High-throughput Methodologies Research Articles

High-Throughput Synthesis and Evaluation of Antiviral Copolymers for Enveloped Respiratory Viruses.

COVID-19 made apparent the devastating impact viral pandemics have had on global health and order. Development of broad-spectrum antivirals to provide early protection upon the inevitable emergence of new viral pandemics is critical. In this work, antiviral polymers are discovered using a combination of high-throughput polymer synthesis and antiviral screening, enabling diverse polymer compositions to be explored. Amphipathic polymers, with ionizable tertiary amine groups, are the most potent antivirals, effective against influenza virus and SARS-CoV-2, with minimal cytotoxicity. It is hypothesized that these polymers interact with the viral membrane as they showed no activity against a nonenveloped virus (rhinovirus). The switchable chemistry of the polymers during endosomal acidification was evaluated using lipid monolayers, indicating that a complex synergy between hydrophobicity and ionization drives polymer-membrane interactions. This new high-throughput methodology can be adapted to continue to engineer the potency of the lead candidates or develop antiviral polymers against other emerging viral classes.

High-confidence and high-throughput quantification of synapse engulfment by oligodendrocyte precursor cells.

Oligodendrocyte precursor cells (OPCs) sculpt neural circuits through the phagocytic engulfment of synapses during development and adulthood. However, existing techniques for analyzing synapse engulfment by OPCs have limited accuracy. Here we describe the quantification of synapse engulfment by OPCs via a two-pronged cell biological approach that combines high-confidence and high-throughput methodologies. Firstly, an adeno-associated virus encoding a pH-sensitive, fluorescently tagged synaptic marker is expressed in neurons in vivo to differentially label presynaptic inputs, depending upon whether they are outside of or within acidic phagolysosomal compartments. When paired with immunostaining for OPC markers in lightly fixed tissue, this approach quantifies the engulfment of synapses by around 30-50 OPCs in each experiment. The second method uses OPCs isolated from dissociated brain tissue that are then fixed, incubated with fluorescent antibodies against presynaptic proteins, and analyzed by flow cytometry, enabling the quantification of presynaptic material within tens of thousands of OPCs in <1 week. The integration of both methods extends the current imaging-based assays, originally designed to quantify synaptic phagocytosis by other brain cells such as microglia and astrocytes, by enabling the quantification of synaptic engulfment by OPCs at individual and populational levels. With minor modifications, these approaches can be adapted to study synaptic phagocytosis by numerous glial cell types in the brain. The protocol is suitable for users with expertise in both confocal microscopy and flow cytometry. The imaging-based and flow cytometry-based protocols require 5 weeks and 2 d to complete, respectively.

Proteomic Analysis Reveals Oxidative Phosphorylation and JAK-STAT Pathways Mediated Pathogenesis of Pemphigus Vulgaris.

Pemphigus vulgaris (PV) stands as a rare autoimmune bullous disease, while the precise underlying mechanism remains incompletely elucidated. High-throughput proteomic methodologies, such as LC-MS/MS, have facilitated the quantification and characterisation of proteomes from clinical skin samples, enhancing our comprehension of PV pathogenesis. The objective of this study is to elucidate the signalling mechanisms underlying PV through proteomic analysis. Proteins and cell suspension were extracted from skin biopsies obtained from both PV patients and healthy volunteers and subsequently analysed using LC-MS/MS and scRNA-seq. Cultured keratinocytes were treated with PV serum, followed by an assessment of protein expression levels using immunofluorescence and western blotting. A total of 880, 605, and 586 differentially expressed proteins (DEPs) were identified between the lesion vs. control, non-lesion vs. control, and lesion vs. non-lesion groups, respectively. The oxidative phosphorylation (OXPHOS) pathway showed activation in PV. Keratinocytes are the major cell population in the epidermis and highly expressed ATP5PF, ATP6V1G1, COX6B1, COX6A1, and NDUFA9. In the cellular model, there was a notable increase in the expression levels of OXPHOS-related proteins (V-ATP5A, III-UQCRC2, II-SDHB, I-NDUFB8), along with STAT1, p-STAT1, and p-JAK1. Furthermore, both the OXPHOS inhibitor metformin and the JAK1 inhibitor tofacitinib demonstrated therapeutic effects on PV serum-induced cell separation, attenuating cell detachment. Metformin notably reduced the expression of V-ATP5A, III-UQCRC2, II-SDHB, I-NDUFB8, p-STAT1, p-JAK1, whereas tofacitinib decreased the expression of p-STAT1 and p-JAK1, with minimal impact on the expression of V-ATP5A, III-UQCRC2, II-SDHB, and I-NDUFB8. Our results indicate a potential involvement of the OXPHOS and JAK-STAT1 pathways in the pathogenesis of PV.

The Genetic Architecture of Arabidopsis thaliana in Response to Native Non-Pathogenic Bacterial Species Revealed by Genome-Wide Association Mapping in Field Conditions

Non-pathogenic bacteria can substantially contribute to plant health by mobilizing and supplying nutrients, providing protection against pathogens, and alleviating abiotic stresses. However, the number of genome-wide association studies reporting the genetic architecture of the response to individual members of the beneficial microbiota remains limited. In this study, we established a genome-wide association study under field conditions to estimate the level of genetic variation and the underlying genetic architecture among 162 accessions of Arabidopsis thaliana originating from 54 natural populations in the southwest of France in response to 13 strains of seven of the most abundant and prevalent non-pathogenic bacterial species isolated from the leaf compartment of A. thaliana in the same geographical region. Using a high-throughput phenotyping methodology to score vegetative growth-related traits, extensive genetic variation was detected among A. thaliana accessions in response to these leaf bacteria, at both the species and strain levels. The presence of crossing reaction norms between each strain and the mock treatment indicates that declaring a strain as a plant growth-promoting bacterium is highly dependent on the host genotype tested. In line with the strong genotype-by-genotype interactions, we detected a complex and highly flexible genetic architecture between the 13 strains. Finally, the candidate genes underlying the quantitative trait loci revealed significant enrichment in several biological pathways, including cell, secondary metabolism, signaling, and transport. Altogether, plant innate immunity appears as a significant source of natural genetic variation in plant–microbiota interactions and opens new avenues for better understanding the ecologically relevant molecular dialog during plant–microbiota interactions. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Hyperspectral Imaging Technique to Characterize Digestate and Visualize Physical Impurities in Anaerobically Digested Biowaste.

Methods used to monitor anaerobic digestion (AD) indicators are commonly based on wet chemical analyses, which consume time and materials. In addition, physical disturbances, such as floating granules (FGs), must be monitored manually. In this study, we present an eco-friendly, high-throughput methodology that uses near-infrared hyperspectral imaging (NIR-HSI) to build a machine-learning model for characterizing the chemical composition of the digestate and a target detection algorithm for identifying FGs. A total of 732 digestate samples were used to develop and validate a model for calculating total nitrogen (TN), total organic carbon (TOC), total ammonia nitrogen (TAN), and chemical oxygen demand (COD), which are the chemical indicators of responses to disturbances in the AD process. Among these parameters, good model performance was obtained using the dried digestates data set, where the coefficient of determination (R2test) and the root-mean-square error (RMSEtest) were 0.82 and 1090 mg/L for TOC, and 0.86 and 690 mg/L for TN, respectively. Furthermore, the unique spectral features of the FGs in reactors with a lipid-rich substrate meant that they could also be identified by the HSI system. Based on these findings, developing NIR-HSI solutions to monitor the digestate properties in AD plants has great potential for industrial application.

High-throughput digital imaging and detection of morpho-physiological traits in tomato plants under drought

Abstract Advances in informatics, robotics, and imaging techniques make it possible to use state-of-the-art digital reconstruction technologies for high-throughput plant phenotyping (HTPP) affected by stress factors, as well as for the ontology of their structural and functional traits. Digital imaging of structural and functional features of the aboveground part of plants is non-destructive and plants can be monitored throughout their entire life cycle. In the experiment with tomato plants (Solanum lycopersicum L.; cv. Gruzanski zlatni) grown in controlled environmental conditions and affected by gradual soil dehydration, we evaluated phenotypic traits and phenotypic plasticity by the PlantScreenTM platform using digital imaging of plant optical signals. In this study, 25 different morpho-physiological traits of the plant were evaluated during the precise control and monitoring of the water content in the soil. Different levels of plant water supply induced statistically significant differences in the formation of individual phenotypic traits. Several plant traits have been identified that are characterized by low variability in both well-hydrated and water-stressed conditions, as well as traits with high phenotypic plasticity. Geometric traits (especially Isotop, Round-2top, and Compside) showed a relatively low level of drought-induced phenotypic plasticity. However, functional and chemometric characteristics (ΔF/F′m, Rfd, Water-1, and ARI-1) showed the potential to exhibit rapid plasticity in water-stressed conditions. Our results confirmed that a high-throughput phenotyping methodology coupled with advanced statistical analysis tools can be successfully applied to characterize crop stress responses and identify traits associated with crop stress tolerance.

In vitro identification of underutilized β-lactam combinations against methicillin-resistant Staphylococcus aureus bacteremia isolates.

Methicillin-resistant Staphylococcus aureus (MRSA) bacteremia is a serious clinical challenge with high mortality rates. Antibiotic combination therapy is currently used in cases of persistent infection; however, the limited development of new antibiotics will likely increase the need for combination therapy, and better methods are needed for identifying effective combinations for treating persistent bacteremia. To identify pairwise combinations with the most consistent potential for benefit compared to monotherapy with a primary anti-MRSA agent, we conducted a systematic study with an in vitro high-throughput methodology. We tested daptomycin and vancomycin each in combination with gentamicin, rifampicin, cefazolin, and oxacillin, and ceftaroline with daptomycin, gentamicin, and rifampicin. Combining cefazolin with daptomycin lowered the daptomycin concentration required to reach 95% growth inhibition (IC95) for all isolates tested and lowered daptomycin IC95 below the sensitivity breakpoint for five out of six isolates that had daptomycin minimum inhibitory concentrations at or above the sensitivity breakpoint. Similarly, vancomycin IC95s were decreased when vancomycin was combined with cefazolin for 86.7% of the isolates tested. This was a higher percentage than was achieved by adding any other secondary antibiotic to vancomycin. Adding rifampicin to daptomycin or vancomycin did not always reduce IC95s and failed to produce synergistic interaction in any of the isolates tested; the addition of rifampicin to ceftaroline was frequently synergistic and always lowered the amount of ceftaroline required to reach the IC95. These analyses rationalize further in vivo evaluation of three drug pairs for MRSA bacteremia: daptomycin+cefazolin, vancomycin+cefazolin, and ceftaroline+rifampicin.IMPORTANCEBloodstream infections caused by methicillin-resistant Staphylococcus aureus (MRSA) have a high mortality rate despite the availability of vancomycin, daptomycin, and newer antibiotics including ceftaroline. With the slow output of the antibiotic pipeline and the serious clinical challenge posed by persistent MRSA infections, better strategies for utilizing combination therapy are becoming increasingly necessary. We demonstrated the value of a systematic high-throughput approach, adapted from prior work testing antibiotic combinations against tuberculosis and other mycobacteria, by using this approach to test antibiotic pairs against a panel of MRSA isolates with diverse patterns of antibiotic susceptibility. We identified three antibiotic pairs-daptomycin+cefazolin, vancomycin+cefazolin, and ceftaroline+rifampicin-where the addition of the second antibiotic improved the potency of the first antibiotic across all or most isolates tested. Our results indicate that these pairs warrant further evaluation in the clinical setting.

Deep mutational scanning and machine learning for the analysis of antimicrobial-peptide features driving membrane selectivity.

Many antimicrobial peptides directly disrupt bacterial membranes yet can also damage mammalian membranes. It is therefore central to their therapeutic use that rules governing the membrane selectivity of antimicrobial peptides be deciphered. However, this is difficult even for short peptides owing to the large combinatorial space of amino acid sequences. Here we describe a method for measuring the loss or maintenance of antimicrobial-peptide activity for thousands of peptide-sequence variants simultaneously, and its application to Protegrin-1, a potent yet toxic antimicrobial peptide, to determine the positional importance and flexibility of residues across its sequence while identifying variants with changes in membrane selectivity. More bacterially selective variants maintained a membrane-bound secondary structure while avoiding aromatic residues and cysteine pairs. A machine-learning model trained with our datasets accurately predicted membrane-specific activities for over 5.7 million Protegrin-1 variants, and identified one variant that showed substantially reduced toxicity and retention of activity in a mouse model of intraperitoneal infection. The high-throughput methodology may help elucidate sequence-structure-function relationships in antimicrobial peptides and inform the design of peptide-based synthetic drugs.

Advancing Cardiovascular Drug Screening Using Human Pluripotent Stem Cell-Derived Cardiomyocytes.

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) have emerged as a promising tool for studying cardiac physiology and drug responses. However, their use is largely limited by an immature phenotype and lack of high-throughput analytical methodology. In this study, we developed a high-throughput testing platform utilizing hPSC-CMs to assess the cardiotoxicity and effectiveness of drugs. Following an optimized differentiation and maturation protocol, hPSC-CMs exhibited mature CM morphology, phenotype, and functionality, making them suitable for drug testing applications. We monitored intracellular calcium dynamics using calcium imaging techniques to measure spontaneous calcium oscillations in hPSC-CMs in the presence or absence of test compounds. For the cardiotoxicity test, hPSC-CMs were treated with various compounds, and calcium flux was measured to evaluate their effects on calcium dynamics. We found that cardiotoxic drugs withdrawn due to adverse drug reactions, including encainide, mibefradil, and cetirizine, exhibited toxicity in hPSC-CMs but not in HEK293-hERG cells. Additionally, in the effectiveness test, hPSC-CMs were exposed to ATX-II, a sodium current inducer for mimicking long QT syndrome type 3, followed by exposure to test compounds. The observed changes in calcium dynamics following drug exposure demonstrated the utility of hPSC-CMs as a versatile model system for assessing both cardiotoxicity and drug efficacy. Overall, our findings highlight the potential of hPSC-CMs in advancing drug discovery and development, which offer a physiologically relevant platform for the preclinical screening of novel therapeutics.

A High-Throughput Screening Strategy for Synthesizing Molecularly Imprinted Polymer Nanoparticles Selectively Targeting Tumors.

Molecularly imprinted polymers (MIPs) show significant promise as effective alternatives to antibodies in disease diagnosis and therapy. However, the challenging process of screening extensive libraries of monomer combinations and synthesis conditions to identify formulations with enhanced selectivity and affinity presents a notable time constraint. The need for expedient methods becomes clear in accelerating the strategic development of MIPs tailored for precise molecular recognition purposes. In this study, an innovative high-throughput screening methodology designed to identify the optimal MIP formulation for targeting tumors is presented. Employing a microtiter plate format, over 100 polymer syntheses are conducted, incorporating diverse combinations of functional monomers. Evaluation of binding performance utilizes fluorescence-based assays, focusing on an epitope of the epidermal growth factor receptor (EGFR). Through this meticulously structured screening process, synthesis conditions that produced MIP nanoparticles exhibiting substantial specificity for EGFR targeting (KD = 10-12m) are identified. These "bionic antibodies" demonstrate selective recognition of cancer cells in whole blood samples, even at concentrations as low as 5cellsmL-1. Further validation through fluorescent imaging confirms the tumor-specific localization of the MIPs in vivo. This highly efficient screening approach facilitates the strategic synthesis of imprinted polymers functioning as precision bioprobes.

Develop targeted protein drug carriers through a high-throughput screening platform and rational design.

Protein-based drugs offer advantages such as high specificity, low toxicity, and minimal side effects compared to small molecule drugs. However, delivery of proteins to target tissues or cells remains challenging due to the instability, diverse structures, charges, and molecular weights of proteins. Polymers have emerged as a leading choice for designing effective protein delivery systems, but identifying a suitable polymer for a given protein is complicated by the complexity of both proteins and polymers. To address this challenge, we developed a fluorescence-based high-throughput screening platform called ProMatch to efficiently collect data on protein-polymer interactions, followed by in vivo and in vitro experiments with rational design. Using this approach to streamline polymer selection for targeted protein delivery, we identified candidate polymers from commercially available options and developed a polyhexamethylene biguanide (PHMB)-based system for delivering proteins to white adipose tissue as a treatment for obesity. We also developed a branched polyethylenimine (bPEI)-based system for neuron-specific protein delivery to stimulate optic nerve regeneration. Our high-throughput screening methodology expedites identification of promising polymer candidates for tissue-specific protein delivery systems, thereby providing a platform to develop innovative protein-based therapeutics. This article is protected by copyright. All rights reserved.

A foundational large language model for edible plant genomes

Significant progress has been made in the field of plant genomics, as demonstrated by the increased use of high-throughput methodologies that enable the characterization of multiple genome-wide molecular phenotypes. These findings have provided valuable insights into plant traits and their underlying genetic mechanisms, particularly in model plant species. Nonetheless, effectively leveraging them to make accurate predictions represents a critical step in crop genomic improvement. We present AgroNT, a foundational large language model trained on genomes from 48 plant species with a predominant focus on crop species. We show that AgroNT can obtain state-of-the-art predictions for regulatory annotations, promoter/terminator strength, tissue-specific gene expression, and prioritize functional variants. We conduct a large-scale in silico saturation mutagenesis analysis on cassava to evaluate the regulatory impact of over 10 million mutations and provide their predicted effects as a resource for variant characterization. Finally, we propose the use of the diverse datasets compiled here as the Plants Genomic Benchmark (PGB), providing a comprehensive benchmark for deep learning-based methods in plant genomic research. The pre-trained AgroNT model is publicly available on HuggingFace at https://huggingface.co/InstaDeepAI/agro-nucleotide-transformer-1b for future research purposes.

Post Natal Microbial and Metabolite Transmission: The Path from Mother to Infant.

The entero-mammary pathway is a specialized route that selectively translocates bacteria to the newborn's gut, playing a crucial role in neonatal development. Previous studies report shared bacterial and archaeal taxa between human milk and neonatal intestine. However, the functional implications for neonatal development are not fully understood due to limited evidence. This study aimed to identify and characterize the microbiota and metabolome of human milk, mother, and infant stool samples using high-throughput DNA sequencing and FT-ICR MS methodology at delivery and 4 months post-partum. Twenty-one mothers and twenty-five infants were included in this study. Our results on bacterial composition suggest vertical transmission of bacteria through breastfeeding, with major changes occurring during the first 4 months of life. Metabolite chemical characterization sheds light on the growing complexity of the metabolites. Further data integration and network analysis disclosed the interactions between different bacteria and metabolites in the biological system as well as possible unknown pathways. Our findings suggest a shared bacteriome in breastfed mother-neonate pairs, influenced by maternal lifestyle and delivery conditions, serving as probiotic agents in infants for their healthy development. Also, the presence of food biomarkers in infants suggests their origin from breast milk, implying selective vertical transmission of these features.

High-throughput assays to assess variant effects on disease.

Interpreting the wealth of rare genetic variants discovered in population-scale sequencing efforts and deciphering their associations with human health and disease present a critical challenge due to the lack of sufficient clinical case reports. One promising avenue to overcome this problem is deep mutational scanning (DMS), a method of introducing and evaluating large-scale genetic variants in model cell lines. DMS allows unbiased investigation of variants, including those that are not found in clinical reports, thus improving rare disease diagnostics. Currently, the main obstacle limiting the full potential of DMS is the availability of functional assays that are specific to disease mechanisms. Thus, we explore high-throughput functional methodologies suitable to examine broad disease mechanisms. We specifically focus on methods that do not require robotics or automation but instead use well-designed molecular tools to transform biological mechanisms into easily detectable signals, such as cell survival rate, fluorescence or drug resistance. Here, we aim to bridge the gap between disease-relevant assays and their integration into the DMS framework.

Gut Microbiota in Primary Osteoporosis: a Systematic Review

AbstractThe link between gut microbiota (GM) and primary osteoporosis has garnered substantial attention. In this study, we conducted a systematic review encompassing 10 studies that employed high-throughput sequencing methodologies. Notably, the taxonomic profiles exhibited pronounced heterogeneity across reviewed studies. A combined analysis of mechanistical studies screened out potential targets for osteoporosis prevention and treatment. We appraised the limitations in previous studies, and proposed suggestions for optimizing sequencing techniques and experimental designs. Larger-size longitudinal cohorts based on multi-omics and extended phenotypes are recommended to comprehensively characterize the involvement of GM in the pathogenesis of osteoporosis.

Predictive design of novel nickel-based superalloys beyond Haynes 282

Nickel-based superalloys are in great demand for harsh-service conditions involving high temperatures and oxidative environments. Haynes 282 stands out due to its excellent high-temperature properties and easy fabricability. However, the upper operation temperature of Haynes 282 is limited due to its relatively low liquidus temperature. Equipped with high-fidelity density-functional theory calculations and high-throughput experimentation methodology, we explored new compositional spaces that exhibit higher liquidus temperature and higher strength. While maintaining the manufacturability, the newly designed alloy shows improved strength and ductility at room temperature and better oxidation resistance up to 800 °C. The new compositions showcase a minor change in the refractory and metalloid content can significantly impact the mechanical and oxidation performance of superalloys.

The eutectic compositional space in Al-Cr-Fe-Ni system utilizing the high-throughput Calphad approach

Eutectic High-Entropy Alloys (EHEAs) show promise in balancing strength and ductility, along with excellent castability and chemical homogeneity. However, identifying optimal eutectic compositions in HEAs has been a persistent challenge, primarily due to the lacuna of multi-component phase diagrams. In addressing this challenge, the eutectic compositional space in Al-Cr-Fe-Ni is unveiled for the first time as a case study using the High-Throughput Calphad methodology. By integrating the Python application programming interface into Thermo-Calc, Latin Hypercube Sampling generated 100,000 compositions examined for eutectic formation based on thermodynamic parameters. This analysis revealed the significant correlation of valence electron concentration, enthalpy of mixing, and atomic size difference on eutectic formation. Moreover, the predictions of the proposed design methodology were compared with reported eutectic compositions, exhibiting excellent agreement. Further, a novel dual-phase eutectic alloy featuring an FCC+B2 combination predicted through the proposed design framework has been successfully cast, providing further experimental validation.

Autonomous Synthesis of Thin Film Materials with Pulsed Laser Deposition Enabled by In Situ Spectroscopy and Automation.

Autonomous systems that combine synthesis, characterization, and artificial intelligence can greatly accelerate the discovery and optimization of materials, however platforms for growth of macroscale thin films by physical vapor deposition techniques have lagged far behind others. Here this study demonstrates autonomous synthesis by pulsed laser deposition (PLD), a highly versatile synthesis technique, in the growth of ultrathin WSe2 films. By combing the automation of PLD synthesis and in situ diagnostic feedback with a high-throughput methodology, this study demonstrates a workflow and platform which uses Gaussian process regression and Bayesian optimization to autonomously identify growth regimes for WSe2 films based on Raman spectral criteria by efficiently sampling 0.25% of the chosen 4D parameter space. With throughputs at least 10x faster than traditional PLD workflows, this platform and workflow enables the accelerated discovery and autonomous optimization of the vast number of materials that can be synthesized by PLD.

High-Throughput Morphological Chirality Quantification of Twisted and Wrinkled Gold Nanorods.

Chirality in gold nanostructures offers an exciting opportunity to tune their differential optical response to left- and right-handed circularly polarized light, as well as their interactions with biomolecules and living matter. However, tuning and understanding such interactions demands quantification of the structural features that are responsible for the chiral behavior. Electron tomography (ET) enables structural characterization at the single-particle level and has been used to quantify the helicity of complex chiral nanorods. However, the technique is time-consuming and consequently lacks statistical value. To address this issue, we introduce herein a high-throughput methodology that combines images acquired by secondary electron-based electron beam-induced current (SEEBIC) with quantitative image analysis. As a result, the geometric chirality of hundreds of nanoparticles can be quantified in less than 1 h. When combining the drastic gain in data collection efficiency of SEEBIC with a limited number of ET data sets, a better understanding of how the chiral structure of individual chiral nanoparticles translates into the ensemble chiroptical response can be reached.

A new high-throughput screening methodology for the discovery of cancer-testis antigen using multi-omics data

BackgroundCancer/testis antigens (CTAs), also known as tumor-specific antigens (TSAs) are specifically expressed in cancer cells and exhibit high immunogenicity, making them promising targets for immunotherapy and cancer vaccines. MethodsA new integrated high-throughput screening methodology for CTAs was proposed in this study through combining DNA methylation and RNA sequencing data. Briefly, the genes with increased transcript level and decreased DNA methylation were identified by multi-omics analysis. RNA sequencing studies in cell lines exposed to DNA methyltransferase (DNMT) inhibitors were performed to validate the inherent causal relationship between DNA hypomethylation and gene expression upregulation. ResultsWe proposed a new integrated high-throughput screening methodology for identification of CTAs using multi-omics analysis. In addition, we tested the feasibility of this method using gastric cancer (GC) as an example. In GC, we identified over 2000 primary candidate CTAs and ultimately identified 20 CTAs with significant tissue-specificity, including a testis-specific serine protease TESSP1/PRSS41. Integrated analysis confirmed that PRSS41 expression was reactivated in gastrointestinal cancers by promoter DNA hypomethylation at the CpG site (cg08104780). Additionally, DNA hypomethylation of PRSS41 predicted a poor prognosis in GC. ConclusionWe propose a new high-throughput screening method for the identification of CTAs in cancer and validate its effectiveness. Our work emphasizes that serine protease PRSS41 is a novel TSA that is reactivated in GC due to promoter DNA hypomethylation.