Cell culture

Human Jurkat cells Clone E6.1 (ATCC TIB-152) were cultured in RPMI-1640 containing 10% fetal bovine serum (FBS) at 37 °C and 5% CO2. Culture medium was refreshed every 2–3 days and cells were kept at densities between 0.5 × 106 and 2 × 106 cells per ml until lysis or drug treatment.

Cell line authentication was accomplished by single nucleotide polymorphism profiling (Multiplexion).

Compound information

The information of the target space of the 144 compounds included in this study was obtained from DrugBank Online (status of July 2023) and vendor specifications. Information about the clinical phase the compounds were in at the time the study was conducted was retrieved from ChemBL (status of July 2023).

Compound treatment

Compounds were prediluted in DMSO and further in culture medium inside a 48-deep-well plate. Per 48-deep-well plate, three DMSO controls were added. For treatment, 4 × 106 cells in RPMI-1640 medium supplemented with 10% FBS were added on top of each compound predilution resulting in a final volume of 2 ml and final treatment concentrations starting from 10 µM to 1 nM in full log10 steps, resulting in five doses for each drug (10 µM, 1 µM, 100 nM, 10 nM, 1 nM). Cells were incubated for 18 h if not stated otherwise at 240 rpm, 37 °C and 5% CO2. The following day, cells were subjected to viability assessment and lysis.

Confluency, viability and metabolic activity assessment

For determination of cell viability and metabolic activity after compound treatment, 100 µl of cell suspension per well were added to a 96-well plate containing 50 µl of IncuCyte Cytotox Dye (250 nM final concentration, Sartorius) and alamarBlue Cell Viability Reagent (10% final concentration (v/v), Invitrogen). The plate was placed into the IncuCyte live-cell analysis system (37 °C and 5% CO2) and cells analyzed for cytotoxicity over a time course of 3 h (×10 magnification, scan type was standard with five images per well, channel selection was phase contrast and fluorescence (300 ms acquisition time), scan interval was every hour). The integrated software of IncuCyte (Basic Analyzer) was used for confluency and cytotoxicity analysis. After 3.5 h, metabolic activity was determined by fluorescence measurement of the AlamarBlue reagent using the fluorescence read out on the microplate reader FluoStar Omega (λex = 544 nm and λem = 584 nm, BMG Labtech).

For confluency and metabolic activity evaluation, the resulting values were normalized to the average values for the DMSO control. For cytotoxicity, the values were corrected for differences in confluency, before normalizing the values to the average values for the DMSO controls. Dose–response curves were fitted to the data as described below (section ‘Curve fitting’).

Any microscopic pictures displayed in the paper or elsewhere were exported from the IncuCyte software as displayed and not further modified.

Cell lysis for protein extraction

To obtain cell lysate from untreated cells (for optimization purposes), cell suspension was centrifuged at 172g for 5 min at room temperature, washed with PBS (phosphate buffered saline, without calcium or magnesium) and pelleted before resuspension in lysis buffer (2% SDS, 40 mM Tris/HCl, pH 8, 95 °C).

Lysis of compound-treated cells was performed in 96-deep-well plates. Therefore, after 18 h of treatment time, 48-deep-well plates were centrifuged (172g, 10 min, 4 °C), supernatant was discarded, cell pellets were resuspended in PBS and transferred to a 96-deep-well plate. Cell pellets were washed two more times with PBS and centrifuged to discard the supernatant before lysis in 100 µl of lysis buffer.

For hydrolysis of DNA, lysate was heated to 95 °C for 10 min while shaking at 172g and trifluoroacetic acid was added to a final concentration of 1% (v/v) and incubated for 1 min while shaking. Subsequently, N-methylmorpholin (NMM) was added for neutralization to the hot lysate to a final concentration of 2% (v/v). Lysate was stored at −20 °C until further use.

Tissue and bacteria sample preparation

Mus musculus (M. musculus) and Arabidopsis thaliana (A. thaliana) tissue samples were snap frozen in liquid nitrogen before homogenization using the TissueLyser II (Quiagen, 5 min, 30 Hz, using one stainless steel bead with a 5 mm diameter). Lysis buffer (4% SDS, 40 mM Tris/HCl, pH 8) was added after removing the bead and samples were sonicated using the Bioruptor Pico (Diagenode, 25 cycles with 30 s on/off). DNA hydrolysis was performed as described above using final concentrations of 2% trifluoroacetic acid and 4% N-methylmorpholin, respectively. Lysates were cleared by centrifugation (60 min, 4 °C, 21,000g). Supernatant lysate was stored at −20 °C until further use.

Escherichia coli (E. coli) and Pseudomonas aeruginosa (P. aeruginosa) were grown in a shaker culture in Luria-Bertani medium at 37 °C, 300 rpm. When reaching an optical density of 0.5 and 0.6, respectively, cultures were harvested by centrifugation (172g, 60 min, 4 °C) and washed twice with PBS. Lysis buffer was added to the pellet, followed by DNA hydrolysis as described above. Lysate was sonicated using the Bioruptor Pico (above) before clearance by centrifugation (60 min, 4 °C, 21,000g). Cleared lysate was stored at −20 °C until further use.

Isolation and sorting of T cells from healthy donors

Thrombocyte-depleted blood samples were obtained from two healthy, voluntary human donors (male, age 26) after they gave written and informed consent. This study was approved by a vote from the ethics committee of the University Hospital München rechts der Isar (564/18S). Sample were transferred into 50 ml Falcon tubes, with each tube containing approximately 15 ml of blood. The Falcon tubes were then filled up to a total volume of 37.5 ml with PBS, and the blood was thoroughly mixed. To isolate peripheral blood mononuclear cells (PBMCs), a 12 ml layer of Pancoll was meticulously underlaid using a 24 ml syringe with a long needle (G 20 × 2 3/4’; Ø 0.9 × 70 mm). Subsequently, the blood samples were subjected to centrifugation using a programmed gradient (acceleration of 7, deceleration of 1, 2, 7g, for 20 min at room temperature). Following the gradient centrifugation, the plasma fraction was discarded, and the PBMC-containing buffy coat was carefully collected. The PBMCs were then washed with 50 ml of PBS using centrifugation (441g, 5 min, at room temperature).

For cell separation, 107 PBMCs were resuspended in 40 µl MACS buffer (PBS, 1% FCS, 2 mM EDTA) and incubated with 10 µl antihuman CD4 beads for 15 min at 4 °C. Subsequently, PBMCs cells were washed with 15 ml of MACS buffer and centrifuged. CD4 T cells were positively enriched with the autoMACS Pro Separator. Flowthrough was collected and used for the isolation of CD8 T cells according to the isolation protocol of CD4 T cells. Isolated primary T cells were cultured in RPMI-1640 containing 10% FBS and 1% penicillin and streptomycin (37 °C, 5% CO2) and were either subjected to HDACi treatment immediately or were activated as described below.

HDACi treatment of peripheral T cells from healthy donors

For each population (CD4+/CD8+) a fraction of cells was activated using Dynabeads Human T-Activator CD3/CD28 for T Cell Expansion and Activation (Invitrogen) and incubated for 48 h (37 °C, 5% CO2) before HDAC inhibitor (HDACi) treatment. Naïve T cells were subjected to treatment immediately after isolation and sorting. Irrespective of activation status, cells were treated with different HDACi (five doses for each drug: 10 µM, 1 µM, 100 nM, 10 nM and 1 nM) for 18 h, followed by viability, confluency and cytotoxicity assessment as described above. Cell lysis, protein extraction followed by proteomic workflow and LC–FAIMS–MS/MS measurement was carried out as described in the respective sections. For samples, where available material was limited, protein input was adjusted for tryptic digestion and obtained peptides were loaded on Evotips and analyzed on an Evosep-FAIMS-Exploris set-up as described previously46 (for a full list of used instrument software, see Supplementary Table 3, Materials).

Transcriptome sample preparation and analysis

For transcriptome analysis, Jurkat cells were treated according to the protocol described above. After 18 h, cells were lysed and total RNA was extracted using the ReliaPrep RNA Cell Miniprep System (Promega), according to the manufacturer’s protocol, and evaluated on a 2100 Bioanalyzer (Agilent Technologies). RNA library preparation occurred with the 3′ mRNA-Seq Library Prep Kit FWD with Unique Dual Indices (Lexogen) and was sent to Lexogen for gene expression profiling. Alignment of obtained reads was done using the data processing pipeline provided by the manufacturer using the QuantSeq FWD pipeline and Homo sapiens (H. sapiens) genome annotation. The obtained alignments were trimmed, reads were counted and normalized. Dose–response curves were fitted to the data as described below (section ‘Curve fitting’).

SP3 sample preparation and tryptic digestion

Protein yield was determined by Thermo Pierce BCA (bicinchoninic acid) protein assays. All steps were performed according to the manufacturer’s protocol.

Before tryptic digest, detergent was removed by single-pot SP3 clean-up, following the protocol first described by Hughes et al.25 adapted to a Bravo Agilent liquid handling platform. In short, lysate containing 200 µg of protein was mixed with 1 mg SP3 beads (50:50 mixture of Sera-Mag carboxylate-modified magnetic bead types A and B (Cytiva Europe)) in a 96-deep-well plate and proteins were precipitated onto the beads in 70% ethanol in ddH2O (double distilled water).

The beads were washed three times with 80% ethanol in ddH2O and once with 100% acetonitrile (ACN). Disulfide bonds were reduced with 10 mM dithiothreitol for 45 min at 37 °C, followed by alkylation of cysteines with 55 mM CAA (2-chloroacetamide) for 30 min at room temperature in 100 µl of digestion buffer (2 mM CaCl2 in 40 mM Tris-HCl, pH 7.8). Trypsin (1:50 (wt/wt) enzyme-to-protein ratio) was added and proteins were digested off the beads at 37 °C and 1,200 rpm overnight. For peptide recovery, the beads were settled on magnets and the supernatant was transferred to a new 96-well plate. Beads were washed by addition of 100 µl 2% formic acid in ddH2O and the supernatant was transferred to the collection plate. Subsequently, the samples were desalted as described below.

Desalting and drying of peptides

Before LC–MS/MS analysis samples were desalted using hydrophilic-lipophilic balanced (10 mg of N-vinylpyrrolidon-divinylbenzol porous particles 30 μm, Macherey-Nagel) 96-well plates using centrifugation at 7g for 1 min until specified otherwise. For this, hydrophilic-lipophilic balanced material was primed with 500 µl of isopropanol, ACN and solvent B (0.1% formic acid in 70% ACN in ddH2O) and equilibrated with 1,000 µl of solvent A (0.1% formic acid in ddH2O) before sample loading (by gravitation, 5 min). The sample flowthrough was reapplied to the plate and bound peptides were washed with 1,000 µl of solvent A. Peptides were eluted with 250 µl of solvent B (3 min, 7g; 1 min, 172g). Samples were frozen at −80 °C, dried by vacuum centrifugation and stored at −20 °C until LC–MS/MS measurement.

High pH reversed-phase fractionation

Here, 50 µg of peptides (A. thaliana for Extended Data Fig. 1i and Jurkat for Fig. 3b and Extended Data Fig. 4d–e) were fractionated by basic pH reversed-phase material (reversed-phase sulfonate cartridge tips; 5 μl of polystyrene-divinylbenzene (PS-DVB) resin, Agilent) into six fractions using the Agilent AssayMAP Bravo pipetting system. The reversed-phase sulfonate cartridges were primed, washed and equilibrated according to the manufacturer’s protocol. Peptides were reconstituted in 100 μl of 25 mM ammonium formate (pH 10) and loaded onto the cartridges. Peptides were fractionated by increasing ACN concentrations (5, 10, 15, 20, 25, 30, 80%). The seven elution steps were either combined into six fractions, combining the 5 and 80% fractions, or into four fractions. For four fractions, the 5 and 25%, the 10 and 30%, the 15 and the 80%, and the 20% ACN fraction and the flowthrough were combined. All fractions were acidified with formic acid to a final concentration of 1%. Samples were frozen at −80 °C, dried by vacuum centrifugation and stored at −20 °C until LC–MS/MS measurement.

Microflow-LC–(FAIMS)–MS/MS measurements

All samples (except where indicated otherwise) were analyzed on a microflow-LC–MS/MS system using a Vanquish Neo ultra high-performance LC system (Thermo Fisher Scientific) coupled to an Orbitrap Eclipse Tribrid mass spectrometer (Thermo Fisher Scientific) with or without installed FAIMS Pro Interface (Thermo Fisher Scientific). For a full list of used instrument software, see Supplementary Table 3, Materials.

Before measurement, samples were reconstituted in 0.1% formic acid, 2% ACN. For system optimization, the peptide concentration was determined using a Nanodrop system (Thermo Fisher Scientific) and the amount of peptide required for each run was injected accordingly. For drug profiling samples, half of the samples were injected per run (50 µg). For fractionated samples everything was injected.

Chromatographic separation was performed via direct injection on a 15 cm Acclaim PepMap 100 C18 column (2 µm, 1 mm inner diameter × 15 cm, Thermo Fisher Scientific) at a flow rate of 50 µl min−1. The column temperature was set to 55 °C. Solvent A was 0.1% formic acid in 3% DMSO in ddH2O, and solvent B was 0.1% formic acid and 3% DMSO in ACN. The gradients for different lengths can be found in Supplementary Table 3, LC gradients.

Incorporation of FAIMS into microflow-LC–MS/MS

Because micro-LC separations generate much sharper peaks than nano-LC, the incorporation of FAIMS into microflow-LC–MS/MS system needed to be evaluated from the bottom up. We first characterized the device for peptide transmission at different compensation voltage (CV) values using a tryptic digest. With these data in hand, we next simulated how many and which CV values should be combined for best proteome coverage. Simulations were experimentally tested using LC gradient lengths between 15 and 180 min and we systematically compared performance with and without FAIMS. For gradient times of 15, 30 and 60 min, only one CV setting can be meaningfully used because CV switching takes substantial amounts of time. Regardless of LC times, FAIMS increased the number of identified protein groups at a given time or halved the MS time needed to obtain the same depth of analysis compared to the same LC set-up but without using FAIMS.

Measurement without FAIMS installed

The OptaMax NG ion source (Thermo Fisher Scientific) with a heated electrospray ionization probe was used to acquire the data. The sprayer was positioned at middle position in the x axis (left to right), at position 1 in the y axis (front to back) and between positions M and L in the z axis (probe height).

The mass spectrometer was operated in data-dependent MS/MS, positive ion mode, using a spray voltage of 3.5 kV, a funnel radio-frequency lens value of 40, an ion transfer tube temperature of 325 °C and vaporizer temperature of 125 °C. The flow rates for sheath gas, auxiliary gas and sweep gas were set to 32, 5 and 0 l min−1, respectively.

A full-scan (MS1) was recorded from 360 to 1,300 m/z with a resolution of 120,000 in the Orbitrap in profile mode. The MS1 AGC target was custom set to 100% and the maxIT was set to 50 ms. Based on the full scans, precursors were targeted for the MS/MS scans (MS2) if the isotope envelope was peptidic (monoisotopic precursor selection), the charge was between 2 and 6 and the intensity exceeded 1 × 104. The MS2 quadrupole isolation window was set to 0.4 m/z. Peptide fragmentation occurred in the ion routing multipole by HCD with a fixed collision energy mode, the collision energy normalized to the precursor m/z and charge with a collision energy of 28%. The MS2 scan was acquired in the Ion Trap with rapid scan rate in centroid mode and a defined first mass of 100 m/z. Specific MS2 properties as well as cycle times for different gradient length can be found in Supplementary Table 3, MS settings.

Measurement with FAIMS installed

The same ion source and probe as above was used, applying the same position setting. The mass spectrometer was operated in data-dependent MS/MS, positive ion mode, using a spray voltage of 4 kV, a funnel radio-frequency lens value of 40, an ion transfer tube temperature of 325 °C and vaporizer temperature of 300 °C. The flow rates for sheath gas and auxiliary gas were set to 40 and 5 l min−1, respectively. FAIMS was operated with standard resolution (inner and outer electrode 100 °C) and a static carrier gas flow of 3.5 l min−1. Measurement parameters were unchanged and the respective FAIMS CV was set to the needed value. For measurements of drug perturbed samples, the 60 min gradient was used with a set CV of −30 V.

If more than one internal CV was used (system optimization), independent experiments were specified for the different CVs in the Tune method with the exact same settings, except for the different CV value (the used CV values can either be read directly from the figures or the raw file names). This leads to the MS looping through the specified experiments of the method, switching after each MS cycle (MS1 scan + MS2 scans). To keep the data points and thus quantification quality stable, the cycle time stated above was divided by the number of used internal CVs resulting in 0.75 s for 60 min (two CVs), 1.4 s for 120 and 180 min (two CVs) and 0.8 s for 120 and 180 min (three CVs).

Database searching

The raw MS data files were processed with MaxQuant v.1.6.2.10 (ref. 27) using the integrated Andromeda search engine and searched against the respective reference database (H. sapiens: downloaded from UniProt containing canonical and isoforms 24 August 2020; 75,776 entries, E. coli: downloaded from UniProt containing canonical and isoforms 1 July 2021; 4,713 entries, P. aeruginosa: downloaded from UniProt containing canonical and isoforms 1 July 2021; 5,563 entries, M. musculus: downloaded from UniProt containing canonical and isoforms 1 July 2021; 25,381 entries, A. thaliana: Araport11 genome release downloaded from Arabidopsis.org containing canonical and isoforms 16 June 2020; 48,359 entries).

Raw files from runs with multiple internal FAIMS CVs had to be split into separate files based on CV values before MaxQuant searches. These separate files were specified as different fractions, as for the basic reverse-phase fractions, of the same experiment in MaxQuant. Multiple injections of the same sample were specified as the same experiment. Standard MaxQuant search parameters were used. Trypsin/P was specified as protease, allowing for up to a maximum of two missed cleavages. Carbamidomethylation of cysteine was specified as fixed modification, while oxidation of methionine and protein N-terminal acetylation were considered as variable modifications. Where specified, mono- and di-methylation of arginine and lysine was enabled as a variable modification. The label free quantification (LFQ) algorithm, with a standard LFQ minimum ratio count setting of 1, as well as the iBAQ (intensity-based absolute quantification) algorithm, with log fit, was switched on where needed. Where used, the Match-Between-Runs algorithm was switched on with default settings (0.7 min and 5 min for matching and retention time alignment window, respectively). The false discovery rate (FDR) was set to 1% on protein and peptide spectral match level. For Prosit rescoring, the FDR was set to 100% on protein and peptide spectral match level. The respective MaxQuant msms .txt and .raw files were rescored by Prosit. Peptides with q values ≤0.01 were retained and proteins were grouped based on the picked FDR method47. For MaxQuant output, proteins for which no unique peptide was found and thus where not distinguishable were aggregated to protein groups. For picked FDR protein group output, proteins are grouped on gene level and only unique peptides are considered. For readability, we refer to all only as proteins in the figures. Data analysis and visualization was performed using R (v.4.1.0) in RStudio (see Supplementary Table 3, Materials for full list of all packages used) and Microsoft Excel 365. Further editing of plots was done in Adobe Illustrator CS6. Information on whether a dataset was rescored or not can be found on MassIVE (Data availability section).

Data processing and analysisCurve fitting

For each protein–drug combination, the LFQ intensity relative to the average protein intensity in the DMSO controls was calculated for all drug concentrations. The same was done for each transcript–drug combination of the transcriptomic data using read counts. For the different viability metrics, the data were prepared as described above. To these normalized data, a sigmoidal four-parametric log-logistic model (equation (1)) was fitted using the dose–response curve R package (v.3.0-1), where x is the log10 of the drug concentration, pEC50 is the negative log of the inflection point of the curve (denoted as the effective concentration 50; EC50), t is the top or low-dose plateau, b is the bottom or high-dose plateau, s is the curve slope between the plateaus and Y(x) is the observed protein ratio compared to the vehicle control at concentration x.

$$Y\left(x\right)=\frac^}_\right)\right)}\right)}+b$$

(1)

For each resulting model, descriptive parameters were extracted and reported. Comprising the optimized slope (s), top (t), bottom (b) and inflection point (EC50), as well as the area under the curve, the coefficient of determination (R2), mean average deviation, the predicted y value of the fitted curve for the highest concentration (end of curve, fold change) and the slope of a linear model fitted to the data.

Curve classification

To avoid manual annotation of >1 million dose–response curves, a random forest classifier was trained using the ranger R package (v.0.14.1). As a ground truth dataset, curves of two compounds were manually annotated as up-, down- and nonregulated. The dataset was split into 80:20 for training and validation dataset, respectively (training 11,562, validation 2,883, total 14,409). The input features were comprised of the values described above, along with the relative LFQ intensities and number of unique peptides for all concentrations and abundance percentile of the respective protein in the DMSO control. After hyper parameter tuning, the final model was trained with 1,200 trees, randomly choosing 15 independent variables at each split and splitting only nodes with a minimum size of 3. Variable importance mode was set to impurity and the Gini split rule was applied. The model’s performance and quality were tested using the validation dataset, calculating precision, confusion matrices and ROC curves. The resulting classifier was used as a prefilter, plotting curves into separate PDFs and writing information into separate .txt files based on the predicted classes, thereby facilitating manual examination of all drug datasets. The same classifier was used for the dose–response curves of the drug perturbed transcriptome dataset. These regulated proteins were further analyzed to explore the mode of action of drugs.

Further filtering

For further analysis, a protein was regarded as up- or down-regulated if it was classified accordingly and the fold change exceeded 1.5 and 0.7 for up- and down-regulation, respectively. The same was applied to all transcripts, additionally retaining only observations where read counts were above 50 for all concentrations.

GO term enrichments

For the heatmap clustering of drugs with similar effects, a GO term enrichment analysis was performed for each drug individually using the clusterProfiler R package (v.4.2.2.)48. Each drug dataset was tested for enrichment of GO terms on all levels (cellular compartment, molecular function and biological process) both in up- and down-regulated proteins with the whole drug dataset as the background. P values were corrected using the FDR approach and the q value cut-off was set to 1. The enrichment results for up- and down-regulation were combined, retaining the more significant entry for duplications. After combining the enrichment results for all drugs, the q values were log transformed, multiplied by −1 for GO terms enriched in down-regulation and z-scored for each GO term individually. The heatmap depicts the combined, preprocessed GO term enrichment results after hierarchically clustering of both rows and columns using Pearson correlation as a distance metric and Weighted Pair Group Method with arithmetic mean as the agglomerative method. The GO term enrichment results displayed in Extended Data Fig. 6a were taken from the global GO term enrichment analysis described above. For Extended Data Fig. 5d a new GO term enrichment analysis was done (P value cut-off, 0.05; P value correction, FDR approach; Subontology, Molecular Function; whole H. sapiens database as background).

Dose-dependent methylation

The search results for lysine and arginine methylation were prepared for dose–response curve fitting similar to the process described for proteins and transcripts above. However, for each peptide–concentration–inhibitor combination the intensity ratio of methylated to unmethylated version was calculated. The resulting value in turn was then normalized to the respective DMSO control before continuing as described above (section ‘Curve fitting’).

Simulation of target coverage in relation to proteomic depth

For the simulation of target coverage over captured proteomic depth we ranked all >8000 proteins of this study by their mean iBAQ values in all DMSO controls in a descending fashion. To simulate the different proteomic depths, this list was cut at the indicated ranks (number of identified proteins). For each drug, we checked in turn how many of its targets were included in the resulting list and calculated the fraction of designated targets that were detected.

Replicate analysis

For the volcano plot displayed in Extended Data Fig. 2b assessing the quantitative reproducibility, the 48 DMSO controls were randomly assigned into two equally sized groups. After median centering normalization of the LFQ intensities of the picked FDR gene group output and filtering for completeness in the dataset, a two-sided Student’s t-test was performed for all 4,694 proteins. P values were corrected for multiple hypothesis testing using the FDR approach using the R package fdrtool (v.1.2.17).

For the comparison of quantitative reproducibility between unregulated and regulated proteins using the five individual doses for each inhibitor as replicates, the LFQ intensities of the picked FDR gene group output were normalized by median centering. The CoV was calculated across the five doses for each drug for each protein that was either classified as up- or down-regulated, or unregulated.

To assess the reproducibility of EC50 determinations, the curves for each protein for each drug replicate were fitted as described above. For proteins being classified as up or down-regulated in three out of four replicates per drug, the standard deviation of the pEC50s was calculated.

Real-time RT–qPCR

For RT–qPCR analysis, cells were treated according to the protocol described above. After 18 h cells were lysed, and total RNA was isolated using the Monarch Total RNA Miniprep Kit (New England Biolabs) according to the manufacturer’s instructions. RNA yield was determined using the Qubit fluorometer (Thermo Fisher Scientific). Complementary DNA (cDNA) was generated from 2 µg of RNA from each sample using the LunaScript RT SuperMix Kit (New England Biolabs) according to the manufacturer’s protocol. Additionally, no-reverse transcriptase controls were generated for each sample during the reverse transcription step. After reverse transcription, the cDNA was diluted ~66 fold with nuclease-free ddH2O. qPCR was performed in triplicates on a CFX384 Touch Real-Time PCR Detection System (Bio-Rad Laboratories, Inc.) using 10 ng of cDNA per sample, the Luna Universal qPCR Master Mix (New England Biolabs) and the primer pairs as shown in Supplementary Table 3, Primer list. No-reverse transcriptase controls were measured in pools of all samples on each plate. Nuclease-free ddH2O was used as the nontemplate control for each assay. Cycling parameters were set to 95 °C (1 min), 40 cycles of 95 °C (15 s) and 60 °C (30 s with plate read on SYBR channel) each, and finally a melt curve was recorded from 60 to 95 °C with an increment of 0.5 °C per 5 s and SYBR channel plate reads after each increment. All samples treated with the same drug as well as the DMSO control were measured on the same plate.

Analysis of RT–qPCR results

Quantification cycle (Cq) and melting temperature (Tm) values were determined in the CFX Manager v.3.1 software (Bio-Rad Laboratories, Inc.). The regression method of the software was used for Cq assessment with baseline correction and curve fit turned on. The fold change in expression after treatment and the ratio of truncated to full-length transcript were calculated in Microsoft Excel 365 from the mean Cq values for each sample using the 2-∆∆Cq method49.

T cell activation assay

Activation potential of HDACi treated Jurkat cells was analyzed using TCR and/or CD3 effector cells (nuclear factor of activated T cells or NFAT) from a T Cell Activation Bioassay (Promega) with slight adaptations of the manufacturer’s protocol. Briefly, TCR/CD3 effector cells (NFAT) were incubated with HDACi (five doses for each drug: 10 µM, 1 µM, 100 nM, 10 nM and 1 nM) for 16 h, followed by unspecific activation via CD3 and/or CD28 using the Human Anti-CD3/CD28 T Cell activation Kit (Cell Signaling Technology). After 5 h, the receptor-mediated signaling was read out by luciferase activity on a microplate reader FluoStar Omega (BMG Labtech). Thereby the strength of the luminescence signal corresponded to the strength of receptor-mediated signaling. To determine the strength of T cell activation, the luminescence signals were normalized to the DMSO control. Dose–response curves were fitted to the data as described in the section ‘Curve fitting’.

T cell aggregation analysis

Using the bright light images of living activated human T cells, acquired using the IncuCyte live-cell analysis system as described above, cell aggregates were assigned and quantified (count and area in µm2). To this end images were processed by ilastik50, a supervised machine learning image analysis tool kit. The average aggregate size was calculated for each image by summing up the detected aggregate areas and dividing by the count of aggregates per image, treating the five images acquired per well as replicates. To assess statistical significance of the HDACi induced reduction of average aggregate size, an analysis of variance test was performed for each inhibitor individually, followed by a Tukey honest significant differences post hoc test.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.