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. 2017 Mar 7;11(1):33.
doi: 10.1186/s12918-017-0410-8.

Dynamic proteomics reveals bimodal protein dynamics of cancer cells in response to HSP90 inhibitor

Anat Zimmer  1 , Shlomit Amar-Farkash  1 , Tamar Danon  1 , Uri Alon  2
Affiliations

Dynamic proteomics reveals bimodal protein dynamics of cancer cells in response to HSP90 inhibitor

Anat Zimmer et al. BMC Syst Biol. .

Abstract

Background: Drugs often kill some cancer cells while others survive. This stochastic outcome is seen even in clonal cells grown under the same conditions. Understanding the molecular reasons for this stochastic outcome is a current challenge, which requires studying the proteome at the single cell level over time. In a previous study we used dynamic proteomics to study the response of cancer cells to a DNA damaging drug, camptothecin. Several proteins showed bimodal dynamics: they rose in some cells and decreased in others, in a way that correlated with eventual cell fate: death or survival. Here we ask whether bimodality is a special case for camptothecin, or whether it occurs for other drugs as well. To address this, we tested a second drug with a different mechanism of action, an HSP90 inhibitor. We used dynamic proteomics to follow 100 proteins in space and time, endogenously tagged in their native chromosomal location in individual living human lung-cancer cells, following drug administration.

Results: We find bimodal dynamics for a quarter of the proteins. In some cells these proteins strongly rise in level about 12 h after treatment, but in other cells their level drops or remains constant. The proteins which rise in surviving cells included anti-apoptotic factors such as DDX5, and cell cycle regulators such as RFC1. The proteins that rise in cells that eventually die include pro-apoptotic factors such as APAF1. The two drugs shared some aspects in their single-cell response, including 7 of the bimodal proteins and translocation of oxidative response proteins to the nucleus, but differed in other aspects, with HSP90i showing more bimodal proteins. Moreover, the cell cycle phase at drug administration impacted the probability to die from HSP90i but not camptothecin.

Conclusions: Single-cell dynamic proteomics reveals sub-populations of cells within a clonal cell line with different protein dynamics in response to a drug. These different dynamics correlate with cell survival or death. Bimodal proteins which correlate with cell fate may be potential drug targets to enhance the effects of therapy.

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Figures

Fig. 1
Fig. 1
Workflow of dynamic proteomics in response to Hsp90 inhibitor drug: We used the LARC library of over 1000 H1299 cancer cell clones; in each clone a protein is tagged fluorescently at its natural locus using exon tagging. We selected 100 proteins in diverse pathways and functions, and assayed their dynamics in the presence of the drug, as well as in a control conditions. Time lapse movies over 20 h were conducted in 96-well format. Automated image analysis captured the protein dynamics at the individual cell level, as well as mitosis and death events for each cell
Fig. 2
Fig. 2
Dynamics of all proteins as a function of time after addition of the Hsp90 inhibitor. Protein dynamics was averaged over all cells, centered and normalized to mean zero and standard deviation one. Red denotes high relative levels and blue–low levels. Ordering of proteins is based on clustering the dynamics using Matlab
Fig. 3
Fig. 3
Several proteins show bimodal dynamics, increasing in some cells and decreasing in others, in a way that correlates strongly with cell survival or death. a Most proteins show dynamics which is unimodal–all cells follow the mean, with about two-fold variation around the mean. Shown are retromer VSP26, and the enzyme ENO1. b 25 proteins have bimodal dynamics. Some cells show a decrease in protein levels (dark blue); other cells show an increase after 12 h (red). Shown are the oncogene DDX5 and the mitotic spindle protein STMN1. c STMN1 dynamics correlate with cell survival or killing: cells in which STMN1 increases are preferentially killed. Surviving cells are in light blue, killed cells in red. d Histogram of slopes of STMN1 protein accumulation in the last 10 h (slope of linear regression of protein level as a function of time). Cells with a large slope (increase) preferentially are killed. e DDX5 dynamics correlate in an inverse way with survival or killing: cells in which DDX5 increases preferentially survive to the end of the movie. Surviving cells are in light blue, killed cells in red. f Histogram of slopes of DDX5 protein accumulation in the last 10 h (slope of linear regression of protein level as a function of time). Cells with a large slope (increase) preferentially survive
Fig. 4
Fig. 4
Cells which undergo mitosis 12 h after drug treatment are preferentially killed. Bar plot shows percentage of cells which show morphological correlate of cell death in the 20 h movie. Cells are binned into those that show mitosis in the first 12 h (n = 161 cells), and those that do not (n = 120 cells). Cells are from multiple movies with different clones. Error bars are standard errors
Fig. 5
Fig. 5
In response to the Hsp90i, the oxidative stress protein thioredoxin reductase TXNRD1 shows nuclear entry at late times. At early times (2 h) most cells show TXNRD1 localized to the ER, a bright dot near the nucleus (red arrow). At late times, most cells show a dot inside the nucleus (red arrows)
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