Proteomic sensors for quantitative multiplexed and spatial monitoring of kinase signaling

Abstract

Understanding kinase action requires precise quantitative measurements of their activity in vivo. In addition, the ability to capture spatial information of kinase activity is crucial to deconvolute complex signaling networks, interrogate multifaceted kinase actions, and assess drug effects or genetic perturbations. Here we develop a proteomic kinase activity sensor technique (ProKAS) for the analysis of kinase signaling using mass spectrometry. ProKAS is based on a tandem array of peptide sensors with amino acid barcodes that allow multiplexed analysis for spatial, kinetic, and screening applications. We engineered a ProKAS module to simultaneously monitor the activities of the DNA damage response kinases ATR, ATM, and CHK1 in response to genotoxic drugs, while also uncovering differences between these signaling responses in the nucleus, cytosol, and replication factories. Furthermore, we developed an in silico approach for the rational design of specific substrate peptides expandable to other kinases. Overall, ProKAS is a versatile system for systematically and spatially probing kinase action in cells.

Fig. 1. Design and rationale of ProKAS, a modular technique for multiplexed analysis of kinase activity using mass spectrometry.

A Schematic representation of the ProKAS construct for expression of the biosensor polypeptide containing fluorescent tags for visualization, a targeting element (TE) for subcellular localization, a multiplexed kinase sensor (MKS) module for detecting kinase activity, and an ALFA tag for high-affinity purification. B Detailed view of the MKS module consisted of multiple kinase sensors, each featuring a kinase substrate motif, a barcode, and a flanking arginine residue allowing for tryptic digestion and independent MS-based quantification. C Overview of the ProKAS workflow, starting with expression of biosensors via plasmid transfection. Expressing cell populations are then treated with a kinase stimulus of choice, after which ProKAS biosensors are purified from cell lysates, digested with trypsin, and the individual kinase sensors are quantified via MS in both their unphosphorylated and phosphorylated forms. D Application of ProKAS for multiplexed spatial analysis of kinase activity. Multiple plasmids with distinct TEs matched to a specific barcode are co-transfected. MS analysis distinguishes the sensors based on the barcode mass, allowing matching signal intensity of the specific peptide-probe to the respective cellular location.

Fig. 2. Development and validation of a ProKAS sensor specific for ATR using phosphoproteomic data.

A Schematic outlining of the strategy for designing kinase sensors based on phosphoproteomic data and known endogenous substrates. 10-15 sequences surrounding phosphorylation events detected to be dependent on a kinase of interest (KOI) are cloned into ProKAS constructs, which are then expressed in cells to test for kinase sensor detectability, inducibility, and specificity. B Diagram showcasing that ATR is activated by single-stranded DNA damage, after which it preferentially phosphorylates substrates at the S/T-Q motif. Selective ATR inhibitors, such as AZD6738, are used in phosphoproteomic analyses to determine ATR-dependence of each detected phosphorylation event. C Identification of FANCD2 S717 as an ATR-specific phosphorylation site that is also induced by genotoxin camptothecin (CPT) through phosphoproteomic analysis. Phosphoproteome results are included as Supplementary Data 3 and 4. D Cloning of sequence surrounding FANCD2 S717 as an ATR kinase sensor candidate into a ProKAS biosensor featuring a nuclear localization signal. E MS/MS spectrum gathered for phosphorylated ATR sensor, showcasing successful detection of the sensor candidate by MS. F MS analysis showing inducibility and specificity of the ATR sensor candidate after treatment with genotoxin and selective ATR inhibition, respectively. Cells were treated with 1 millimolar HU for 2 hours, and ATR-inhibited cells were treated with 5 micromolar AZD6738 15 minutes prior to HU addition. Error bars in F indicate the mean and standard deviation of triplicate independent experiments. Source data are provided as a Source Data file.

Fig. 3. Pipeline for the computational design and experimental validation of a CHK1-specific ProKAS sensor.

A Overview of the computational approach for generating kinase-specific motifs based on PSPA data. A genetic algorithm approach was utilized to arrive at a cohort of optimized sensor candidates out of the billions of possible sensor peptide sequences. B Workflow for multiplexed screening and selection of MS-detectable kinase sensors from candidates generated and filtered in silico. C Schematic illustrating CHK1 as a downstream effector kinase of ATR, and that CHK1 can be selectively inhibited by CHIR-124. D Selection of 10 candidate sequences for a CHK1 sensor based on PSPA scores as shown in (A). E MS/MS spectra gathered for the phosphorylated form of the indicated CHK1 sensor candidates. Screening sensor with the candidates indicated in (D) were expressed in HEK293T cells treated with HU. F MS analysis showing inducibility and specificity of the CHK1-4 sensor candidate after treatment with genotoxin and selective CHK1 inhibition, respectively. Cells were treated with 1 millimolar HU for 2 hours, and CHK1-inhibited cells were treated with 500 nanomolar CHIR-124 15 minutes prior to HU addition. Error bars in G indicate the mean and standard deviation of triplicate independent experiments. Source data are provided as a Source Data file.

Fig. 4. Multiplexed analysis of DDR kinase activities using ProKAS.

A Schematic illustrating the canonical mechanisms of activation for DDR kinases ATR, ATM, and CHK1. B Design of a triplexed ProKAS construct containing kinase sensors for ATR, ATM, and CHK1. C Validation of kinase specificity of each sensor within the triplexed ProKAS construct via treatment with selective kinase inhibitors for each KOI in the presence of either 1 micromolar CPT or 1 millimolar HU. ATR, ATM, and CHK1 were inhibited with 5 micromolar AZD6738, 50 nanomolar AZD0156, and 500 nanomolar CHIR-124, respectively. D Flowchart showing primary components of the semi-automated high-throughput pipeline enabling larger-scale ProKAS experiments. E MS analysis showing kinase activation dynamics in response to 1 micromolar CPT over 6 hours in HEK293T cells expressing the triplexed ProKAS construct. F MS analysis showing kinase activation dynamics in response to 1 millimolar HU over 6 hours in HEK293T cells expressing the triplexed ProKAS construct. G MS analysis showing inhibitor titration using ProKAS to demonstrate the potency and selectivity of ATM inhibitor AZD0156. Cells were treated with 1 micromolar CPT for 30 minutes to activate ATM. Error bars/envelopes in E, F and G indicate the mean and standard deviation of triplicate independent experiments, except for CHK1 sensor quantification in G which comprises duplicate independent experiments from which no statistical significance has been calculated or displayed. Source data are provided as a Source Data file.

Fig. 5. Spatially-encoded ProKAS for analysis of DDR kinase activities.

A Illustration of nuclear and cytosolic ProKAS biosensors featuring distinct codes for simultaneous monitoring of DDR kinase activities in both locations. B Microscopy showing the localization of ProKAS biosensors containing an NLS or an NES. Experiment was repeated 3 times. C Peak area ratios for DDR kinase sensors showing the proportion of phosphorylated sensor peak area to unphosphorylated peak area. Data based on MS analysis of cells not treated with any genotoxin. D MS analysis of co-expressed NLS- and NES-containing ProKAS biosensors, simultaneously monitoring DDR kinase signaling kinetics in both the nucleus and cytosol in response to 1 millimolar HU and 1 micromolar CPT. The experiments were quantified via SILAC. E Nuclear and cytosolic DDR kinase signaling kinetics were also monitored after treating cells with 10 micromolar gemcitabine (GEM) and 5 micromolar doxorubicin (DOXR). The experiments were quantified via label-free quantification (LFQ). F Microscopy showing the co-localization of the ProKAS biosensor (utilizing PCNA as a targeting element) with EdU foci. The experiment was repeated 3 times. G Densitometry analysis of EdU and ProKAS-PCNA signal across the white line drawn across the nucleus in panel E, showing signal coincidence between the EdU and ProKAS-PCNA foci formed. H MS analysis simultaneously monitoring the effect of HU on ProKAS biosensors containing an NLS or PCNA as the targeting element. I Schematic illustration of different spatial distributions of ATR, CHK1, and ATM kinase activity observed upon replication stress or DSBs. Error bars/envelopes in C, D, and F indicate the mean and standard deviation of triplicate independent experiments. Source data are provided as a Source Data file.

Fig. 6. Expanded multiplexing capabilities via additional codes, sensor and TMT quantification.

A Schematic illustrating an expanded ProKAS biosensor featuring added sensors: one targeted by the ATM kinase and PPM1D phosphatase, and another targeted by CDK1 and 2. A larger cohort of amino acid codes is also depicted, expanded from three to six total codes. B Experimental design combining the expanded cohort of codes with TMT 6plex, allowing for the multiplexed analysis of 36 experimental conditions in one MS sample. Separate cell cultures were treated with drug or vehicle in triplicate, after which lysates with different codes were pooled to perform 6 affinity purifications. Tryptic digests from these purifications were then labeled with different TMT reagents before being pooled into a single sample for analysis via LC-MS/MS. Drug treatments were 2 hours in duration at the following doses: 1 micromolar camptothecin (CPT), 1 millimolar hydroxyurea (HU), 3 micromolar gemcitabine (GEM), 5 micromolar doxorubicin (DOXR), 10 micromolar PPM1D inhibitor (PDi), and 5 micromolar ATR inhibitor (ATRi). C Matrices displaying TMT reporter channel relative abundances for the unphosphorylated and phosphorylated forms of the sensor peptides. D TMT-based MS quantification for each sensor peptide in each code, with each code corresponding to a given drug treatment. Error bars in D indicate the mean and standard deviation of triplicate independent experiments. Source data are provided as a Source Data file.