High-throughput Setting Research Articles

Discovery of RNA Biomarkers for Prostate Cancer Using Cross-Platform Transcriptomics

Microarray and Single-Molecule Molecular Inversion Probe (smMIP)-based targeted RNA sequencing are two RNA profiling platforms for identifying disease-associated biomarkers. The microarray uses a GeneChip array with oligonucleotide probes to measure expression levels across thousands of genes, while smMIPs capture and quantify RNA transcripts and transcript variants via next-generation sequencing. To evaluate the strengths and weaknesses of both platforms, a comparative gene expression profiling study was conducted using RNA samples from 52 prostate tissues (normal, benign prostatic hyperplasia (BPH) and various prostate cancer (PCa) grades). Of all genes covered by both platforms, only 35% of the expression levels aligned, with 45% showing discrepancies. Both platforms identified the same 17 genes as potential PCa biomarkers. Microarray analysis identified an additional 253 genes that were not covered or not identified by smMIP technology, while smMIP technology identified eight markers not covered or not identified in the microarray core gene analysis, including fusion genes and splice variants. For high-grade prostate cancer (HG-PCa), the smMIP-method identified 8 markers, and the microarray identified 17 markers, with FOLH1, FAP and CLDN3 being common across both platforms. The choice of RNA expression analysis technology depends on research objectives; microarray technology is useful for the evaluation of a wide range of genes but has low throughput. In contrast, smMIP-based RNA sequencing enables sensitive analysis with minimal RNA in a medium- to high-throughput setting.

Parallel and High Throughput Reaction Monitoring with Computer Vision

AbstractWe report the development and applications of a computer vision based reaction monitoring method for parallel and high throughput experimentation (HTE). Whereas previous efforts reported methods to extract bulk kinetics of one reaction from one video, this new approach enables one video to capture bulk kinetics of multiple reactions running in parallel. Case studies, in and beyond well‐plate high throughput settings, are described. Analysis of parallel dye‐quenching hydroxylations, DMAP‐catalysed esterification, solid‐liquid sedimentation dynamics, metal catalyst degradation, and biologically‐relevant sugar‐mediated nitro reduction reactions have each provided insight into the scope and limitations of camera‐enabled high throughput kinetics as a means of widening known analytical bottlenecks in HTE for reaction discovery, mechanistic understanding, and optimisation. It is envisaged that the nature of the multi‐reaction time‐resolved datasets made available by this analytical approach will later serve a broad range of downstream efforts in machine learning approaches to exploring chemical space.

Patient-Derived Organoids: A Game-Changer in Personalized Cancer Medicine.

Research on cancer therapies has benefited from predictive tools capable of simulating treatment response and other disease characteristics in a personalized manner, in particular three-dimensional cell culture models. Such models include tumor-derived spheroids, multicellular spheroids including organotypic multicellular spheroids, and tumor-derived organoids. Additionally, organoids can be grown from various cancer cell types, such as pluripotent stem cells and induced pluripotent stem cells, progenitor cells, and adult stem cells. Although patient-derived xenografts and genetically engineered mouse models replicate human disease in vivo, organoids are less expensive, less labor intensive, and less time-consuming, all-important aspects in high-throughput settings. Like in vivo models, organoids mimic the three-dimensional structure, cellular heterogeneity, and functions of primary tissues, with the advantage of representing the normal oxygen conditions of patient organs. In this review, we summarize the use of organoids in disease modeling, drug discovery, toxicity testing, and precision oncology. We also summarize the current clinical trials using organoids.

New Insights for High-Throughput CO2 Hydrogenation to High-Quality Fuel.

In the case of CO2 thermal-catalytic hydrogenation, highly selective olefin generation and subsequent olefin secondary reactions to fuel hydrocarbons in an ultra-short residence time is a huge challenge, especially under industrially feasible conditions. Here, we report a pioneering synthetic process that achieves selective production of high-volume commercial gasoline with the assistance of fast response mechanism. In situ experiments and DFT calculations demonstrate that the designed NaFeGaZr presents exceptional carbiding prowess, and swiftly forms carbides even at extremely brief gas residence times, facilitating olefin production. The created successive hollow zeolite HZSM-5 further reinforces aromatization of olefin diffused from NaFeGaZr via optimized mass transfer in the hollow channel of zeolite. Benefiting from its rapid response mechanism within the multifunctional catalytic system, this catalyst effectively prevents the excessive hydrogenation of intermediates and controls the swift conversion of intermediates into aromatics, even in high-throughput settings. This enables a rapid one-step synthesis of high-quality gasoline-range hydrocarbons without any post-treatment, with high commercial product compatibility and space-time yield up to 0.9 kggasoline ⋅ kgcat -1 ⋅ h-1. These findings from the current work can provide a shed for the preparation of efficient catalysts and in-depth understanding of C1 catalysis in industrial level.

Addressing heterogeneous sensitivity in biomarker screening with application in NanoString nCounter data

Biomarkers are measurable indicators of biological processes and have wide biomedical applications including disease screening and prognosis prediction. Candidate biomarkers can be screened in high-throughput settings, which allow simultaneous measurements of a large number of molecules. For binary biomarkers, the ability to detect a molecule may be hindered by the presence of background noise and the variable signal strength, which lower the sensitivity to a different extent for different target molecules in a sample-specific manner. This heterogeneity in detection sensitivity is often overlooked and leads to an inflated false positive rate. We propose a novel sensitivity adjusted likelihood-ratio test (SALT), which properly controls the false positives and is more powerful than the unadjusted approach. We show that sample-and-feature-specific detection sensitivity can be well estimated from NanoString nCounter data, and using the estimated sensitivity in SALT results in improved biomarker screening.

Parallel and High Throughput Reaction Monitoring with Computer Vision.

We report the development and applications of a computer vision based reaction monitoring method for parallel and high throughput experimentation (HTE). Whereas previous efforts reported methods to extract bulk kinetics of one reaction from one video, this new approach enables one video to capture bulk kinetics of multiple reactions running in parallel. Case studies, in and beyond well-plate high throughput settings, are described. Analysis of parallel dye-quenching hydroxylations, DMAP-catalysed esterification, solid-liquid sedimentation dynamics, metal catalyst degradation, and biologically-relevant sugar-mediated nitro reduction reactions have each provided insight into the scope and limitations of camera-enabled high throughput kinetics as a means of widening known analytical bottlenecks in HTE for reaction discovery, mechanistic understanding, and optimisation. It is envisaged that the nature of the multi-reaction time-resolved datasets made available by this analytical approach will later serve a broad range of downstream efforts in machine learning approaches to exploring chemical space.

Recent Advances in Bacterial Detection Using Surface-Enhanced Raman Scattering.

Rapid identification of microorganisms with a high sensitivity and selectivity is of great interest in many fields, primarily in clinical diagnosis, environmental monitoring, and the food industry. For over the past decades, a surface-enhanced Raman scattering (SERS)-based detection platform has been extensively used for bacterial detection, and the effort has been extended to clinical, environmental, and food samples. In contrast to other approaches, such as enzyme-linked immunosorbent assays and polymerase chain reaction, SERS exhibits outstanding advantages of rapid detection, being culture-free, low cost, high sensitivity, and lack of water interference. This review aims to cover the development of SERS-based methods for bacterial detection with an emphasis on the source of the signal, techniques used to improve the limit of detection and specificity, and the application of SERS in high-throughput settings and complex samples. The challenges and advancements with the implementation of artificial intelligence (AI) are also discussed.

Discrete frequency infrared-guided image for microplastic analysis: Performance and limitations

Applying a quantum cascade laser as a light source in a benchtop infrared spectrometer has enabled a new strategy for microplastic analysis. This strategy uses discrete frequency infrared images to recognize particles from a reflecting substrate prior to chemical identification, thereby improving analysis throughput.This research work established the performance of the approach using an Agilent Laser Direct Infrared chemical imaging system (LDIR). Over 90 % of fluorescently labelled polyethylene particles (> 20 µm) spiked in the environmental matrix were correctly identified at scanning wavenumbers and sensitivity settings. When the high-throughput setting was used, the discrete frequency infrared image-guided microplastic analysis demonstrated good selectivity. With around 30 % of particles in the sample scanned, the technique could recognize around 86 % percent of fluorescently labelled polyethylene particles introduced.The MirrIR low-e slide and gold-coated filter are suitable reflecting substrates for this particle analysis strategy. When detecting small microplastic particles with sizes between 5 and 20 µm, around 94 % spiked polymethyl methacrylate particles could be found on the MirrIR low-e slide. The analysis also showed good accuracy and precision in a three-day test. The texture on the gold-coated filter surface could interfere with the small particle analysis.Overall, this novel discrete frequency infrared image-guided particle analysis shows good accuracy, high precision, and respectable selectivity, which are all critical characteristics for microplastic analysis.

Review of predicting protein stability changes upon variations.

Forecasting alterations in protein stability caused by variations holds immense importance. Improving the thermal stability of proteins is important for biomedical and industrial applications. This review discusses the latest methods for predicting the effects of mutations on protein stability, databases containing protein mutations and thermodynamic parameters, and experimental techniques for efficiently assessing protein stability in high-throughput settings. Various publicly available databases for protein stability prediction are introduced. Furthermore, state-of-the-art computational approaches for anticipating protein stability changes due to variants are reviewed. Each method's types of features, base algorithm, and prediction results are also detailed. Additionally, some experimental approaches for verifying the prediction results of computational methods are introduced. Finally, the review summarizes the progress and challenges of protein stability prediction and discusses potential models for future research directions.

High-Throughput Acoustic Ejection Mass Spectrometry with Adjustable Signal Durations.

High-throughput mass spectrometry (MS) has witnessed rapid advancements and has found extensive applications across various disciplines. It enables the fast and accurate analysis of large sample sets, delivering a 10-fold or greater enhancement in analytical throughput when compared to conventional LC-MS methods. However, the signal duration in these high-throughput MS technologies is typically confined to a narrow range, presenting challenges for workflows demanding prolonged signal durations. In this study, we introduce a method that enables precise modulation of the signal duration on an acoustic ejection mass spectrometry (AEMS) system while ensuring high signal reproducibility. This flexibility allows for simultaneous and precise analysis of a significantly greater number of MS/MS transitions in high-throughput MS environments. Additionally, it offers a unique approach for parameter optimization and method development with minimal sample volume requirements. This advancement enhances the efficiency of MS-based analyses across diverse applications and facilitates broader utilization of MS technologies in high-throughput settings, including data-dependent acquisition (DDA) and data-independent acquisition (DIA).

Abstract 6878: Images to insights: Transforming TME characterization into actionable spatial metrics with AI-powered multiplex immunofluorescence

Abstract Background: Precise and actionable assessment of cellular mechanisms and interactions within the tumor microenvironment (TME) is critical for advancing clinical trials, preclinical research, and informed decision-making in immuno-oncology. Multiplex immunofluorescence (mIF) assays have emerged as pivotal tools that can enable comprehensive visualization of protein expression and cellular interactions within the TME. Integration of advanced digital analysis tools and mIF assays, can revolutionize our ability to interpret the TME and usher these assays into high-throughput clinical settings. Methods: In this study, we processed tumor samples utilizing Akoya's PhenoCode Signature Immune Profile Human Protein Panel, which includes immune profiling markers CD8, CD68, CD3, CD20 and tumor marker PanCK. Following the Akoya Opal staining protocol, formalin-fixed paraffin-embedded (FFPE) samples were stained and scanned using the PhenoImager HT scanner. The images were then analyzed using Reveal’s AI-mIF digital assay on the ImageDx platform to generate contextual and spatial biomarker data. A deep learning-based cell segmentation model based on MaskRCNN architecture, signal preprocessing for noise and autofluorescence, and dynamic intensity normalization were used. Phenotype classification was precisely executed using dynamic thresholding or neural network classifiers as needed. Results were validated by establishing concordance with pathologist readouts. Results: The application of our methodology yielded highly concordant results when compared with manual counts, demonstrating the efficacy of our approach. The R2 correlation coefficient across all markers passed the minimum criteria of R2>0.85 with regression slopes between 0.7-1.3, indicating a robust alignment with traditional pathologist assessments. We showcase stratifying samples Desert, Excluded, or Inflamed phenotypes and other quantitative and spatial results such as interactions of immune cells and tumor within 30-um neighborhoods, and their dispersion and arrangement in the TME. Conclusion: The integration of Reveal Biosciences' ImageDx platform and RevealAI-mIF provides an ecosystem to host, visualize, and analyze Akoya’s immune profiling images. This collaborative effort has enabled the transformation of complex multiplex imaging data into distinct spatial phenomic endpoints, thereby introducing the potential to significantly advance biomarker discovery and contribution to translational and clinical research in the field of immuno-oncology. Citation Format: Misagh Naderi, Lazar Krstic, Djordje Cikic, Kristen Ruf, Igor Mihajlovic, Sinisa Todorovic, Claire Weston. Images to insights: Transforming TME characterization into actionable spatial metrics with AI-powered multiplex immunofluorescence [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 6878.

Rapid high-throughput processing of tissue samples for microbiological diagnosis of periprosthetic joint infections using bead-beating homogenization.

Enrichment of periprosthetic tissue samples in blood culture bottles (BCBs) for microbiological diagnosis of periprosthetic joint infections (PJI) is more reliable than the use of an enrichment broth. Nevertheless, the extremely time-consuming homogenization of the samples for BCB processing has so far limited its use, especially in high-throughput settings. We aimed to establish a highly scalable homogenization process of tissue samples for long-term incubation in BCBs. A protocol for homogenization of tissue samples using bead beating was established and validated. In a second step, the use of the homogenate for enrichment in BCBs was compared to the use of thioglycolate broth (TB) in terms of diagnostic accuracy using clinical tissue samples from 150 patients with suspected PJI. Among 150 analyzed samples, 35 samples met the microbiological criteria for PJI. Using BCB, 32 of 35 (91.4%) PJI were detected compared to 30 of 35 (85.7%) by TB. The use of BCB had a lower secondary contamination rate (2/115; 1.7% vs 4/115; 3.5%) but the trend was not significant due to low numbers of samples (P = 0.39). The time to process a batch of 12 samples using the established homogenization method was 23 ± 5 min (n = 10 batches). We established and validated a homogenization workflow that achieves the highest sensitivity in the microbiological diagnostic of PJI. The enrichment of the tissue homogenate in BCBs showed equally good results as the use of enrichment broth and allows semi-automated high-throughput processing while demonstrating lower contamination rates in our study.

Abstract 13988: Post Stress Test High-Sensitivity Cardiac Troponin I in the Evaluation of Patients With Acute Chest Pain for Enhanced Ischemic Risk Assessment: A Pilot Study

Introduction: High-sensitivity cardiac troponin I (hs-cTnI) assays provide comparable diagnostic information that allow for better detection of myocardial injury. However, hs-cTnI release after cardiac stress testing and its potential utility for identifying inducible myocardial ischemia and risk-stratification are unclear. Aim: This pilot study examined integrating hs-cTnI biomarker testing into cardiac imaging stress testing to improve detection of inducible ischemia and aid in risk-stratification in patients with chest pain being evaluated for acute coronary syndrome (ACS). Methods: We prospectively enrolled 200 consecutive adult patients referred by the Emergency Room for cardiac stress testing for evaluation of possible ACS. A venous blood sample was collected between 0.5 and 2 hours post stress testing; cTnI concentrations were measured in-house by high sensitivity methods. Changes from pre-stress test hs-cTnI concentrations were calculated and association with outcome was explored using a logistic regression model. Results: Patient median age was 62 years, IQR 54-70; 49% were women. ACS known risk factors included hypertension 81.5%, cigarette smoking 49.5% and diabetes 33.5%. Median BMI was 31.63 kg/m 2 , IQR 27.24-35.84. Pre-stress test hs-cTnI median levels were 6 ng/L, IQR 5-9, for women (URL <52 ng/L) and 8 ng/L, IQR 6-12, for men (URL <77 ng/L). A total of 121 patients (60.5%) underwent nuclear stress tests, and 79 (39.5%) received echocardiographic stress tests. Of the 191 samples analyzed, 36% revealed increased hs-cTnI levels from pre-stress test values: 33% in nuclear stress tests (median increase 2 ng/L, IQR 1-5), 41% in echocardiographic stress tests (median increase 2 ng/L, IQR 1-5). In the 155 patients with 6 months of follow-up, changes in post-stress test hs-cTnI levels were not significantly correlated to stress test results, percutaneous coronary interventions or death (p=.85). Conclusion: This study provides insight into the potential of hs-cTnI to aid in identifying inducible myocardial ischemia in a real-world, high-throughput setting. Although a subset of patients exhibited modest increases in hs-cTnI post stress testing, overall, these did not appear to be correlated with inducible myocardial ischemia.

Recent advances in the application of induced pluripotent stem cell technology to the study of myeloid malignancies

Acquired myeloid malignancies are a spectrum of clonal disorders known to be caused by sequential acquisition of genetic lesions in hematopoietic stem and progenitor cells, leading to their aberrant self-renewal and differentiation. The increasing use of induced pluripotent stem cell (iPSC) technology to study myeloid malignancies has helped usher a paradigm shift in approaches to disease modeling and drug discovery, especially when combined with gene-editing technology. The process of reprogramming allows for the capture of the diversity of genetic lesions and mutational burden found in primary patient samples into individual stable iPSC lines. Patient-derived iPSC lines, owing to their self-renewal and differentiation capacity, can thus be a homogenous source of disease relevant material that allow for the study of disease pathogenesis using various functional read-outs. Furthermore, genome editing technologies like CRISPR/Cas9 enable the study of the stepwise progression from normal to malignant hematopoiesis through the introduction of specific driver mutations, individually or in combination, to create isogenic lines for comparison. In this review, we survey the current use of iPSCs to model acquired myeloid malignancies including myelodysplastic syndromes (MDS), myeloproliferative neoplasms (MPN), acute myeloid leukemia and MDS/MPN overlap syndromes. The use of iPSCs has enabled the interrogation of the underlying mechanism of initiation and progression driving these diseases. It has also made drug testing, repurposing, and the discovery of novel therapies for these diseases possible in a high throughput setting.

Fluorescence-based thermal stability screening is concentration-dependent and varies with protein size

Proteins are used in many areas including diagnostic and therapeutical applications. Screening protein stability is an essential step for production, pharmacokinetic/pharmacodynamic studies, and determination of storage conditions. Instability of proteins can cause serious problems such as activity loss and unexpected adverse effects, so determination of sensitive and reliable methods for protein stability measurement is crucial. There are several “gold-standard” protein stability tests such as differential scanning calorimetry (DSC), but they are usually not suitable for high-throughput settings and consume large amounts of proteins. Instead, more high-throughput methods such as fluorescent-based assays can be used and validated to make stability screening process more straight-forward, easier, and lower-cost. Here, two methods were systemically compared to see whether their measurements depended on protein sizes. DSC and Sypro Orange dye-based fluorescent assay were compared for various proteins with different sizes and quaternary structures. This is the first systemic comparison of these two methods for thermal stability testing for different ranges of proteins in the literature. It was shown that protein melting temperature (Tm) measured by fluorescent assay highly depends on protein concentration and protein size. Larger proteins with multi-domain structures such as monoclonal antibodies gave more deviated and lower than expected Tms compared to small proteins. It has been concluded that fluorescent-based thermal stability assays are more suitable for smaller proteins, but protein concentrations used are still needed to be optimized in their settings for more reliable results.

Comparison of Total Nitrogen estimation by Kjeldahl Method and CHNS Analyzer in Dry Tropical Grassland

The Dumas method (CHNS analyzer) is replacing the traditional Kjeldahl method as the method of choice for N analysis due to advancements in dry combustion nitrogen (N) analyzer technology and the high cost of disposing of hazardous laboratory waste chemicals. As a result, a comparison of the Dumas method (CHNS analyzer) with the Kjeldahl method is critical. Typically, such comparisons were conducted on a small number of distinct samples. The goal of this study was to compare the performance of instruments that use the automated Dumas method (CHNS analyzer) and the Kjeldahl method for N analysis of agricultural materials in a high-throughput laboratory setting. Using both instruments, N concentrations in manure, sewage sludge, plant tissue, plant seeds, and feedstuff fibres were determined. We collected samples for the analysis of organic matter from the horticulture area at the Banaras Hindu University campus in Varanasi, and total Nitrogen (N) was estimated using the Kjeldahl (wet oxidation) and Perkin-Elmer 2.400 Series II-CHN Mode techniques (dry oxidation or combustion). The positive correlation (r = 0.94**) of the Kjeldahl method with N- determination from a CHNS analyzer demonstrates that the method is practical for routinely determining the total nitrogen content of soils

Discovery of DTX3L inhibitors through a homogeneous FRET-based assay that monitors formation and removal of poly-ubiquitin chains

Ubiquitination is a reversible protein post-translational modification in which consequent enzymatic activity results in the covalent linking of ubiquitin to a target protein. Once ubiquitinated, a protein can undergo multiple rounds of ubiquitination on multiple sites or form poly-ubiquitin chains. Ubiquitination regulates various cellular processes, and dysregulation of ubiquitination has been associated with more than one type of cancer. Therefore, efforts have been carried out to identify modulators of the ubiquitination cascade. Herein, we present the development of a FRET-based assay that allows us to monitor ubiquitination activity of DTX3L, a RING-type E3 ubiquitin ligase. Our method shows a good signal window with a robust average Z’ factor of 0.76 on 384-well microplates, indicating a good assay for screening inhibitors in a high-throughput setting. From a validatory screening experiment, we have identified the first molecules that inhibit DTX3L with potencies in the low micromolar range. We also demonstrate that the method can be expanded to study deubiquitinases, such as USP28, that reduce FRET due to hydrolysis of fluorescent poly-ubiquitin chains.

A CRISPR/Cas9-Based Assay for High-Throughput Studies of Cancer-Induced Innervation

The aggressive nature of certain cancers and their adverse effects on patient outcomes have been linked to cancer innervation, where neurons infiltrate and differentiate within the cancer stroma. Recently we demonstrated how cancer plasticity and TGFβ signaling could promote breast cancer innervation that is associated with increased cancer aggressivity. Despite the promising potential of cancer innervation as a target for anti-cancer therapies, there is currently a significant lack of effective methods to study cancer-induced neuronal differentiation, hindering the development of high-throughput approaches for identifying new targets or pharmacological inhibitors against cancer innervation. To overcome this challenge, we used CRISPR-based endogenous labeling of the neuronal marker β3-tubulin in neuronal precursors to investigate cancer-induced neuronal differentiation in nerve-cancer cocultures and provide a tool that allows for better standardization and reproducibility of studies about cancer-induced innervation. Our approach demonstrated that β3-tubulin gene editing did not affect neuronal behavior and enabled accurate reporting of cancer-induced neuronal differentiation dynamics in high-throughput settings, which makes this approach suitable for screening large cohorts of cells or testing various biological contexts. In a more context-based approach, by combining this method with a cell model of breast cancer epithelial-mesenchymal transition, we revealed the role of cancer cell plasticity in promoting neuronal differentiation, suggesting that cancer innervation represents an underexplored path for epithelial-mesenchymal transition-mediated cancer aggressivity.

Toward high-throughput engineering techniques for improving CAR intracellular signaling domains.

Chimeric antigen receptors (CAR) are generated by linking extracellular antigen recognition domains with one or more intracellular signaling domains derived from the T-cell receptor complex or various co-stimulatory receptors. The choice and relative positioning of signaling domains help to determine chimeric antigen receptors T-cell activity and fate in vivo. While prior studies have focused on optimizing signaling power through combinatorial investigation of native intracellular signaling domains in modular fashion, few have investigated the prospect of sequence engineering within domains. Here, we sought to develop a novel in situ screening method that could permit deployment of directed evolution approaches to identify intracellular domain variants that drive selective induction of transcription factors. To accomplish this goal, we evaluated a screening approach based on the activation of a human NF-κB and NFAT reporter T-cell line for the isolation of mutations that directly impact T cell activation in vitro. As a proof-of-concept, a model library of chimeric antigen receptors signaling domain variants was constructed and used to demonstrate the ability to discern amongst chimeric antigen receptors containing different co-stimulatory domains. A rare, higher-signaling variant with frequency as low as 1 in 1000 could be identified in a high throughput setting. Collectively, this work highlights both prospects and limitations of novel mammalian display methods for chimeric antigen receptors signaling domain discovery and points to potential strategies for future chimeric antigen receptors development.

Zebrafish Larvae Position Tracker (Z-LaP Tracker): a high-throughput deep-learning behavioral approach for the identification of calcineurin pathway-modulating drugs using zebrafish larvae

Brain function studies greatly depend on quantification and analysis of behavior. While behavior can be imaged efficiently, the quantification of specific aspects of behavior is labor-intensive and may introduce individual biases. Recent advances in deep learning and artificial intelligence-based tools have made it possible to precisely track individual features of freely moving animals in diverse environments without any markers. In the current study, we developed Zebrafish Larvae Position Tracker (Z-LaP Tracker), a modification of the markerless position estimation software DeepLabCut, to quantify zebrafish larval behavior in a high-throughput 384-well setting. We utilized the high-contrast features of our model animal, zebrafish larvae, including the eyes and the yolk for our behavioral analysis. Using this experimental setup, we quantified relevant behaviors with similar accuracy to the analysis performed by humans. The changes in behavior were organized in behavioral profiles, which were examined by K-means and hierarchical cluster analysis. Calcineurin inhibitors exhibited a distinct behavioral profile characterized by increased activity, acoustic hyperexcitability, reduced visually guided behaviors, and reduced habituation to acoustic stimuli. The developed methodologies were used to identify ‘CsA-type’ drugs that might be promising candidates for the prevention and treatment of neurological disorders.