General motivations of automated phenotyping on blood donors on Groupamatic: actual possibilities.
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ABO Blood-Group System↗
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Results for “Automated phenotyping”
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Replacement of chromosome 3D with Thinopyrum chromosome 3St led to increased drought tolerance during the flowering stage in wheat.
The stable 3St(3D) substitution line offers promising genetic potential for improving drought tolerance in wheat during critical reproductive stages. The flowering stage is highly susceptible to drought, which significantly reduces wheat grain yield globally. Low genetic diversity in wheat further limits the discovery of optimal gene variants for breeding climate-resilient varieties. The substitution of chromosome 3D by a group 3 chromosome pair from Thinopyrum intermedium × Th. ponticum artificial hybrid was identified using in situ hybridization and genotyping-by-sequencing. This homoeologous substitution showed good functional compensation for grain yield and fertility, similar to the wheat parents ('Mv9kr1' and 'Mv Karizma') in field and greenhouse trials. The substitution line exhibits a semidwarf phenotype due to the Rht8 and Rht2 dwarfing alleles. Automated shoot phenotyping after a 10-day water withdrawal at flowering revealed efficient water preservation allowing to maintain photosynthetic functions, sustained photosynthetic activity, and less chlorophyll degradation, indicated by Normalized Difference Vegetation Index (NDVI) and modified Normalized Difference Index (mND705) values and moderate level of protective functions shown by the expression of stress-related genes. Compared to the wheat parents, the substitution line developed thicker roots with increased volume under drought, resulting in a lower surface-to-volume ratio. This may enhance water storage efficiency and help reduce yield loss under drought conditions.
Triticum↗
DeepLabCut-based automated system reveals diverse temperature tolerance among medaka strains and related Oryzias species.
Temperature is a critical environmental factor influencing the physiology and behavior of ectothermic animals, yet conventional methods for evaluating thermal tolerance in fish rely on subjective manual observation of loss of equilibrium (LOE), limiting experimental throughput and introducing observer bias. Here, we developed an automated temperature tolerance evaluation system integrating DeepLabCut-based pose estimation with custom image processing algorithms to objectively quantify the timing of LOE during thermal stress tests. Our system incorporated region partitioning and color transformation preprocessing to improve keypoint detection accuracy, followed by a classification model combining ResNet34-based frame features with keypoint coordinates to objectively determine the timing of LOE without manual observation. Validation against manual annotation showed that the automated system achieved an accuracy comparable to the natural variability between trained investigators, and outperformed naive human observers, supporting its validity as an objective and reproducible alternative to manual scoring. Using this system, we characterized cold and heat tolerance across six medaka strains (Oryzias latipes: d-rR/TOKYO, HB11A, OK-Cab, HO5 and HdrR-II1; O. sakaizumii: HNI-II). Cold and heat tolerance assessment revealed inter-strain variation, with HdrR-II1 among the most cold- and heat-tolerant strains and HNI-II the least tolerant of both cold and heat stress. We further evaluated cold tolerance in medaka-related species (O. sinensis, O. cabaranensis, O. curvinotus, O. luzonensis, O. celebensis, and O. javanicus) and zebrafish (Danio rerio), revealing substantial interspecific variation that broadly corresponded with latitudinal distribution. O. latipes, distributed at the highest latitudes among the tested species, exhibited the greatest cold tolerance, whereas O. celebensis, O. javanicus, and other tropical or low-latitude species showed comparatively low cold tolerance. Our automated system provides a robust, high-throughput platform for thermal tolerance evaluation and, combined with the genetic and genomic resources available in medaka, establishes a foundation for elucidating the molecular mechanisms underlying temperature adaptation in fish.
Animals↗
Evaluation of an automated immunoassay procedure for apo-LDL for phenotyping type II hyperlipoproteinemia.
Apo-low density lipoproteins were determined by an automated immunoassay procedure on serum samples from 88 normolipidemic individuals and 84 hyperlipoproteinemic subjects, to establish whether this method was useful in the routine detection of type II hyperlipoproteinemia. The results obtained were compared with the cholesterol levels of the same specimens. In subjects with type II hyperlipoproteinemia, the apo low density lipoprotein levels, as well as the ratio of low density lipoprotein cholesterol/apo-low density lipoprotein were higher, as expected, than in normals or in subjects with other types of hyperlipoproteinemia. However, there was considerable overlap in individual values of both these parameters, between patients with type II hyperlipoproteinemia and normals or subjects with other types of hyperlipoproteinemia, suggesting that apo low density lipoprotein levels alone were not sufficiently discriminatory for the laboratory determination of type II hyperlipoproteinemia.
Adult↗
Tribus: semi-automated discovery of cell identities and phenotypes from multiplexed imaging and proteomic data.
MOTIVATION: Multiplexed imaging and single-cell analysis are increasingly applied to investigate the tissue spatial ecosystems in cancer and other complex diseases. Accurate single-cell phenotyping based on marker combinations is a critical but challenging task due to (i) low reproducibility across experiments with manual thresholding, and, (ii) labor-intensive ground-truth expert annotation required for learning-based methods. RESULTS: We developed Tribus, an interactive knowledge-based classifier for multiplexed images and proteomic datasets that avoids hard-set thresholds and manual labeling. We demonstrated that Tribus recovers fine-grained cell types, matching the gold standard annotations by human experts. Additionally, Tribus can target ambiguous populations and discover phenotypically distinct cell subtypes. Through benchmarking against three similar methods in four public datasets with ground truth labels, we show that Tribus outperforms other methods in accuracy and computational efficiency, reducing runtime by an order of magnitude. Finally, we demonstrate the performance of Tribus in rapid and precise cell phenotyping with two large in-house whole-slide imaging datasets. AVAILABILITY AND IMPLEMENTATION: Tribus is available at https://github.com/farkkilab/tribus as an open-source Python package.
Proteomics↗
A semi-automated Bratton & Marshall micromethod for determining acetylator phenotype of rabbits using the Abbott Bichromatic Analyzer-100.
A micromethod for the determination of acetylator phenotype using the method of Bratton & Marshall (1939) J. Biol. Chem. 128, 537--550) was adapted to the Abbott Bichromatic Analyzer (ABA-100). Advantages of this procedure include: (a) the requirement for small blood samples (20 microliter) making it a suitable pediatric method and eliminating the need for venipunctures; (b) elimination of tedious manual reading of absorbances of samples and mathematical manipulation of data by the automated capabilities of the ABA-100; (c) an increase in the accuracy of the assay by reading samples at two wavelengths thereby correcting for differences in background.
Acetylation↗
Genetic basis of escape-related locomotor performance in a wild-introgressed sheep population.
Rapid running and jumping are core components of escape responses in prey animals and provide measurable traits for studying locomotor performance in large mammals. The genetic basis of these escape-related locomotor traits remains poorly understood in large mammals, partly because repeated, standardized phenotyping under field conditions is challenging. Here, we leveraged a sheep population carrying argali-introgressed genetic components to map genetic variations associated with running speed and jumping height. Through controlled field experiments, automated high-resolution phenotyping, whole-genome analysis, and gene-edited mouse models, we identified two loci associated with escape-related locomotor traits: one in ABCC4 (Chr10:71,849,347; p = 5.03 × 10-7) linked to maximum running speed and another in GRID2 (Chr6:32,120,477; p = 1.30 × 10-9) associated with jumping height. Functional assays in knockout mice reveal that disruption of Grid2 reduces jumping ability, whereas Abcc4 knockout and knockdown increase running speed through enhanced heart contractility under stress. These results elucidated the genetic bases of wild-derived variations in affecting locomotor performance.
Animals↗
Zebrafish as a versatile model in biomedical research, from disease modeling to regenerative medicine: a review.
Zebrafish are an effective animal model widely utilized in biomedical research. They are known for their rapid reproduction and substantial genetic similarity to humans. Their transparent embryos directly enable the visualization of developmental processes and disease progression. This makes zebrafish invaluable for studying a broad range of human diseases, including cancer, cardiovascular disorders, and neurodegenerative conditions. Compared with other vertebrate models, zebrafish offer several advantages, including ease of genome editing, cost-effective maintenance, and suitability for high-throughput drug screening. Recent advancements have expanded the use of zebrafish in disease modeling and regenerative medicine, providing deeper insights into the genetic and cellular mechanisms underlying human pathologies. Zebrafish provide a robust platform for evaluating the safety, efficacy, and regenerative potential of both natural and synthetic biomaterials, including hydroxyapatite, bioactive glass nanoparticles, and bioceramics. This capability facilitates the creation of artificial tissues that closely resemble native structures. Additionally, integrating artificial intelligence technologies has improved automated data analysis and phenotyping in zebrafish studies, enhancing both accuracy and throughput. This review highlights current applications of zebrafish in disease modeling, drug discovery, regenerative medicine, and biomaterial assessment, emphasizing their evolving role as a versatile preclinical platform supported by advanced genetic and computational tools.
Animals↗
DeepPlaque: a scalable multimodal platform for Aβ pathology and cell analysis in Alzheimer's disease.
Histological analysis is essential for understanding disease pathology and the microenvironment, particularly in Alzheimer's disease (AD), characterized by beta-amyloid (Aβ) plaques that exist as diffuse, fibrillar, and core species, with distinct toxicity levels. However, accurate classification of Aβ plaque types in postmortem brain tissues and profiling of surrounding cells present significant challenges. To address these challenges, we developed "DeepPlaque", an integrated system featuring "PlaqueNet", a deep learning model for automated classification of Aβ plaque species from diverse imaging platforms. DeepPlaque includes automated workflows for cellular phenotyping and proteomic profiling through targeted laser microdissection. PlaqueNet achieves expert-level accuracy (AUC > 90%) in classifying the 3 major Aβ plaque species, supporting consistent and large-scale annotation. By integrating spatial cellular phenotyping with laser microdissection, DeepPlaque enables high-throughput proteomic analysis of Aβ plaque niches, revealing that microglia are more abundant around core and fibrillar Aβ plaques, with increased expression of apolipoprotein E and amyloid precursor protein in core Aβ plaques. This customizable platform enhances the molecular and cellular characterization of Aβ plaque-associated environments, providing critical insights into AD pathology.
Alzheimer Disease↗
SNPannotator: automated functional annotation of genetic variants and linked proxies.
SUMMARY: Genome-wide association studies (GWASs) have identified thousands of genetic variants associated with complex traits and diseases. However, explaining the mechanisms underlying phenotypic variation remains challenging. Here, we introduce SNPannotator, an automated post-GWAS analysis software package designed to streamline the interpretation of GWAS findings. Our pipeline implements a multi-step process that identifies proxy variants in high linkage disequilibrium (LD) with associated lead variants, then queries comprehensive resources (including Ensembl, the GTEx Portal, the eQTL Catalog, and STRING DB) for genomic position, deleteriousness, regulatory annotations, clinical significance, trait associations, expression (eQTLs) and splicing quantitative trait loci (sQTLs), and functional enrichment analyses and compiles the results into user-friendly reports. This package is implemented in the R programming language and includes auxiliary functions for variant lookup and LD exploration. SNPannotator provides a practical framework for efficiently deriving biologically meaningful insights from GWAS data and for assisting researchers in prioritizing candidate variants for functional validation. AVAILABILITY AND IMPLEMENTATION: The SNPannotator package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/SNPannotator. The development version and tutorial is available on GitHub (https://github.com/omicslaboratory/SNPannotator). The online version of the package is available at https://omicslab.org/snpannotator.
Software↗
ClearDepthIAS enables automated high-throughput quantification of roots in soil-grown taproot crops.
Understanding root system architecture is critical for improving crop productivity and resilience, yet phenotyping root traits such as root growth angle and rooting depth remains technically challenging, especially at high throughput. Here, we present ClearDepthIAS, a high-throughput imaging and analysis platform that enables nondestructive, automated quantification of root architecture traits in taproot system crops. By capturing and stitching 360° images of roots growing along the transparent walls of pots and applying deep learning-based segmentation (ClearDepth-WRT), we measured wall root shallowness (WRS)-a proxy for root growth angle-with high precision. We demonstrated for the tap root systems of soybean and canola that the system accurately detects root tips, quantifies their vertical distribution, and extracts biologically meaningful traits such as root area, distribution indices, and growth angles. Validation experiments in canola and soybean demonstrated that WRS can correlate with root crown architecture in mature plants, both in greenhouse and field settings. Furthermore, WRS and root distribution indices derived from ClearDepthIAS are predictors of early root architecture and can be correlated with root biomass distribution across soil depths under field conditions; however, environmental interactions may influence these relationships and weaken or even negate such correlations, as observed when comparing field to field variation in root system architecture. Our system enables efficient phenotyping of genetically diverse populations, with medium to high trait heritability, supporting its utility for genome-wide association studies and breeding. ClearDepthIAS accelerates the development of root ideotypes for improved resource acquisition and carbon sequestration, offering a scalable tool for supporting climate-resilient agriculture.
Plant Roots↗
Accelerating natural product discovery, characterization and engineering by biofoundries.
Covering: From early developments to the presentNatural product (NP) discovery is increasingly constrained by low-throughput screening, repeated rediscovery, and challenges in scaling genome mining-guided validation workflows. This highlight examines how automated biofoundries are accelerating NP discovery, characterization, and engineering through integrated design-build-test-learn (DBTL) cycles. We discuss recent advances in phenotype-first and genome-first discovery strategies enabled by robotics, high-throughput pathway reconstitution, and automated screening platforms. We further highlight emerging technologies, including cell-free biosynthesis, automated culturomics, programmable chassis engineering, and AI-assisted workflow orchestration, that may enable increasingly autonomous biofoundries for scalable exploration of NP chemical space and therapeutic discovery.
Journal Article↗
Automated Deep Learning-Based Detection of Early Atherosclerotic Plaques in Carotid Ultrasound Imaging.
BACKGROUND: Carotid plaque presence is associated with cardiovascular risk, even among asymptomatic individuals. While deep learning has shown promise for carotid plaque phenotyping in patients with advanced atherosclerosis, its application in population-based settings of asymptomatic individuals remains unexplored. METHODS: We developed a YOLOv8-based model for plaque detection using carotid ultrasound images from 19,499 participants of the population-based UK Biobank (UKB) and fine-tuned it for external validation in the BiDirect study (N = 2,105). Cox regression was used to estimate the impact of plaque presence and count on major cardiovascular events. To explore the genetic architecture of carotid atherosclerosis, we conducted a genome-wide association study (GWAS) meta-analysis of the UKB and CHARGE cohorts. Mendelian randomization (MR) assessed the effect of genetic predisposition to vascular risk factors on carotid atherosclerosis. RESULTS: Our model demonstrated high performance with accuracy, sensitivity, and specificity exceeding 85%, enabling identification of carotid plaques in 45% of the UKB population (aged 47-83 years). In the external BiDirect cohort, a fine-tuned model achieved 86% accuracy, 78% sensitivity, and 90% specificity. Plaque presence and count were associated with risk of major adverse cardiovascular events (MACE) over a follow-up of up to seven years, improving risk reclassification beyond the Pooled Cohort Equations. A GWAS meta-analysis of carotid plaques uncovered two novel genomic loci, with downstream analyses implicating targets of investigational drugs in advanced clinical development. Observational and MR analyses showed associations between smoking, LDL cholesterol, hypertension, and odds of carotid atherosclerosis. CONCLUSIONS: Our model offers a scalable solution for early carotid plaque detection, potentially enabling automated screening in asymptomatic individuals and improving plaque phenotyping in population-based cohorts. This approach could advance large-scale atherosclerosis research.
atherosclerosis↗
Unique growth and morphology properties of Clade 5 Clostridioides difficile strains revealed by single-cell time-lapse microscopy.
Clostridioides difficile is a gastrointestinal pathogen of both humans and agricultural animals and thus a major One Health threat. The C. difficile species consists of five main clades, with Clade 5 currently undergoing speciation from Clades 1-4. Clade 5 strains are highly prevalent in agricultural animals and can cause zoonotic infections, suggesting that these strains have evolved phenotypes that distinguish them from Clade 1-4 strains. Here, we compare the growth properties of Clade 5 strains to those of Clade 1-4 strains using anaerobic time-lapse microscopy coupled with automated image analysis. Our analyses indicate that Clade 5 strains grow faster and are more likely to form long chains of cells than Clade 1-4 strains. Using comparative genomic and CRISPRi analyses, we show that the chaining phenotype of Clade 5 strains is driven by the orientation of the invertible cmr switch sequence, with chaining strains exhibiting a bias to the cmr-ON state. Interestingly, Clade 5 strains with a bias towards the cmr-ON state shifted to a largely cmr-OFF state during murine infection, suggesting that the cmr-OFF state is under positive selection during infection. Collectively, our data reveal that Clade 5 strains have distinct growth properties, which may allow them to inhabit diverse ecological niches.
Journal Article↗
Unique growth and morphology properties of Clade 5 Clostridioides difficile strains revealed by single-cell time-lapse microscopy.
Clostridioides difficile is a gastrointestinal pathogen of both humans and agricultural animals and thus a major One Health threat. The C. difficile species consists of five main clades, with Clade 5 currently undergoing speciation from Clades 1-4. Since Clade 5 strains are highly prevalent in agricultural animals and a frequent cause of zoonotic infections, these strains may have evolved phenotypes that distinguish them from Clade 1-4 strains. Here, we compare the growth properties of Clade 5 strains to those of Clade 1-4 strains using anaerobic time-lapse microscopy coupled with automated image analysis. Our analyses indicate that Clade 5 strains grow faster and are more likely to form long chains of cells than Clade 1-4 strains. Using comparative genomic and CRISPRi analyses, we show that the chaining phenotype of Clade 5 strains is driven by the orientation of the invertible cmr switch sequence, with chaining strains exhibiting a bias to the cmr-ON state. Interestingly, Clade 5 strains with a bias towards the cmr-ON state shifted to a largely cmr-OFF state during murine infection, suggesting that the cmr-OFF state is under positive selection during infection. Collectively, our data reveal that Clade 5 strains have distinct growth properties, which may allow them to inhabit diverse ecological niches.
Clostridioides difficile↗
Is immunochemical determination of haptoglobin phenotype dependent?
Immunochemical methods have been used to determine the concentration of haptoglobins. The dependence on the phenotype was tested with highly purified Hp 2-1, Hp 2-2 and Hp 1-1, by immunonephelometry and radial immunodiffusion (RID). Measurements with three different instruments: automated immunonephelometer (AIP, Technicon), laser nephelometer (LN, Behring) and immunochemistry system (ICS, Beckman) were performed. For each type of apparatus antisera against a pool of haptoglobins were provided by the respective manufacturers. Some experiments were done with an antiserum to the haptoglobin heavy chain prepared in the laboratory. This study shows that haptoglobin determination depends neither on the physical geometry of the instruments or on the type of antiserum used in this work. In contrast, the data display a dependence on haptoglobin phenotype. When Hp 2-1, the most common phenotype, is taken as a standard, thd values obtained for Hp 2-2 are in good agreement with those obtained for Hp 2-1. However, the values obtained for Hp 1-1 are overestimated unless they are corrected by an experimental factor which has been determined in this study.
Haptoglobins↗
Emergence of a Novel, Phenotypically Difficult-to-Detect Vancomycin-Resistant Enterococcus faecium Clone (ST117/CT7799).
A significant increase of vancomycin-resistant Enterococcus faecium (VREfm) infections was observed in South-Eastern Austria since 2024. The prolonged outbreak is caused by a novel vanB-VREfm clone (ST117/CT7799, "VREfmstyr"). This study characterizes the atypical difficult-to-detect resistance phenotype and assesses the genomic relatedness of the isolates. Patient and outbreak characteristics were investigated including whole genome sequencing of the isolates. Sensitivity of broth microdilution (BMD), gradient tests (GT), disk diffusion (DD), and automated susceptibility testing (VITEK2) was compared. The performance of commercial screening media was evaluated. From sporadic detections in early 2024 case numbers began to rise during the year. In 30/31 (97%) of all cases, intra-hospital transmission was considered likely and an association with invasive procedures was identified in most cases. Core genome multilocus sequence typing revealed only six allelic differences between VREfmstyr isolates collected in a 12-month period, all belonging to the E. faecium ST117/CT7799 lineage. BMD detected vancomycin resistance (MIC > 4 mg/L) in no more than 16/31 (52%) of isolates after 24 h incubation, while GT and DD misclassified all isolates. Only prolonged incubation improved the performance of these assays. VITEK2 analysis, however, correctly classified all 31 isolates. Of four commercially available VRE-screening agars, only one was capable of detecting VREfmstyr after 24 h incubation. The emergence and clonal dissemination of VREfm ST117/CT7799 reveals a serious diagnostic gap as commonly used diagnostic algorithms fail to reliably detect this resistance phenotype. Our findings should help to further evaluate the true geographical distribution and clinical significance of this novel VREfm clone.
Enterococcus faecium↗
Accelerate Your Science: Direct-to-Biology Strategies in Medicinal Chemistry.
Direct-to-biology (D2B) is a powerful strategy that accelerates early drug discovery. It enables compounds to be synthesized in miniaturized formats and evaluated directly as crude reaction mixtures. This bypasses the need for purification during the initial design-make-test cycle. Advances in robust synthetic methodologies, automation, reaction miniaturization, and biological screening have transformed D2B from a proof-of-concept approach into a versatile medicinal chemistry platform. This platform is applicable to fragment optimization, covalent ligands, macrocycles, proteolysis-targeting chimeras (PROTACs), molecular glues, and cellular phenotypic screening. This perspective focuses on the synthetic transformations, assay technologies, and platform implementations that drive modern D2B workflows. It emphasizes reaction robustness, assay compatibility, and practical implementation. Analysis of the current literature revealed that D2B is more governed by reaction reliability than synthetic diversity. Amide coupling and click chemistry dominate reported workflows, while more complex transformations remain underexplored. We discuss the complementary strengths and limitations of biochemical, biophysical, and cellular readouts, identify current bottlenecks in reaction scope and data management, and highlight emerging opportunities arising from reaction miniaturization, machine learning, automated experimentation, and advanced synthetic methodologies. Rather than replacing conventional medicinal chemistry, D2B fundamentally shifts experimental effort from purification toward early biological validation and is poised to become an integral component of future medicinal chemistry workflows.
Humans↗