Project description:Large scale proteomic profiling of cell lines can yield valuable insights into the molecular signatures attributed to variable genotypes or induced perturbations. Specifically, the ability to perform deep and rapid proteome analysis of pharmacologically modulated cells could generate drug-protein associations for large libraries of compounds that predict mechanism of action and enable rational drug design. Although isobaric labelling has greatly increased the throughput of proteomic analysis at deep coverage, the commonly used sample preparation workflows often require complex time-consuming steps and/or costly consumables, limiting their suitability for large scale studies. Here, we present a simplified and cost effective one-pot reaction sample preparation workflow in a 96-well plate format with manual parallel processing (SimPLIT), that minimizes processing steps and reduces technical variability. The workflow is based on a sodium deoxycholate lysis buffer and a single detergent clean-up step after peptide labeling, followed by quick off-line fractionation and MS2 analysis. The simplified workflow demonstrates high reproducibility and provides improved proteome representation compared to alternative approaches. We showcase the large-scale applicability of the workflow by investigating proteomic heterogeneity in a panel of colorectal cancer cell lines and by performing target discovery for a set of molecular glue degraders in different cell lines, in a 96-sample assay. Using this workflow, we report a subset of frequently dysregulated proteins in colorectal cancer cells and uncover cell-dependent protein degradation profiles of seven cereblon E3 ligase modulators (CRL4CRBN). Overall, SimPLIT is a robust method that can be easily implemented in most proteomics laboratories for medium-to-large scale TMT-based studies involving deep profiling of cell lines.
Project description:Large scale proteomic profiling of cell lines can yield valuable insights into the molecular signatures attributed to variable genotypes or induced perturbations. Specifically, the ability to perform deep and rapid proteome analysis of pharmacologically modulated cells could generate drug-protein associations for large libraries of compounds that predict mechanism of action and enable rational drug design. Although isobaric labelling has greatly increased the throughput of proteomic analysis at deep coverage, the commonly used sample preparation workflows often require complex time-consuming steps and/or costly consumables, limiting their suitability for large scale studies. Here, we present a simplified and cost effective one-pot reaction sample preparation workflow in a 96-well plate format with manual parallel processing (SimPLIT), that minimizes processing steps and reduces technical variability. The workflow is based on a sodium deoxycholate lysis buffer and a single detergent clean-up step after peptide labeling, followed by quick off-line fractionation and MS2 analysis. The simplified workflow demonstrates high reproducibility and provides improved proteome representation compared to alternative approaches. We showcase the large-scale applicability of the workflow by investigating proteomic heterogeneity in a panel of colorectal cancer cell lines and by performing target discovery for a set of molecular glue degraders in different cell lines, in a 96-sample assay. Using this workflow, we report a subset of frequently dysregulated proteins in colorectal cancer cells and uncover cell-dependent protein degradation profiles of seven cereblon E3 ligase modulators (CRL4CRBN). Overall, SimPLIT is a robust method that can be easily implemented in most proteomics laboratories for medium-to-large scale TMT-based studies involving deep profiling of cell lines.
Project description:Large numbers of cells are generally required for quantitative global proteome profiling due to the significant surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations, such as circulating tumor cells (CTCs) and early metastatic cells. Herein we report a facile mass spectrometry (MS)-based single-cell proteomics method that capitalizes on a MS-compatible nonionic surfactant, n-Dodecyl-β-D-maltoside, for greatly reducing the surface adsorption losses by ~20-fold for effective single-tube processing of single cells, thus significantly improving detection sensitivity for single-cell proteomic analysis. With standard MS platforms, the method allows for the first time precise, label-free, reliable quantification of hundreds of proteins from single human cells in a simple, convenient manner. When applied to a patient CTC-derived xenograft (PCDX) model, the method can reveal distinct protein signatures between primary tumor cells and early metastases to the lungs at the single-cell resolution. The approach paves the way for routine, precise quantitative single-cell proteomic analysis.
Project description:Solid tumors are complex organs comprising neoplastic cells and stroma, yet cancer cell lines remain widely used to study tumor biology, biomarkers and experimental therapy. Here, we performed a fully integrative analysis of global proteomic data comparing human colorectal cancer (CRC) cell lines to primary tumors and normal tissues. We found a significant, systematic difference between cell line and tumor proteomes, with a major contribution from tumor stroma proteomes. Nevertheless, cell lines overall mirrored the proteomic differences observed between tumors and normal tissues, in particular for genetic information processing and metabolic pathways, indicating that cell lines provide a system for the study of the intrinsic molecular programs in cancer cells. Intersection of cell line data with tumor data provided insights into tumor cell specific proteome alterations driven by genomic alterations. Our integration of cell line proteogenomic data with drug sensitivity data highlights the potential of proteomic data in predicting therapeutic response. We identified representative cell lines for the proteomic subtypes of primary tumors, and linked these to drug sensitivity data to identify subtype-specific drug candidates.
Project description:Development and evaluation of an autosampler for integrating nanoPOTS with LC-MS. Includes proteomic data from nanoPOTS-based sample preparation, including diluted peptides of Shewanella oneidensis MR-1, single cultured MCF10A cells, and single cells from three Acute Myeloid Leukemia (AML) cell lines (MOLM-14, K562, and CMK). Data was searched with MaxQuant.
Project description:Local invasion is a critical early step in metastatic cancer. This study investigated the invasion mechanisms of primary (IGR39) and metastatic (IGR37) melanoma cells at the single-cell level using single-probe single-cell mass spectrometry (SCMS). We detected 166 of 228 metabolites in IGR39 and 168 of 172 in IGR37.
Project description:Large numbers of cells are generally required for quantitative global proteome profiling due to the significant surface adsorption losses associated with sample processing. Such bulk measurement obscures important cell-to-cell variability (cell heterogeneity) and makes proteomic profiling impossible for rare cell populations (e.g., circulating tumor cells (CTCs)). Here we report a surfactant-assisted one-pot sample preparation coupled with mass spectrometry (MS) termed SOP-MS for label-free global single-cell proteomics. SOP-MS capitalizes on the combination of a MS-compatible nonionic surfactant, n-Dodecyl-β-D-maltoside, and hydrophobic surface-based low-bind tubes or multi-well plates for ‘all-in-one’ one-pot sample preparation. This ‘all-in-one’ method including elimination of all sample transfer steps maximally reduces surface adsorption losses by ≥20-fold for effective processing of single cells, thus significantly improving detection sensitivity for single-cell proteomics. This method allows for the first time, convenient, label-free quantification of hundreds of proteins from single human cells and ~1200 proteins from small tissue sections (close to ~20 cells). When applied to a patient CTC-derived xenograft (PCDX) model at the single-cell resolution, SOP-MS can reveal distinct protein signatures between primary tumor cells and early metastatic lung cells, which are related to the selection pressure of anti-tumor immunity during breast cancer metastasis. The approach paves the way for routine, precise, quantitative single-cell proteomics.
Project description:Single cell proteomics data from three murine cell lines (Raw, SVEC4-10, C10) and HeLa cell lysate. Samples were digested with trypsin and labeled with TMT 10-Plex on a small scale using nanoPOTS, followed by LC-MS/MS analysis.