High-throughput Screening Technologies Research Articles

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.

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 & 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.

ScDecouple: decoupling cellular response from infected proportion bias in scCRISPR-seq.

Single-cell clustered regularly interspaced short palindromic repeats-sequencing (scCRISPR-seq) is an emerging high-throughput CRISPR screening technology where the true cellular response to perturbation is coupled with infected proportion bias of guide RNAs (gRNAs) across different cell clusters. The mixing of these effects introduces noise into scCRISPR-seq data analysis and thus obstacles to relevant studies. We developed scDecouple to decouple true cellular response of perturbation from the influence of infected proportion bias. scDecouple first models the distribution of gene expression profiles in perturbed cells and then iteratively finds the maximum likelihood of cell cluster proportions as well as the cellular response for each gRNA. We demonstrated its performance in a series of simulation experiments. By applying scDecouple to real scCRISPR-seq data, we found that scDecouple enhances the identification of biologically perturbation-related genes. scDecouple can benefit scCRISPR-seq data analysis, especially in the case of heterogeneous samples or complex gRNA libraries.

Antimicrobials: An update on new strategies to diversify treatment for bacterial infections.

Ninety-five years after Fleming's discovery of penicillin, a bounty of antibiotic compounds have been discovered, modified, or synthesised. Diversification of target sites, improved stability and altered activity spectra have enabled continued antibiotic efficacy, but overwhelming reliance and misuse has fuelled the global spread of antimicrobial resistance (AMR). An estimated 1.27 million deaths were attributable to antibiotic resistant bacteria in 2019, representing a major threat to modern medicine. Although antibiotics remain at the heart of strategies for treatment and control of bacterial diseases, the threat of AMR has reached catastrophic proportions urgently calling for fresh innovation. The last decade has been peppered with ground-breaking developments in genome sequencing, high throughput screening technologies and machine learning. These advances have opened new doors for bioprospecting for novel antimicrobials. They have also enabled more thorough exploration of complex and polymicrobial infections and interactions with the healthy microbiome. Using models of infection that more closely resemble the infection state in vivo, we are now beginning to measure the impacts of antimicrobial therapy on host/microbiota/pathogen interactions. However new approaches are needed for developing and standardising appropriate methods to measure efficacy of novel antimicrobial combinations in these contexts. A battery of promising new antimicrobials is now in various stages of development including co-administered inhibitors, phages, nanoparticles, immunotherapy, anti-biofilm and anti-virulence agents. These novel therapeutics need multidisciplinary collaboration and new ways of thinking to bring them into large scale clinical use.

Autoantibody Profiling Using Human Autoantigen Protein Array and AlphaScreen.

Autoantibodies that recognize self-antigens are believed to have a close relationship with diseases such as autoimmune diseases, cancer, and lifestyle diseases. Analysis of autoantibodies is essential for investigating pathology mechanisms, diagnosis, and therapeutics of these diseases. We developed an autoantibody profiling assay using a cell-free synthesized protein array and high-throughput screening technology. Our assay system can sensitively detect interaction between recombinant antigen protein and autoantibody and efficiently analyze autoantibody profiling in patients' sera.

Advances in Droplet-Based Microfluidic High-Throughput Screening of Engineered Strains and Enzymes Based on Ultraviolet, Visible, and Fluorescent Spectroscopy

Genetic engineering and directed evolution are effective methods for addressing the low yield and poor industrialization level of microbial target products. The current research focus is on how to efficiently and rapidly screen beneficial mutants from constructed large-scale mutation libraries. Traditional screening methods such as plate screening and well-plate screening are severely limited in their development and application due to their low efficiency and high costs. In the past decade, microfluidic technology has become an important high-throughput screening technology due to its fast speed, low cost, high automation, and high screening throughput, and it has developed rapidly. Droplet-based microfluidic high-throughput screening has been widely used in various fields, such as strain/enzyme activity screening, pathogen detection, single-cell analysis, drug discovery, and chemical synthesis, and has been widely applied in industries such as those involving materials, food, chemicals, textiles, and biomedicine. In particular, in the field of enzyme research, droplet-based microfluidic high-throughput screening has shown excellent performance in discovering enzymes with new functions as well as improved catalytic efficiency or stability, acid-base tolerance, etc. Currently, droplet-based microfluidic high-throughput screening technology has achieved the high-throughput screening of enzymes such as glycosidase, lipase, peroxidase, protease, amylase, oxidase, and transaminase as well as the high-throughput detection of products such as riboflavin, coumarin, 3-dehydroquinate, lactic acid, and ethanol. This article reviews the application of droplet-based microfluidics in high-throughput screening, with a focus on high-throughput screening strategies based on UV, visible, and fluorescence spectroscopy, including labeled optical signal detection screening, as well as label-free electrochemical detection, mass spectrometry, Raman spectroscopy, nuclear magnetic resonance, etc. Furthermore, the research progress and development trends of droplet-based microfluidic technology in enzyme modification and strain screening are also introduced.

FLECS technology for high-throughput screening of hypercontractile cellular phenotypes in fibrosis: A function-first approach to anti-fibrotic drug discovery

The pivotal role of myofibroblast contractility in the pathophysiology of fibrosis is widely recognized, yet HTS approaches are not available to quantify this critically important function in drug discovery. We developed, validated, and scaled-up a HTS platform that quantifies contractile function of primary human lung myofibroblasts upon treatment with pro-fibrotic TGF-β1. With the fully automated assay we screened a library of 40,000 novel small molecules in under 80 h of total assay run-time. We identified 42 hit compounds that inhibited the TGF-β1-induced contractile phenotype of myofibroblasts, and enriched for 19 that specifically target myofibroblasts but not phenotypically related smooth muscle cells. Selected hits were validated in an ex vivo lung tissue models for their inhibitory effects on fibrotic gene upregulation by TGF-β1. Our results demonstrate that integrating a functional contraction test into the drug screening process is key to identify compounds with targeted and diverse activity as potential anti-fibrotic agents.

Applications of CRISPR screening to lung cancer treatment.

Lung cancer is an extremely aggressive and highly prevalent disease worldwide, and it is one of the leading causes of cancer death. Deciphering intrinsic genetic mechanism, finding new targets, and overcoming drug resistance are the key to lung cancer treatment. High-throughput CRISPR screening has been extensively used to obtain the genes related to cancers including lung cancer. This review describes CRISPR/Cas9 or CRISPR/dCas9-based technologies for high-throughput screening. We summarize the applications of CRISPR screening technology in exploring the mechanism of lung cancer development in vivo or in vitro, overcoming drug resistance, improving the effect of immunotherapy, and discovering new therapeutic targets. This review highlights the potential of CRISPR screening in combination with tumor barcoding and high-throughput sequencing (Tuba-seq) to precisely quantify the impact of alterations in many tumor suppressor genes on lung cancer.

High-throughput screening technologies for drug discovery

The development of high-throughput screening technologies has revolutionized the field of drug discovery by significantly improving the efficiency of compound library screening. Traditional screening methods, such as manual screening and biochemical assays, were time-consuming and limited in their ability to identify lead compounds. However, the advent of high-throughput screening technologies has overcome these limitations and provided researchers with a more efficient and effective approach. This review begins by examining the background, characteristics, and limitations of conventional screening methods. These methods often required large amounts of time, resources, and labor, making them impractical for large-scale compound screening. In recent years, some new technologies have emerged, including virtual screening, image analysis, prediction methods, and microarray-based screening. Each of these approaches has its own strengths and limitations, but collectively they have greatly enhanced the efficiency and accuracy of compound identification. These viewpoints highlight the successful application of these technologies in identifying lead compounds for various therapeutic targets. Finally, the review envisions the future development of high-throughput screening technologies. It emphasizes the need for continuous optimization and innovation to further improve the efficiency and effectiveness of compound identification. The ultimate goal is to shorten drug development timelines and provide high-quality lead compounds for the benefit of patients. In conclusion, the emergence of high-throughput screening technologies has significantly improved the efficiency of compound library screening and provided better lead compounds for drug discovery. Ongoing advancements in these technologies hold great promise for the future of pharmaceutical research and development.

Simeprevir restores the anti-Staphylococcus activity of polymyxins

Methicillin-resistant Staphylococcus aureus (MRSA) infection poses a severe threat to global public health due to its high mortality. Currently, polymyxins are mainly used for the treatment of Gram-negative bacterial-related infection, while exhibiting limited antibacterial activities against Staphylococcus aureus (S. aureus). However, the combination of antibiotics with antibiotic adjuvants is a feasible strategy for the hard-treated infection and toxicity reducing. We will investigate the antibacterial activity of simeprevir (SIM), which treated for genotype 1 and 4 chronic hepatitis C, combined with polymyxins against MRSA through high-throughput screening technology. In our study, the synergistic antibacterial effect of SIM and polymyxins against S. aureus in vitro was found by checkerboard assay and time-growth curve. The cytotoxicity of SIM combined with polymyxin B sulfate [PB(S)] or polymyxin E (PE) in vitro was evaluated using CCK-8, human RBC hemolysis and scratch assays. In addition, we investigated the eradication of biofilm formation of S. aureus by biofilm inhibition assay and the killing of persister cells. Moreover, we evaluated the therapeutic effect and in vivo toxicity of the combination against MRSA in murine subcutaneous abscess model. Furthermore, it was preliminarily found that SIM significantly enhanced the destruction of MRSA membrane by SYTOX Green and DISC3(5) probes. In summary, these results reveal that the therapy of SIM combined with polymyxins (especially PE) is promising for the treatment of MRSA infection.

Artificial intelligence-driven shimming for parallel high field nuclear magnetic resonance

Rapid drug development requires a high throughput screening technology. NMR could benefit from parallel detection but is hampered by technical obstacles. Detection sites must be magnetically shimmed to ppb uniformity, which for parallel detection is precluded by commercial shimming technology. Here we show that, by centering a separate shim system over each detector and employing deep learning to cope with overlapping non-orthogonal shimming fields, parallel detectors can be rapidly calibrated. Our implementation also reports the smallest NMR stripline detectors to date, based on an origami technique, facilitating further upscaling in the number of detection sites within the magnet bore.

Opportunities and Prospects for Preclinical Drug Safety Assessment Using Alternative Methods: Experience from the Toxicology in the 21st Century (Tox21) Programme in the USA

SCIENTIFIC RELEVANCE. The Tox21 (Toxicology in the 21st Century) programme was developed by the US Tox21 Consortium with the aim to replace animal-based toxicity assessments of chemicals with a wide range of in vitro and in silico testing approaches and has since been successfully applied in practice.AIM. The study aimed to review information on alternative in vitro models developed as part of the Tox21 programme for testing the toxicity of chemical compounds.DISCUSSION. According to the information provided by the National Toxicology Program, Environmental Protection Agency, National Center for Advancing Translational Sciences, and other Tox21 Consortium members on their official websites and in the literature, the Tox21 Consortium has developed a quantitative high-throughput screening technology for testing the safety of chemicals and created the Tox21 10K library of chemical compounds using this screening technology. The library has been successfully used to create models that predict the toxicity of chemicals prior to preclinical studies. Researchers have proposed new approaches to studying the safety of chemical compounds in human cell lines to replace in vivo studies. Innovative organ-on-chip, multi-organ-on-chip, and organoid models are free from the drawbacks and limitations of cell-line models and offer more accurate representations of complex cell–matrix and organ–organ interactions. Developed under the Tox21 programme to search for new chemical toxicity biomarkers and gene signatures, novel transcriptomics (toxicogenomics) technologies can be used to classify toxicants according to their health risks and to identify potential side effects long before discovering any pathological changes in the body. The Interagency Coordinating Committee on the Validation of Alternative Methods conducts technical evaluation of alternative testing methods and promotes their implementation into regulatory practice.CONCLUSIONS. Thus, new tools and technologies provide an opportunity for switching from in vivo toxicity testing of candidate medicinal products to in silico and in vitro methods.

Applications of multi-omics analysis in human diseases.

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.