Automated sample preparation with SP3 for low-input clinical proteomics.

Journal: Molecular systems biology
Published Date:

Abstract

High-throughput and streamlined workflows are essential in clinical proteomics for standardized processing of samples from a variety of sources, including fresh-frozen tissue, FFPE tissue, or blood. To reach this goal, we have implemented single-pot solid-phase-enhanced sample preparation (SP3) on a liquid handling robot for automated processing (autoSP3) of tissue lysates in a 96-well format. AutoSP3 performs unbiased protein purification and digestion, and delivers peptides that can be directly analyzed by LCMS, thereby significantly reducing hands-on time, reducing variability in protein quantification, and improving longitudinal reproducibility. We demonstrate the distinguishing ability of autoSP3 to process low-input samples, reproducibly quantifying 500-1,000 proteins from 100 to 1,000 cells. Furthermore, we applied this approach to a cohort of clinical FFPE pulmonary adenocarcinoma (ADC) samples and recapitulated their separation into known histological growth patterns. Finally, we integrated autoSP3 with AFA ultrasonication for the automated end-to-end sample preparation and LCMS analysis of 96 intact tissue samples. Collectively, this constitutes a generic, scalable, and cost-effective workflow with minimal manual intervention, enabling reproducible tissue proteomics in a broad range of clinical and non-clinical applications.

Authors

  • Torsten Müller
    German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Mathias Kalxdorf
    German Cancer Research Center (DKFZ), Heidelberg, Germany.
  • Rémi Longuespée
    Department of Clinical Pharmacology and Pharmacoepidemiology, Heidelberg University, Heidelberg, Germany.
  • Daniel N Kazdal
    Institute of Pathology, Heidelberg University, Heidelberg, Germany.
  • Albrecht Stenzinger
    From the Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany (P.S., J.P.R., P.K., H.P.S., D.B.); Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.K., K.H.M.H.); Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany (J.P.R., M.H.); Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany (M.W.); Department of Neuroradiology, University of Heidelberg Medical Center, Heidelberg, Germany (P.K.); Junior Group Medical Imaging and Radiology-Cancer Prevention, German Cancer Research Center (DKFZ), Heidelberg, Germany (S.B.); Division of Medical Physics, German Cancer Research Center (DKFZ), Heidelberg, Germany (T.A.K.); Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany (A.S.); and German Cancer Consortium (DKTK), Heidelberg, Germany (H.P.S., K.H.M.H., D.B.).
  • Jeroen Krijgsveld
    German Cancer Research Center (DKFZ), Heidelberg, Germany.