SLAS Discovery

Protein degradation mediated by PROteolysis TArgeting Chimeras (PROTACs) is a complex, dynamic, and multi-step process. The structural diversity of PROTACs, coupled with cellular heterogeneity, introduces considerable variability at each stage. Therefore, a straightforward yet comprehensive approach is essential to effectively dissect these mechanisms. The existing methods for evaluating PROTAC degradation are often time-consuming, labor-intensive, or expensive, and many capture only a partial view of the entire degradation process. Consequently, multiple complementary assays are typically required to obtain the complete picture. Here, we use commercially available Cyclin dependent Kinase 6 (CDK6) targeting PROTACs to demonstrate that a simple, single-step, no-wash, additive-free, GFP-based live-cell imaging approach can rapidly generate dynamic degradation metrics (Dmax, Rmax, TDmax, CDmax, DC50, Km, Tmax, Kdeg, Vmax, DT50, {tau} and TRmax) for comparing and ranking PROTACs within a controlled reporter system. The rapid assay turnaround time, excellent assay qualities and compatibility with high-throughput (HT) formats will facilitate quick hit identification and serve as a guiding assay for hit-to-lead optimization campaigns.

The cytosolic histone deacetylase 6 (HDAC6) plays a key role not only in cancer but also in neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) peripheral neuropathies. Pharmacological inhibition as well as genetic silencing of HDAC6 is able to rescue several defects. Therefore, developing pharmacological compounds targeting this enzyme is of crucial interest. Here, we report the design, synthesis, and characterization of Methyl Fluorescent Hydroxamic Acid (MeFluHyA), a novel fluorescent HDAC6-selective probe designed for cell imaging. By integrating a Cy5 fluorophore into a phenyl hydroxamic acid scaffold, MeFluHyA shows binding to the catalytically active CD2 domain of HDAC6 without significantly inhibiting its deacetylating function in the sub-micromolar range. Biochemical enzymatic activity assays confirmed its selectivity over other HDAC isoforms. Fluorescent imaging studies in HeLa cells demonstrated strong colocalization with HDAC6-eGFP and a commercial HDAC6 antibody. Competitive binding assays revealed that MeFluHyA effectively identifies known HDAC6 inhibitors, with reduced probe-binding serving as a readout for successful target engagement. MeFluHyA is a cell-permeable, fast and easy to use probe that can be added organism-independent, and its red-shifted fluorophore makes it compatible with multiplex, live and fixed imaging. Overall, we provide a novel tool for screening platforms aimed at identifying novel HDAC6-targeting inhibitors.

Protein-based fluorescent sensors are a powerful addition to the biology toolbox for their ability to be stably expressed within living organisms, tissues, cells, and subcellular compartments, with the capacity to report on the presence of specific target molecules or other analytes. At the same time, sensor components will unavoidably present opportunities for unintended interaction with endogenous cellular machinery, potentially confounding both sensor function and cell health. Interactions with host components may not be readily predictable during the sensor design process, especially when simultaneously optimizing many other sensor parameters such as fluorescence response, dynamic range, and kinetics. Characterizing effects of sensor expression on cells is currently a laborious ad hoc process; new methods to characterize the cell expression effects of sensors and their variants could dramatically improve sensor design pipelines, laying the groundwork to recognize potentially problematic expression side effects earlier in the iterative design and testing workflow. Here, we take a dual high-content imaging-based and biochemical approach to examine sensor interactions with native cell biology, focusing on the widely used GCaMP calcium sensor. We identify a morphology-based signature of the cellular effects of high sensor expression in a neuroblastoma cell line. Subsequently, we identify biochemical interactions between GCaMP and a component of the mammalian cytoskeleton and track morphological features in sensor-expressing cells that lack these structural components. Our findings present an entry point for engineering new minimally cross-reactive sensor versions given a contextual biological understanding of sensor overexpression. We anticipate that as this and related workflows are incorporated into sensor engineering pipelines, bioorthogonality can be more systematically assessed and prioritized in diverse sensor scaffolds.

Antimicrobial resistance (AMR) represents a major global health threat, with plasmid-borne mcr genes driving colistin resistance and exposing critical gaps in One-Health surveillance across human, animal, and environmental reservoirs. The most prevalent variant, mcr-1, remains difficult to monitor in resource-limited settings due to the lack of rapid, affordable, and field-deployable molecular tools. Here, we developed C12amcr, an integrated molecular toolbox that combines pre-amplification PCR with a fluorescent CRISPR-Cas12a assay targeting a conserved region of mcr-1 and a custom low-cost, hand-held 3D-printed portable fluorometer. Under optimized conditions, the assay achieved a limit of detection of 630 cells/mL. In poultry feces spiked with mcr-1-positive E. coli, C12amcr detected as few as 1,800 cells/mL. When tested on 22 community-derived E. coli isolates, the assay showed 100% concordance with both next-generation sequencing for mcr-1 detection and phenotypic colistin susceptibility testing by broth microdilution. The accompanying portable fluorometer performed equivalently to a laboratory microplate reader while enabling fully decentralized workflows compatible with portable PCR platforms. By integrating locally produced molecular reagents, straightforward protocols, and an accessible field-ready fluorescence reader, C12amcr overcomes key barriers to decentralized AMR surveillance and provides a practical, scalable solution for One-Health monitoring in resource-limited settings.

Thermal proteome profiling (TPP) measures drug target engagement across the proteome by detecting ligand-induced changes in protein thermal stability, but conventional workflows are laborious and costly, which has limited their use in compound screening. We present DASH-PISA, a single-well thermal fractionation approach in which a lysate is heated through a series of defined temperatures in one well and sampled at each step, and the aliquots are pooled into another well. Because sampling and pooling both take place in plate format, the thermal treatment runs on a standard liquid-handling robot with an integrated thermocycler and a 96-channel pipette. Combined with filter-based separation of soluble protein, data-independent acquisition (DIA), and a spike-in SILAC reference, DASH-PISA forms a fully automated, high-throughput TPP workflow. As a proof of concept, stepwise single-well fractionation reproduced the melting curves of traditional TPP, and the pooled DASH-PISA workflow detected known kinase targets of staurosporine; a spike-in SILAC reference further improved their recovery. By running on commercially available automation, this workflow makes proteome-wide target identification practical at the scale required for compound-library screening.

The dopamine D2 receptor is a principal target of antipsychotic and antiparkinsonian drugs, yet well-characterized fluorescent ligands for the D2 receptor remain scarce compared with its relative, the D3 receptor. Here we show that fluorescent ligands developed for the D3 subtype can be repurposed as tools for studying the D2 receptor pharmacology. Using a machine-learning-assisted, live-cell fluorescence microscopy assay, we screened twelve fluorescent probes and identified eight ligands that bound specifically to the D2 receptor at 10 nM. Four ligands exhibited classical saturable binding, yielding Kd in the nanomolar range, whereas the remaining probes displayed unsaturated, time-dependent binding profiles. Kinetic analyses revealed substantial differences in association and dissociation behavior, highlighting the importance of time-resolved measurements. Competition binding with dopamine and haloperidol yielded affinity estimates consistent with literature values. These results expand the toolkit of validated D2 receptor fluorescent probes and support cross-receptor repurposing as a strategy for fluorescent ligand development.

"Undruggable" proteins without surface-accessible binding sites pose significant challenges to target-based drug discovery. Innovative approaches are needed to tackle these proteins. One promising strategy is targeting them in their nascent chain form at the ribosome, where they have a different conformation than in the folded form. A systematic approach to screen for such compounds has however been lacking. Here, we present a high-throughput assay to identify small molecules that specifically inhibit a protein of interest in its nascent-chain form. The assay employs a human in vitro transcription/translation system and monitors expression of the protein in real time via fluorescence detection. Specific inhibitors for the nascent chain of interest can then be identified by comparison with a counter screen. The assay was optimized to maximal sensitivity and reaction costs of ~$0.01 per well, enabling large-scale screens. We validated performance with the reference compound PF846 on the nascent chain of the protein PCSK9. Feasibility for high-throughput screening was demonstrated using a library of 1,760 compounds against the oncogenic KRAS variant A146T and the protein ApoC3, with mean Z' scores of 0.69 and 0.88, respectively. Sixteen global translation inhibitors were identified in each campaign, while no compounds met the criteria for POI-selective inhibition. The assay thus provides a robust platform for larger-scale screening campaigns.

Macrocyclic peptides have gained increased attention amid claims they are a "Goldilocks" therapeutic modality that can encode the selectivity of a biologic in a footprint close to that of a small molecule. Here we attempt to find a peptide that binds selectively to NRAS, sparing HRAS and KRAS, while accessing the cytosol via passive cell permeability. To do so, we combine subtractive affinity selection with mRNA display to identify Compound 1, an 11mer macrocyclic peptide which binds NRAS at a novel allosteric site between Helix 3 and Helix 4 of the GTPase domain. Compound 1 has total isoform selectivity and can be tuned to achieve activation-state selectivity with a single amino acid change. While it has preferential affinity for oncogenic NRAS-specific mutations, it does not inhibit NRAS function or achieve passive membrane permeability.

High-throughput enzymology enables quantitative characterization of enzyme function across hundreds to thousands of sequence variants and experimental conditions. Nevertheless, the scale and complexity of these datasets create substantial challenges for analysis and quality control. High-Throughput Microfluidic Enzyme Kinetics (HT-MEK), for example, generates large microscopy datasets that must pass through multiple analytical stages, including image processing, initial-rate fitting, and kinetic modeling. Choices or errors made at any stage can propagate into the final kinetic parameters without being evident from fit statistics alone. Here, we provide a broadly applicable guide for analyzing high-throughput enzymology data from the HT-MEK platform, using Michaelis-Menten kinetics as a representative example. We first outline the conceptual workflow from raw fluorescence measurements to estimates of kcat, KM, and kcat/KM. We then discuss common experimental and analytical failure modes and present a framework for deciding when data should be refitted, filtered, qualified, or repeated. Finally, we provide a step-by-step workflow using Mercury, an open-source Python framework that integrates scalable HT-MEK data processing with traceable quality control and diagnostic visualization. This workflow preserves the connection between reported parameters and their underlying measurements and can be adapted to other kinetic models and biochemical assays.

Glycine transporter 1 (GlyT1) is a clinically investigated target whose conformational plasticity enables ligands to engage distinct extracellular and intracellular binding sites. However, methods to directly quantify intracellular binding remain limited and largely rely on radioligand-based approaches. Here, we report a NanoBRET-based binding assay to quantify intracellular GlyT1 target engagement in living cells using a fluorescent tracer derived from the GlyT1 inhibitor Cmpd1. With a structure-based approach followed by pharmacological characterization we identified a suitable meta-substituted exit vector for linker and fluorophore attachment while maintaining GlyT1 binding. A series of fluorescent probes was synthesized and evaluated, leading to the development of GlyT1-probe which displayed high-affinity binding and enabled robust NanoBRET measurements. Unlike the functional uptake assay, the NanoBRET assay distinguished intracellular from extracellular GlyT1 inhibitors and resolved structure-activity relationships at the intracellular binding site. This assay provides a quantitative approach to interrogate conformation-selective GlyT1 target engagement in living cells, facilitating the characterization of GlyT1 ligands targeting the intracellular site.

To explore adapting qPCR systems for end-point nucleic acid quantification using dyes such as SYTO-9, we quantified serial dilutions of DNA and RNA standards in the range of 0.75 - 200 ng/{micro}l on 384-well qPCR devices. SYTO-9 fluorescence was successfully measured using standard SYBR Green settings. Blank-subtracted relative SYTO-9 signal showed a logarithmic dependence on DNA/RNA concentration (R2 > 0.95). Measurements were highly stable with different incubation times, temperatures of up to 95{degrees}C, and photobleaching. The described approach is a valuable QC option for high-throughput DNA/RNA isolations and could be adapted to additional fluorometric assays beyond nucleic acids.

Sequence-specific cleavage of viral RNA by deoxyribozymes offers a direct approach for suppressing viral gene expression, although limited cellular accessibility remains a major barrier to their functional application. In this study, a panel of eight deoxyribozymes representing different catalytic motifs and target regions was designed against SARS-CoV-2 Spike RNA and comparatively evaluated through time-dependent in vitro cleavage assays. Substantial target-site-dependent differences were observed even among candidates sharing the same catalytic core, demonstrating that catalytic performance was strongly influenced by the selected RNA target region. Among the tested sequences, 8-17-769 exhibited the fastest and most reproducible cleavage profile and the highest observed rate constants, and was therefore selected as the catalytic module for subsequent chimera development. To evaluate its activity in a cellular context, 8-17-769 was combined with the nucleolin-binding aptamer AS1411 in two opposite linear orientations. In an A549-based Spike expression model, both AS1411-containing chimeras were associated with reduced S gene expression, with 8-17-769-AS1411 producing the strongest response, corresponding to an approximately tenfold reduction relative to the reference group and reaching statistical significance (p < 0.05). The functional difference between the two orientations was further examined structurally. Circular dichroism spectroscopy showed that 8-17-769-AS1411 more closely preserved the spectral characteristics of the individual components, while computational analyses indicated a dynamic organization more similar to the free deoxyribozyme-RNA complex and a nucleolin-compatible docking configuration. Collectively, these findings identify 8-17-769 as a lead Spike RNA-cleaving deoxyribozyme and demonstrate that the linear organization of aptamer and catalytic modules can substantially influence the structural and functional properties of aptamer-deoxyribozyme chimeras.

Surface plasmon resonance (SPR) and biolayer interferometry (BLI) are well-established label-free analytical technologies used to determine biomolecular interaction rates (ka, kd) and equilibrium constants (KD). While assays historically rely on multi-cycle kinetics (MCK), this approach requires intermediate surface regeneration, risking sensor degradation and increasing reagent consumption. Single-cycle kinetics (SCK) avoids regeneration by sequentially injecting increasing analyte concentrations. However, implementing SCK workflows on biosensor platforms like the OctetRR8 BLI instrument (Sartorius) remains a challenge due to the lack of built-in or online data analysis tools. To address this, we introduce SCKAnalyzer, a free, user-friendly web application designed for processing and fitting SCK datasets. SCKAnalyzer accepts column-based text files and native raw BLI data (.frd format) from Sartorius instruments, performing data pre-processing and non-linear fitting to a 1:1 interaction model. Additionally, it offers advanced simulation capabilities that allow variable association and dissociation durations across sequential injection steps, a feature absent in existing platforms such as BiacoreTMSimul8. The performance of SCKAnalyzer was experimentally validated using a 3-step SCK method (SCK3) for DNA/RNA duplex formation on a Biacore T200, as well as 4-step (SCK4) and 5-step (SCK5) methods for DNA/DNA duplex formation on an OctetRR8 BLI instrument. Binding parameters (ka, kd, KD) derived from SCKAnalyzer showed excellent agreement with values from established proprietary softwares such as Biacore T200 Evaluation Software v3.2.1 and BIAevaluation v4.1.1. SCKAnalyzer provides a practical alternative to MCK, reducing probe consumption and extending SCK utility across biosensor platforms. SCKAnalyzer is available at sck.iecb.u-bordeaux.fr (source code: github.com/synthaze/sckanalyzer).

Laboratory automation is increasingly being adopted in academic research to improve throughput, reproducibility, and integration with computational workflows. A common bottleneck in molecular biology workflows is the isolation of DNA, typically performed with spin-column kits relying on centrifugation or vacuum. Existing automation solutions depend on magnetic separation systems requiring costly additional modules and specialist kits, making them impractical for laboratories that perform large numbers of extractions only occasionally. Here we present an open-source, 3D-printed vacuum manifold compatible with the Opentrons family of liquid handling robots that enables automated spin-column DNA isolation for up to 24 samples per run (96 with four manifolds in series). The manifold can be manufactured for approximately $2 using standard ABS filament and is compatible with widely available spin-columns, requiring no additional specialist reagents. Using this system, hands-on time for 24 plasmid minipreps was reduced by 80% compared to manual centrifugation and vacuum protocols. The automated workflow demonstrated comparable or greater plasmid yields and eliminated failed recoveries. All design files, labware definitions, and Opentrons protocols are released under the CERN Open Hardware Licence or MIT License. This device lowers the cost barrier to automated DNA purification for academic laboratories worldwide.

Numerous CRISPR-Cas systems have been developed for molecular detection of genetic elements exploiting the trans-cleavage activity of LbCas12a combined with short fluorescent probes. Alongside, a large variability of buffer conditions has been reported. However, how solution chemistry balances enzymatic turnover, signal stability, and detection performance have not been systematically defined. Here, we evaluated how the interplay between anions (chloride vs. acetate), additives, sequence probes, and ionic strength contributes to LbCas12a trans-cleavage activity. Buffer composition screening revealed that chloride-based buffers maintain a low background but slow catalytic rates, meanwhile acetate-based buffers accelerate enzymatic turnover with and without the target DNA sequence, increasing non-specific signals. Reducing agents and surfactants were the best enhancer combinations, improving cleavage performance by increasing ternary complex formation (Km). High salt concentrations only slightly improved the reaction kinetics. Finally, the 5FAM-TTATT-3BHQ-1 reporter showed the best balance between signal intensity and nonspecific background. Our findings indicate that LbCas12a tolerates a vast buffer composition landscape, maintaining robust trans-cleavage activity and detection performance. However, conditions that enhance catalytic activity simultaneously increase nonspecific probe cleavage and higher background, resulting in lower detection performance. Therefore, assay optimization should prioritize detection performance rather than maximal fluorescence output, where solution chemistry is a critical determinant.

Pairing label-free microscopy with virtual staining could reduce the cost and experimental burden of fluorescence microscopy, but its impact is conditional on generalizable inference. Most virtual staining studies assess performance using image quality assessment (IQA) metrics developed for natural images, yet how well these metrics translate to microscopy remains unknown. Here, we examined the behavior of seven commonly-used full-reference training objectives and metrics, MAE, PSNR, SSIM, foreground PSNR and SSIM, LPIPS, and DISTS, under controlled image degradation and realistic out-of-distribution virtual staining. We applied graded intensity, textural, and morphological transformations to Cell Painting images spanning 18 cell lines, seeding densities, and fluorescence channels. Channel, cell line identity and seeding density explained substantial metric variation after controlling for degradation magnitude. DISTS and foreground metrics showed more favorable balance between degradation sensitivity and biological invariance, although no metric reported performance independent of biological context. Incrementally degrading images and evaluating concomitant metric degradation further revealed that most metrics used only a small fraction of their nominal numerical ranges and frequently plateaued while image degradation visibly continued. We next trained three popular virtual staining model architectures (UNet, WGAN-GP, UNeXt) on five U2-OS seeding densities separately, and computed metrics on model predictions across 17 unseen cell lines. We observed that architecture and training U2-OS seeding density together explain less than 2% of metric variation. Visual inspection suggested comparable scores across cell lines correspond to qualitatively distinct errors, such as differences in cell morphology and marker intensity. These findings show that conventional IQA metrics do not effectively translate to virtual staining applications. Selection or optimization of virtual staining models against real application such as in label-free high content drug screening should instead be approached in an application-oriented fashion.

The base excision repair pathway removes small base lesions from DNA and is initiated in humans by one of eleven DNA glycosylases with overlapping substrate specificities. Despite their central role in genome maintenance and transcription, systematic datasets describing small-molecule binders or inhibitors of DNA glycosylases are lacking. Here, through a collaborative effort across Scandinavia, we establish a high-throughput biochemical assay platform for human DNA glycosylases and use it to generate a systematic small-molecule screening dataset. A chemogenomic library was screened against nine glycosylases with assays of sufficient quality for hit identification, yielding primary screening and concentration-response data and identifying multiple previously unreported inhibitors. All primary and processed data, along with detailed assay protocols and metadata, are publicly available to support reuse in chemical biology, further compound optimization, assay development and comparative studies of DNA repair enzymes.

Circular single-stranded DNA (cssDNA) is a versatile biomolecule with applications spanning genome editing, DNA nanotechnology, synthetic biology, molecular diagnostics, and aptamer development. Compared with linear single-stranded DNA, cssDNA offers enhanced structural stability, resistance to in-cellulo degradation by exonucleases and enables the generation of long, sequence-defined DNA molecules that are difficult to obtain through conventional chemical synthesis methods. Despite its growing utility, widespread adoption of cssDNA has been limited by the lack of accessible, scalable, and cost-effective production methods, with many existing workflows relying on specialised reagents, extensive optimisation, or commercially synthesised DNA. Here, we present a streamlined, end-to-end protocol for the laboratory-scale production of high-purity cssDNA using an M13 phagemid-based system and standard molecular biology laboratory infrastructure. The workflow encompasses bacterial culture, phage amplification, nuclease treatment, phage precipitation, anion-exchange purification, and quality control, with practical optimisations to improve yield, reproducibility, and scalability. Using this approach, yields range from 120-195 {micro}g of purified cssDNA from 300 mL of culture supernatant. The protocol provides detailed guidance on critical process parameters, troubleshooting, and quality assessment, enabling reliable production of cssDNA suitable for a wide range of downstream molecular biology and genome engineering applications.

The primary question in this work is whether a turbidity trace contains more than one resolved kinetic event before any rate or aggregate-mass information is supplied. A blind, three-gate hierarchical Gompertz procedure applied to human interleukin-1 receptor antagonist (IL-1ra) at 53 {degrees}C in phosphate retained one phase at 1 and 2 mg/mL, but selected two phases at 5 and 10 mg/mL. Thus the first resolved multiphase behavior appears at the low protein concentration of 5 mg/mL. Source optical-rate markers were withheld until after selection. Those observed optical rates were then compared with blind-fit phase rates, and the same comparison was expressed on a conditional mass-equivalent scale using the published 5.3-fold Type-I/Type-II turbidity response ratio. A separate 50 {degrees}C citrate series (4, 6, 8, 10, and 14 mg/mL) also selected two phases in every retained trace; its printed Vmax values were used only as post-selection comparators. Finally, two challenging 40 {degrees}C high-concentration traces (180 and 200 mg/mL) were digitized from the instrument-output plot and each retained two phases. All three conditions are analyzed independently. Blind recognition is therefore the central result; the mass-equivalent conversion is a secondary, post-selection interpretation of optical phase rates. The absolute {micro}M/min scale remains conditional on an endpoint aggregate fraction measured elsewhere.

Multispectral imaging is a cornerstone of chemical imaging, with bioluminescence, fluorescence, and phosphorescence imaging underpin much of preclinical and analytical chemical measurements. However, conventional implementations are typically proprietary, costly, and resolve spectral content from the reflectance of a broadband source, without control over the excitation spectrum. This architecture cannot isolate excitation-dependent photophysical processes. Spectral discrimination through sequentially resolved narrowband excitation, detected on a single broadband-sensitive camera, instead enables excitation-dependent fluorescence, phosphorescence, and reflectance measurements not available to such broadband approaches. We describe AURORA-MSI, an open-source multispectral platform that inverts this arrangement with fourteen narrowband LEDs spanning 367-940 nm, a 120-element annular illumination ring with eight independently addressable azimuthal sectors, and a thermoelectrically cooled monochrome CMOS camera. Radiometric calibration, spatial uniformity mapping, and camera noise characterization establish the quantitative measurement foundation. In bioluminescence imaging, AURORA-MSI localized sources in a calibrated tissue-mimicking mouse phantom comparably to a commercial Revvity IVIS Spectrum, detected luciferase-expressing HSJD-GBM1-001 glioblastoma cells across a dilution series, with reduced replicate consistency at the lowest densities, and mapped substrate-free fungal-pathway emission in an intact bioluminescent Petunia hybrida, none requiring photon-counting instrumentation. Time-resolved phosphorescence decay imaging of six inorganic phosphors over 33 minutes found tri-exponential models adequate at early times, while distributed-lifetime models were preferred at extended timescales. Multispectral image-texture features extracted across all fourteen excitation bands discriminated ten pharmaceutical and household powders spanning distinct chemical compositions and, in two cases, distinct formulations of the same compound. Generality beyond these regimes was established through dual-excitation fluorescence fingerprinting of fifteen mineral specimens and wavelength-selective plant-tissue imaging exploiting UV and NIR penetration-depth differences. Excitation-side spectral encoding enables photophysical measurements that detection-side systems with uncontrolled broadband illumination cannot isolate, and the cooled-camera architecture supports weak-signal modalities without photon-counting instrumentation.