomical pathology laboratory at BC Children's Hospital prepared H&E-stained 10 µm FFPE sections from clinical FFPE blocks and isolated the tumor and adjacent normal regions using macro-dissection. Following transfer to the research laboratory, we used rapid automated sonication-free acidassisted proteome (ASAP) processing (26) and data-independent acquisition (DIA) liquid chromatography-mass spectrometry (LC-MS/MS) to assemble quantitative proteome maps comparing the abundance of 4703 proteins in metastatic nodules and adjacent normal lung regions.…”

HistoMIL: A Python package for training multiple instance learning models on histopathology slides

“…Notably, QuPath allows a basic ML pipeline with packages like scikit-learn 20 through its graphical user interface, but it is not designed for DL or MIL algorithms. Pre-processing packages like PyHIST, 21 deep-histopath, 22 Multi_Scale_Tools, 23 and ASAP 24 include common processing steps like reading multi-scale information, 25 tissue segmentation or patching. However, designing a comprehensive and broadly applicable ML-oriented package that encompasses basic components like WSI reading, patch extraction and color normalisation 26 , 27 remains difficult.…”