High-throughput Screening Technologies Research Articles

A machine learning based framework for breast cancer prediction using biomarkers

<p>Breast cancer is the most frequent cancer in women and the second-leading cause of cancer-related deaths globally. The main problems in managing breast cancer are high heterogeneity and the formation of therapeutic resistance. White blood cells, omics and large Wisconsin diagnostic breast cancer datasets present the three-decade genomic revolution and advance the understanding of cellular function. The precision of cancer diagnosis has also increased over the past decades. High throughput sequencing, screening, and artificial intelligence technologies have significantly improved and increased the methodologies used for diagnosis, prognosis, and therapy. This paper follows several phases of breast cancer, studies datasets and evaluate many algorithms of machine learning (ML) used for analysis and feature selection i.e. k-means, similarity correlation, genetic algorithm, and principal component analysis, have been used to recognize the subset of proteins with the highest significance for breast cancer prediction by using different biomarkers. The best correlation, as determined by Pearson correlation, between copy number and protein is 0.014, and the accuracy achieved by the genetic algorithm is 93.5% using multi-omics datasets.</p>

High-throughput screening for optimizing adoptive T cell therapies.

Adoptive T cell therapy is a pivotal strategy in cancer immunotherapy, demonstrating potent clinical efficacy. However, its limited durability often results in primary resistance. High-throughput screening technologies, which include both genetic and non-genetic approaches, facilitate the optimization of adoptive T cell therapies by enabling the selection of biologically significant targets or substances from extensive libraries. In this review, we examine advancements in high-throughput screening technologies and their applications in adoptive T cell therapies. We highlight the use of genetic screening for T cells, tumor cells, and other promising combination strategies, and elucidate the role of non-genetic screening in identifying small molecules and targeted delivery systems relevant to adoptive T cell therapies, providing guidance for future research and clinical applications.

An iridium(III) complex-based luminogenic probe for high-throughput screening of hydrogen sulfide donors in living cells.

The scarcity of suitable high-throughput screening technology for hydrogen sulfide (H2S) donors has hampered the discovery of H2S donors. In this study, a long-lived cyclometalated iridium complex was rationally designed as a mitochondria-targeted H2S probe to monitor the real-time dynamic change of H2S. By using the time-resolved emission spectroscopy (TRES) technique, an anti-interference high-throughput screening system was developed to monitor H2S in living cells with decreased false negative results. As a proof-of-concept, three natural products were identified as potential H2S donors from a natural product library using the developed TRES probe. Notably, the discovery of allicin and diallyl trisulfide demonstrated the feasibility of this screening platform, while garlic-derived allyl methyl sulfide was explored as a H2S donor candidate. The results were further validated by a commercial assay. We anticipate this high-throughput platform could facilitate the discovery of H2S donors by discriminating the endogenous interfering fluorescence from biological systems.

Anti-OAcGD2 antibody in combination with ceramide kinase inhibitor mediates potent antitumor cytotoxicity against breast cancer and diffuse intrinsic pontine glioma cells.

O-acetylated GD2 (OAcGD2) is a cancer-related antigen that is currently being explored for therapeutic use. Exploring the intricate mechanisms behind OAcGD2 synthesis in cancer cells has long been a challenge. Leveraging state-of-the-art high-throughput RNAi screening and confocal imaging technologies, our study delves into the genetic network orchestrating OAcGD2 synthesis in breast cancer cells. By conducting a comprehensive siRNA screen targeting the OAcGD2 phosphatome/kinome, we identified 43 genetic modulators, with 25 downregulating and 18 upregulating OAcGD2 synthesis. Among these, our study focused on CERK, the gene-encoding ceramide kinase, a pivotal player in glycosphingolipid metabolism. Through meticulous experimentation utilizing anti-CERK inhibitor and siRNAs, we made a significant discovery: CERK inhibition robustly upregulates OAcGD2 in both neuroblastoma and breast cancer cells, concurrently dampening cell migration. Furthermore, our findings highlight an exciting prospect: augmenting the antibody-dependent cell cytotoxicity of the chimeric human/mouse anti-OAcGD2 IgG1 monoclonal antibody (c8B6 mAb) against breast cancer and diffuse intrinsic pontine glioma cell lines in combination with specific CERK inhibitors. These results underscore the pivotal role of CERK inhibition in bolstering OAcGD2 synthesis, thus, presenting a promising strategy to increase the efficacy of anti-OAcGD2-based immunotherapy in patients with neuroectodermal tumors. By shedding light on this intricate interplay, our study paves the way for innovative therapeutic strategies poised to revolutionize the treatment landscape for these aggressive malignancies.

Exploring the Artificial Intelligence and Its Impact in Pharmaceutical Sciences: Insights Toward the Horizons Where Technology Meets Tradition.

The technological revolutions in computers and the advancement of high-throughput screening technologies have driven the application of artificial intelligence (AI) for faster discovery of drug molecules with more efficiency, and cost-friendly finding of hit or lead molecules. The ability of software and network frameworks to interpret molecular structures' representations and establish relationships/correlations has enabled various research teams to develop numerous AI platforms for identifying new lead molecules or discovering new targets for already established drug molecules. The prediction of biological activity, ADME properties, and toxicity parameters in early stages have reduced the chances of failure and associated costs in later clinical stages, which was observed at a high rate in the tedious, expensive, and laborious drug discovery process. This review focuses on the different AI and machine learning (ML) techniques with their applications mainly focused on the pharmaceutical industry. The applications of AI frameworks in the identification of molecular target, hit identification/hit-to-lead optimization, analyzing drug-receptor interactions, drug repurposing, polypharmacology, synthetic accessibility, clinical trial design, and pharmaceutical developments are discussed in detail. We have also compiled the details of various startups in AI in this field. This review will provide a comprehensive analysis and outline various state-of-the-art AI/ML techniques to the readers with their framework applications. This review also highlights the challenges in this field, which need to be addressed for further success in pharmaceutical applications.

Utilizing a high-throughput visualization screening technology to develop a genetically encoded biosensor for monitoring 5-aminolevulinic acid production in engineered Escherichia coli

5-Aminolevulinic acid (5-ALA) is a non-protein amino acid widely used in agriculture, animal husbandry and medicine. Currently, microbial cell factories are a promising production pathway, but the lack of high-throughput fermentation strain screening tools often hinders the exploration of engineering strategies to increase cell factory yields. Here, mutant AC103-3H was screened from libraries of saturating mutants after response-specific engineering of the transcription factor AsnC of L-asparagine (Asn). Based on mutant AC103-3H, a whole-cell biosensor EAC103-3H with a specific response to 5-ALA was constructed, which has a linear dynamic detection range of 1–12 mM and a detection limit of 0.094 mM, and can be used for in situ screening of potential high-producing 5-ALA strains. With its support, overexpression of the C5 pathway genes using promoter engineering assistance resulted in a 4.78-fold enhancement of 5-ALA production in the engineered E. coli. This study provides an efficient strain screening tool for exploring approaches to improve the 5-ALA productivity of engineered strains.

Mid-infrared chemical imaging of living cells enabled by plasmonic metasurfaces.

Mid-Infrared (MIR) chemical imaging provides rich chemical information of biological samples in a label-free and non-destructive manner. Yet, its adoption to live-cell analysis is limited by the strong attenuation of MIR light in water, often necessitating cell culture geometries that are incompatible with the prolonged viability of cells and with standard high-throughput workflow. Here, we introduce a new approach to MIR microscopy, where cells are imaged through their localized near-field interaction with a plasmonic metasurface. Chemical contrast of distinct molecular groups provided sub-cellular resolution images of the proteins, lipids, and nucleic acids in the cells that were collected using an inverted MIR microscope. Time-lapse imaging of living cells demonstrated that their behaviors, including motility, viability, and substrate adhesion, can be monitored over extended periods of time using low-power MIR light. The presented approach provides a method for the non-perturbative MIR imaging of living cells, which is well-suited for integration with modern high-throughput screening technologies for the label-free, high-content chemical imaging of living cells.

Antimicrobial activity of compounds identified by artificial intelligence discovery engine targeting enzymes involved in Neisseria gonorrhoeae peptidoglycan metabolism

BackgroundNeisseria gonorrhoeae (Ng) causes the sexually transmitted disease gonorrhoea. There are no vaccines and infections are treated principally with antibiotics. However, gonococci rapidly develop resistance to every antibiotic class used and there is a need for developing new antimicrobial treatments. In this study we focused on two gonococcal enzymes as potential antimicrobial targets, namely the serine protease L,D-carboxypeptidase LdcA (NgO1274/NEIS1546) and the lytic transglycosylase LtgD (NgO0626/NEIS1212). To identify compounds that could interact with these enzymes as potential antimicrobials, we used the AtomNet virtual high-throughput screening technology. We then did a computational modelling study to examine the interactions of the most bioactive compounds with their target enzymes. The identified compounds were tested against gonococci to determine minimum inhibitory and bactericidal concentrations (MIC/MBC), specificity, and compound toxicity in vitro.ResultsAtomNet identified 74 compounds that could potentially interact with Ng-LdcA and 84 compounds that could potentially interact with Ng-LtgD. Through MIC and MBC assays, we selected the three best performing compounds for both enzymes. Compound 16 was the most active against Ng-LdcA, with a MIC50 value < 1.56 µM and MBC50/90 values between 0.195 and 0.39 µM. In general, the Ng-LdcA compounds showed higher activity than the compounds directed against Ng-LtgD, of which compound 45 had MIC50 values of 1.56–3.125 µM and MBC50/90 values between 3.125 and 6.25 µM. The compounds were specific for gonococci and did not kill other bacteria. They were also non-toxic for human conjunctival epithelial cells as judged by a resazurin assay. To support our biological data, in-depth computational modelling study detailed the interactions of the compounds with their target enzymes. Protein models were generated in silico and validated, the active binding sites and amino acids involved elucidated, and the interactions of the compounds interacting with the enzymes visualised through molecular docking and Molecular Dynamics Simulations for 50 ns and Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA).ConclusionsWe have identified bioactive compounds that appear to target the N. gonorrhoeae LdcA and LtgD enzymes. By using a reductionist approach involving biological and computational data, we propose that compound Ng-LdcA-16 and Ng-LtgD-45 are promising anti-gonococcal compounds for further development.

Optimized near infrared reflectance model based on preprocessing selection strategy for rapid analysis of 2-acetyl-1-pyrroline content in rice flour

The chemical compound 2-acetyl-1-pyrroline (2-AP) is a major contributor to the fragrance of rice. This report presents the first efforts towards determining the quantities of 2-AP in rice by grating-based visible–near-infrared reflectance spectroscopy using a diverse set of rice varieties. Calibration models generated by preprocessing treatments using Design of Experiment (DoE) and ‘one-variable-at-atime’ (OVAT) methods were compared and optimized. A modified partial least squares model with “2, 3, 1, 1”/SNV and 174 variables yielded a coefficient of determination for calibration values of 0.978. The results showed that a DoE-based preprocessing selection could efficiently and accurately optimize 2-AP calibration models, with advantages over OVAT. The findings of this study provide high-throughput screening technology for high-precision fragrance quality breeding, monitoring and control in the rice industry. The method demonstrated here also has potential for application in many other disciplines, including identification of plant fragrances and quality control of cereal products.

A Novel Class I HDAC Inhibitor, AW01178, Inhibits Epithelial-Mesenchymal Transition and Metastasis of Breast Cancer.

Epithelial-mesenchymal transition (EMT) refers to the transformation of polar epithelial cells into motile mesenchymal cells under specific physiological or pathological conditions, thus promoting the metastasis of cancer cells. Epithelial cadherin (E-cadherin) is a protein that plays an important role in the acquisition of tumor cell motility and serves as a key EMT epithelial marker. In the present study, AW01178, a small-molecule compound with potential therapeutic efficacy, was identified via in-cell Western high-throughput screening technology using E-cadherin as the target. The compound induced the upregulation of E-cadherin at both mRNA and protein levels and inhibited the EMT of breast cancer cells in vitro as well as metastasis in vivo. Mechanistically, AW01178 is a novel benzacetamide histone deacetylase inhibitor (HDACi) mainly targeting class I histone deacetylases. AW01178 promoted the transcription and expression of E-cadherin through enhancing the acetylation level of histone H3 in the E-cadherin promoter region, thereby inhibiting the metastasis of breast cancer cells. The collective findings support the potential utility of the novel HDACi compound identified in this study, AW01178, as a therapeutic drug for breast cancer and highlight its value for the future development of HDACi structures as anticancer drugs.

Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges.

Polypharmacy is commonly employed in clinical settings. The potential risks of drug-drug interactions (DDIs) can compromise efficacy and pose serious health hazards. Integrating pharmacokinetics (PK) and pharmacodynamics (PD) models into DDIs research provides a reliable method for evaluating and optimizing drug regimens. With advancements in our comprehension of both individual drug mechanisms and DDIs, conventional models have begun to evolve towards more detailed and precise directions, especially in terms of the simulation and analysis of physiological mechanisms. Selecting appropriate models is crucial for an accurate assessment of DDIs. This review details the theoretical frameworks and quantitative benchmarks of PK and PD modeling in DDI evaluation, highlighting the establishment of PK/PD modeling against a backdrop of complex DDIs and physiological conditions, and further showcases the potential of quantitative systems pharmacology (QSP) in this field. Furthermore, it explores the current advancements and challenges in DDI evaluation based on models, emphasizing the role of emerging in vitro detection systems, high-throughput screening technologies, and advanced computational resources in improving prediction accuracy.

Site-Specific Acetylation of the Transcription Factor Protein Max Modulates Its DNA Binding Activity.

Chemical protein synthesis provides a powerful means to prepare novel modified proteins with precision down to the atomic level, enabling an unprecedented opportunity to understand fundamental biological processes. Of particular interest is the process of gene expression, orchestrated through the interactions between transcription factors (TFs) and DNA. Here, we combined chemical protein synthesis and high-throughput screening technology to decipher the role of post-translational modifications (PTMs), e.g., Lys-acetylation on the DNA binding activity of Max TF. We synthesized a focused library of singly, doubly, and triply modified Max variants including site-specifically acetylated and fluorescently tagged analogs. The resulting synthetic analogs were employed to decipher the molecular role of Lys-acetylation on the DNA binding activity and sequence specificity of Max. We provide evidence that the acetylation sites at Lys-31 and Lys-57 significantly inhibit the DNA binding activity of Max. Furthermore, by utilizing high-throughput binding measurements, we assessed the binding activities of the modified Max variants across diverse DNA sequences. Our results indicate that acetylation marks can alter the binding specificities of Max toward certain sequences flanking its consensus binding sites. Our work provides insight into the hidden molecular code of PTM-TFs and DNA interactions, paving the way to interpret gene expression regulation programs.

Statistical approaches enabling technology-specific assay interference prediction from large screening data sets

High throughput screening (HTS) technologies allow the biological testing of hundreds of thousands of compounds per day. Typically, a substantial proportion of the initial hits obtained by HTS are artifacts caused by assay interference. Therefore, global and technology-specific in silico models for identifying and predicting compounds interfering with biological assays have been developed. The global models benefit from training on large screening data sets, while the specialized models benefit from training on assay technology-specific experimental data. In this work, we develop and explore strategies for generating better predictors of technology-specific assay interference by utilizing the large bioactivity data matrices global models are trained on and employing partially new compound labeling approaches to maintain the assay technology awareness of specialized models. We demonstrate the utility of the statistically derived interference labels in machine learning using fluorescence-based assay interference as a representative example. Our random forest and multi-layer perceptron classifiers showed improved performance compared to existing models, achieving Matthews correlation coefficients (MCCs) of up to 0.47 on holdout data and up to 0.45 on an external test set. These results demonstrate that accurate assay-specific interference labels can be derived from large bioactivity data matrices, enabling the development of new machine-learning models without the need for further experimental data.

Discovery of a Unique Set of Dog-Seroreactive Coccidioides Proteins Using Nucleic Acid Programmable Protein Array.

Valley Fever (VF), caused by fungi in the genus Coccidioides, is a prevalent disease in southwestern and western parts of the United States that affects both humans and animals, such as dogs. Although the immune responses to infection with Coccidioides spp. are not fully characterized, antibody-detection assays are used in conjunction with clinical presentation and radiologic findings to aid in the diagnosis of VF. These assays often use Complement Fixation (CF) and Tube Precipitin (TP) antigens as the main targets of IgG and IgM reactivity, respectively. Our group previously reported evidence of over 800 genes expressed at the protein level in C. posadasii. However, antibody reactivity to the majority of these proteins has never been explored. Using a new, high-throughput screening technology, the Nucleic Acid Programmable Protein Array (NAPPA), we screened serum specimens from dogs against 708 of these previously identified proteins for IgG reactivity. Serum from three separate groups of dogs was analyzed and revealed a small panel of proteins to be further characterized for immuno-reactivity. In addition to CF/CTS1 antigen, sera from most infected dogs showed antibody reactivity to endo-1,3-betaglucanase, peroxisomal matrix protein, and another novel reactive protein, CPSG_05795. These antigens may provide additional targets to aid in antibody-based diagnostics.

High-throughput visualization mutation screening technology to enhance the specificity of CadR based whole-cell cadmium biosensor

As a heavy metal pollutant, Cd2+ often enters the human body through the food chain causing great harm to human health. Whole-cell biosensor is an emerging technology for rapid on-site detection of heavy metals with the advantages of inexpensive, fast to mass-produce, and strong in anti-interference resistance, but suffering from insatisfactory specificity. In this study, a strategy of Adjacent Site Saturation Mutation (ASSM) was designed to improve the specificity of transcription factor CadR, which acted as the recognition element and determined the specificity of whole cell Cd2+ biosensors. A specific saturated library was constructed using the strategy of adjacent mutation. After two rounds of high-throughput visual screening, a whole-cell biosensor with good response to Cd2+, and with significant weakened Hg2+ interference was obtained. The optimized whole-cell biosensor showed a linear dynamic concentration range from 500 nM to 100 μM, a detection limit of 0.079 μM, and has satisfactory specificity and anti-interference. The ASSM strategy proposed in this study can provide a new method for the application of synthetic biology in food safety detection, indicating the importance of whole-cell biosensors for the detection of heavy metals.

Abstract 6587: Discovery of molecular glue of FBXW7:MYC interaction using small molecule microarray

Abstract MYC deregulation is a key driver and feature of many human cancers. Yet, despite its importance in cancer progression, MYC is considered “undruggable” by small molecules due to its disordered conformation. Therefore, interfering with its interactions with protein partners to modulate MYC activity appears viable. FBXW7 is an E3 ligase, which serves as a constituent of the SCF (Skp1-Cul1-F box) ubiquitin ligase complex. It mediates the degradation of both c-Myc and Cyclin E through ubiquitin-dependent proteolysis. FBXW7 mutations are found in various human tumor types, establishing its role as a general human tumor suppressor. Specifically, FBXW7 down-regulation increases MYC level, and its expression correlates to lethal outcomes of breast epithelial cells, poor prognosis in glioblastoma, and T cell acute lymphoblastic leukemia remission. Therefore, our project aims to discover potent small molecule glues that would enhance the interaction between FBXW7 and MYC, leading to MYC degradation through proteolysis and eventually suppression of various cancers. Taking advantage of our high throughput screening technology, Small Molecule Microarrays (SMMs), we successfully identified 29 compounds that potently bind to both c-Myc and FBXW7 after screening our 65,000 ‘drug-like’ small molecule library. We then performed secondary assays using a mCherry-GFP MYC reporter cell line to identify our lead compound, KI-FBX-001, which decreases MYC’s expression:transcription ratio by over 50% compared to control. Additionally, this down-regulation can be rescued by protease inhibitor MG132, indicating that the effect is achieved via proteolysis. In vitro dose-response and time-course treatment revealed an optimal drugging concentration at 15uM in K562 cells, with inhibition reaching its peak at the 4-hour time point at over 60%. Using the Qiagen MYC reporter assay, the in vitro IC50 of KI-FBX-001 is found at 7.42uM. Beyond the protein level, the qPCR result also reveals a decrease in MYC mRNA level at over 70%. Surface Plasmon Resonance (SPR) is used to confirm direct binding between FBXW7 and KI-FBX-001, which showed promising binding with kd=0.17µM. Proximity Ligation Assay further confirms KI-FBX-001’s effect in enhancing FBXW7:MYC interaction in the cell. In summary, our findings proved that KI-FBX-001 is a promising molecular glue in the FBXW7:MYC interaction, and can effectively modulate the MYC level for future therapeutic purposes. Citation Format: Shenghao Guo, Morgan Stilgenbauer, Bocheng Wu, Yatin Mankan, Angela N. Koehler. Discovery of molecular glue of FBXW7:MYC interaction using small molecule microarray [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6587.

Abstract 3094: Biophysics in kinase drug discovery programs: Spectral shift technology from fragment screening to mechanism of action

Abstract Using Eurofins Discovery’s state-of-the art Hit-Finding platform, our experts designed an optimized screening cascade combining cutting-edge High-throughput Screening (HTS) technologies with the latest Biophysics techniques. With advanced technologies, such as the Echo® MS and RapidFire MS for HTS, and SPR, MST, TSA, nanoDSF and ITC for Biophysics, the cascade is effective for primary screening, orthogonal assays and evaluating Mechanism of Action (MoA). There are many biophysical approaches which bring value to drug discovery programs, and it is the target properties which drive the selection of the most suitable technology. In this presentation, we will highlight the novel Spectral Shift technology, one of the techniques employed by the Dianthus (well-known for the MST-TRIC). The goal was to rapidly implement an assay that monitors the ability of PIM3 to bind to ATP, as well as to compounds and fragments in solution. Using the Echo MS’ acoustic-droplet ejection, nanoliter dispensing in 394-well plates, robust data by Spectral Shift were quickly generated for the fragment-based screening campaign. Hits were confirmed in Dose-response with µM-range affinity values (KD), and TSA was used as an orthogonal assay for the identification of stabilizers among the selected PIM3 binders. For a defined set of 826 proprietary fragments, a single dose screening at 600µM using Spectral Shift was run, leading to the identification of 11% of potential binders. The 100 fragments were then confirmed in dose-response titration and 80 binders were confirmed. The top 30 binders were selected for orthogonal characterization using TSA. This led to the selection of the top 6 confirmed fragments, on which a MoA study in MST-TRIC was performed. Following this evaluation, 3 allosteric binders were identified, with KD from 355 to 550µM (affinity of orthosteric binders from 55 to 333µM). Biophysics plays an important role in drug discovery by qualifying valuable assets and increasing the success rates of future Hit-finding programs. The fragment hits selected in this PIM3 Kinase project, which show ADP-Glo™ activities, are now ready for crystallography studies (NMR &amp; X-ray co-crystallization). Citation Format: Celine Legros, Vanessa Porkolab, Maud Sigoillot, Olivier Mirguet. Biophysics in kinase drug discovery programs: Spectral shift technology from fragment screening to mechanism of action [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3094.

Biomarkers in Ovarian Cancer: Towards Personalized Medicine.

Ovarian cancer is one of the deadliest cancers in women. The lack of specific symptoms, especially at the initial stages of disease development, together with the malignancy heterogeneity, lower the life expectancy of patients. Aiming to improve survival rates, diagnostic and prognostic biomarkers are increasingly employed in clinics, providing gynecologists and oncologists with new tools to guide their treatment decisions. Despite the vast number of investigations, there is still an urgent need to discover more ovarian cancer subtype-specific markers which could further improve patient classification. To this end, high-throughput screening technologies, like mass spectrometry, are applied to deepen the tumoral cellular landscape and describe the malignant phenotypes. As for disease treatment, new targeted therapies, such as those based on PARP inhibitors, have shown great efficacy in destroying the tumoral cells. Likewise, drug-nanocarrier systems targeting the tumoral cells have exhibited promising results. In this narrative review, we summarize the latest achievements in the pursuit of biomarkers for ovarian cancer and recent anti-tumoral therapies.

An Automated Single-Cell Droplet-Digital Microfluidic Platform for Monoclonal Antibody Discovery.

Monoclonal antibody (mAb) discovery plays a prominent role in diagnostic and therapeutic applications. Droplet microfluidics has become a standard technology for high-throughput screening of antibody-producing cells due to high droplet single-cell confinement frequency and rapid analysis and sorting of the cells of interest with their secreted mAbs. In this work, a new method is described for on-demand co-encapsulation of cells that eliminates the difficulties associated with washing in between consecutive steps inside the droplets and enables the washing and addition of fresh media. The new platform identifies hybridoma cells that are expressing antibodies of interest using antibody-characterization assays to find the best-performing or rare-cell antibody candidates.

High-Throughput Screening Techniques for the Selection of Thermostable Enzymes.

The acquisition of a thermostable enzyme is an indispensable prerequisite for its successful implementation in industrial applications and the development of novel functionalities. Various protein engineering approaches, including rational design, semirational design, and directed evolution, have been employed to enhance thermostability. However, all of these approaches require sensitive and reliable high-throughput screening (HTS) technologies to efficiently and rapidly identify variants with improved properties. While numerous reviews focus on modification strategies for enhancing enzyme thermostability, there is a dearth of literature reviewing HTS methods specifically aimed at this objective. Herein, we present a comprehensive overview of various HTS methods utilized for modifying enzyme thermostability across different screening platforms. Additionally, we highlight significant recent examples that demonstrate the successful application of these methods. Furthermore, we address the technical challenges associated with HTS technologies used for screening thermostable enzyme variants and discuss valuable perspectives to promote further advancements in this field. This review serves as an authoritative reference source offering theoretical support for selecting appropriate screening strategies tailored to specific enzymes with the aim of improving their thermostability.