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In the version of this article initially published, in Fig. 4c, the y-axis units read ‘mg/dl’ but should have read ‘percent baseline’. The error has been corrected in the HTML and PDF versions of the article.

Introduction Despite trastuzumab having enhanced selectivity for human epidermal growth factor receptor 2 (HER-2) overexpressing breast cancer cells, treatment is hampered by interindividual variation and tumors with high mitogenic potential. The lack of significant clinical benefit in certain patient cohorts suggests that HER-2 expression is ineffective as a sole prognostic indicator of response to therapy. Therefore, optimizing the clinical role of trastuzumab in drug combinations remains critical for clinical success. Aim To investigate the effects of trastuzumab in combination with either doxorubicin or geldanamycin on in vitro cell viability, cell cycling, apoptosis and relative HER-2 expression in HER-2-positive (SK-BR-3) and estrogen receptor-positive (MCF-7) breast adenocarcinoma models. Results HER-2-rich SK-BR-3 cells demonstrated a greater sensitivity to the effects of doxorubicin than MCF-7 cells. Concurrent trastuzumab exposure resulted in a further reduction in cell viability. This decreased cell viability induced by doxorubicin was associated with activation of executioner caspases as well as with alterations in cell-cycle kinetics, primarily promoting S-phase accumulation. Doxorubicin had no effect on surface HER-2 density expression. Geldanamycin reduced cell viability significantly greater in SK-BR-3 than MCF-7 cells, and was associated with G2 cell-cycle accumulation. The addition of trastuzumab did not augment these effects. Geldanamycin promoted substantial reductions in relative surface HER-2 density in SK-BR-3 cells. Conclusion The in vitro data supported the rationale for using doxorubicin in trastuzumab-based therapies. Therefore, despite the incidence of cardiotoxicity, doxorubicin could retain a fundamental role in treating HER-2-positive breast cancer. While geldanamycin is a potent cytotoxic agent, its concurrent use with trastuzumab requires further research into the transient or permanent nature of alterations in HER-2 status in cell progeny.

Protein localisation and translocation between intracellular compartments underlie almost all physiological processes. The hyperLOPIT proteomics platform combines mass spectrometry with state-of-the-art machine learning to map the subcellular location of thousands of proteins simultaneously. We combine global proteome analysis with hyperLOPIT in a fully Bayesian framework to elucidate spatiotemporal proteomic changes during a lipopolysaccharide (LPS)-induced inflammatory response. We report a highly dynamic proteome in terms of both protein abundance and subcellular localisation, with alterations in the interferon response, endo-lysosomal system, plasma membrane reorganisation and cell migration. Proteins not previously associated with an LPS response were found to relocalise upon stimulation, the functional consequences of which are still unclear. By quantifying proteome-wide uncertainty through Bayesian modelling, a necessary role for protein relocalisation and the importance of taking a holistic overview of the LPS-driven immune response has been revealed. The data are showcased as an interactive application freely available for the scientific community.

Protein localisation and translocation between intracellular compartments underlie almost all physiological processes. The hyperLOPIT proteomics platform combines mass spectrometry with state-of-the-art machine learning to map the subcellular location of thousands of proteins simultaneously. We combine global proteome analysis with hyperLOPIT in a fully Bayesian framework to elucidate spatiotemporal proteomic changes during a lipopolysaccharide (LPS)-induced inflammatory response. We report a highly dynamic proteome in terms of both protein abundance and subcellular localisation, with alterations in the interferon response, endo-lysosomal system, plasma membrane reorganisation and cell migration. Proteins not previously associated with an LPS response were found to relocalise upon stimulation, the functional consequences of which are still unclear. By quantifying proteome-wide uncertainty through Bayesian modelling, a necessary role for protein relocalisation and the importance of taking a holistic overview of the LPS-driven immune response has been revealed. The data are showcased as an interactive application freely available for the scientific community.

Protein localisation and translocation between intracellular compartments underlie almost all physiological processes. The hyperLOPIT proteomics platform combines mass spectrometry with state-of-the-art machine learning to map the subcellular location of thousands of proteins simultaneously. We combine global proteome analysis with hyperLOPIT in a fully Bayesian framework to elucidate spatiotemporal proteomic changes during a lipopolysaccharide (LPS)-induced inflammatory response. We report a highly dynamic proteome in terms of both protein abundance and subcellular localisation, with alterations in the interferon response, endo-lysosomal system, plasma membrane reorganisation and cell migration. Proteins not previously associated with an LPS response were found to relocalise upon stimulation, the functional consequences of which are still unclear. By quantifying proteome-wide uncertainty through Bayesian modelling, a necessary role for protein relocalisation and the importance of taking a holistic overview of the LPS-driven immune response has been revealed. The data are showcased as an interactive application freely available for the scientific community.

Numerous in vitro models endeavour to mimic the characteristics of primary human hepatocytes for applications in regenerative medicine and pharmaceutical science. Mature hepatocyte-like cells (HLCs) derived from human induced pluripotent stem cells (hiPSCs) are one such in vitro model. Due to insufficiencies in transcriptome to proteome correlation, characterising the proteome of HLCs is essential to provide a suitable framework for their continual optimization. Here we interrogated the proteome during stepwise differentiation of hiPSCs into HLCs over 40 days. Whole cell protein lysates were collected and analysed using stabled isotope labelled mass spectrometry based proteomics. Quantitative proteomics identified over 6,000 proteins in duplicate multiplexed labelling experiments across two different time course series. Inductive cues in differentiation promoted sequential acquisition of hepatocyte specific markers. Analysis of proteins classically assigned as hepatic markers demonstrated trends towards maximum relative abundance between differentiation day 30 and 32. Characterisation of abundant proteins in whole cells provided evidence of the time dependent transition towards proteins corresponding with the functional repertoire of the liver. This data highlights how far the proteome of undifferentiated precursors have progressed to acquire a hepatic phenotype and constructs a platform for optimisation and improved maturation of HLC differentiation.