High-throughput Phenotyping Pipeline Research Articles

LncRNA U90926 is dispensable for the development of obesity-associated phenotypes invivo.

Obesity is a global health problem characterized by excessive fat accumulation, driven by adipogenesis and lipid accumulation. Long non-coding RNAs (lncRNAs) have recently been implicated in regulating adipogenesis and adipose tissue function. Mouse lncRNA U90926 was previously identified as a repressor of invitro adipogenesis in 3T3-L1 preadipocytes. Consequently, we hypothesized that, invivo, U90926 may repress adipogenesis, and hence its deletion would increase weight gain and adiposity. We tested the hypothesis by applying U90926-deficient (U9-KO) mice to a high-throughput phenotyping pipeline. Compared with WT, U9-KO mice showed no major differences across a wide range of behavioral, neurological, and other physiological parameters. In mice fed a standard diet, we have found no differences in obesity-related phenotypes, including weight gain, fat mass, and plasma concentrations of glucose, insulin, triglycerides, and free fatty acids, in U9-KO mice compared to WT. U90926 deficiency lacked a major effect on white adipose tissue morphology and gene expression profile. Furthermore, in mice fed a high-fat diet, we found increased expression of U90926 in adipose tissue stromal vascular cell fraction, yet observed no effect of U90926 deficiency on weight gain, fat mass, adipogenesis marker expression, and immune cell infiltration into the adipose tissue. These data suggest that the U90926 lacks an essential role in obesity-related phenotypes and adipose tissue biology invivo.

A comprehensive characterization of agronomic and end-use quality phenotypes across a quinoa world core collection.

Quinoa (Chenopodium quinoa Willd.), a pseudocereal with high protein quality originating from the Andean region of South America, has broad genetic variation and adaptability to diverse agroecological conditions, contributing to the potential to serve as a global keystone protein crop in a changing climate. However, the germplasm resources currently available to facilitate quinoa expansion worldwide are restricted to a small portion of quinoa's total genetic diversity, in part because of day-length sensitivity and issues related to seed sovereignty. This study aimed to characterize phenotypic relationships and variation within a quinoa world core collection. The 360 accessions were planted in a randomized complete block design with four replicates in each of two greenhouses in Pullman, WA during the summer of 2018. Phenological stages, plant height, and inflorescence characteristics were recorded. Seed yield, composition, thousand seed weight, nutritional composition, shape, size, and color were measured using a high-throughput phenotyping pipeline. Considerable variation existed among the germplasm. Crude protein content ranged from 11.24% to 17.81% (fixed at 14% moisture). We found that protein content was negatively correlated with yield and positively correlated with total amino acid content and days to harvest. Mean essential amino acids values met adult daily requirements but not leucine and lysine infant requirements. Yield was positively correlated with thousand seed weight and seed area, and negatively correlated with ash content and days to harvest. The accessions clustered into four groups, with one-group representing useful accessions for long-day breeding programs. The results of this study establish a practical resource for plant breeders to leverage as they strategically develop germplasm in support of the global expansion of quinoa.

PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time.

High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10-355 plants. We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.

Relieving the Phenotyping Bottleneck for Grape Bunch Architecture in Grapevine Breeding Research: Implementation of a 3D-Based Phenotyping Approach for Quantitative Trait Locus Mapping

In viticulture, winemakers and the industry demand grape bunches that have a reduced degree of bunch compactness. The major aspect is that a loose bunch compactness reduces the risk of severe Botrytis bunch-rot infections. Grapevine breeders focus hereby on several bunch-architecture-related traits. For specific breeding approaches and breeding-research-related topics, such as Quantitative Trait Locus (QTL) analysis or molecular marker development, the exact and objective phenotyping of such traits is mandatory. In this study, a precise and high-throughput 3D phenotyping pipeline was applied to screen 1514 genotypes from three mapping populations with different genetic backgrounds to investigate its applicability for QTL mapping approaches. In the first step, the phenotypic data of one population containing 150 genotypes were collected and analyzed with the 3D phenotyping pipeline. Additionally, corresponding reference data were obtained. Phenotypic values and results of a QTL analysis were compared with each other. Strongly positive correlations up to r = 0.93 between 3D and reference measurements could be detected for several traits. The ten-times-faster 3D phenotyping pipeline revealed 20, and the reference phenotyping methods revealed 22 QTLs. Eighteen of these QTLs were consistent between both procedures. In the next step, screening was extended to four different mapping populations across several seasons. In total, up to 1500 genotypes were screened during one season (&gt;5000 grape bunches in total). The data analysis revealed significant differences across years and populations. Three bunch-architecture traits, including total berry volume, bunch width, and berry diameter, explained the highest amount of variability in the phenotypic data. A QTL analysis was performed on the phenotypic data of the involved populations to identify comparative genetic loci for bunch-architecture traits. Between 20 and 26 stable and reproducible QTLs for the investigated populations were detected. A common QTL for berry diameter could be identified for all populations. Our results strongly conclude that this locus is co-located on chromosome 17 when mapped to the grapevine reference genome. The results show that the implementation of the 3D phenotyping platform allows for precise and extended screenings of different, genetic diverse mapping populations and thus opens up the possibility to uncover the genomic architecture of this highly complex quantitative grapevine trait.

Dimensionality Reduction of High-throughput Phenotyping Data in Cotton Fields

In this paper, we implement data reduction methods to reduce the size of image datasets from cotton fields for use in a high-throughput phenotyping (HTP) pipeline in order to allow for data transfer more quickly over poor internet connections. We investigate dimensionality reduction methods to accomplish this goal. Specifically, we utilize Principal Component Analysis (PCA) to compress image data into a smaller dimension space, which when uncompressed retains significant variability from the original image. To demonstrate the ability of PCA to produce quality reconstructions, we consider the example use case of detecting cotton bloom flowering patterns with reconstructed images. We employ Open Source Computer Vision (OpenCV) to generate pixel-wise masks which both further reduces the byte size of data and successfully identifies cotton bloom flowering. The results indicate a high amount of data reduction from the original to the reconstructed images; byte sizes reduce 93% through PCA while preserving around 98% variance when using a much smaller number of components. Bitwise masking with OpenCV yields a 99% reduction in file size. The results demonstrate great potential in employing machine learning techniques for the data reduction pre-processing step prior to performing subsequent analysis. This data reduction is a crucial step in developing a field-based HTP big data pipeline.

Software design for image mapping and analytics for high throughput phenotyping

Phenotyping is essential for the advancement of plant breeding and quantitative genetics. Image-based high throughput phenotyping (HTP) is a game changer to expedite a breeding pipeline by supporting large coverage and image analysis for morphological and spectral signature of the plant canopy. To meet the demand of a cost-effective and globally consistent HTP solution, a customized analytic tool, Image Mapping & Analytics for Phenotyping (IMAP), was developed to implement high throughput image processing and deliver plot-level metrics of plant phenotypes. IMAP is open source software written in Python and designed to provide data visualization and batch processes through algorithms for GIS interface, geometric and radiometric calibrations, geo-fencing, segmentation, and gridding. IMAP was applied and validated on aerial images collected by an unmanned aerial vehicle (UAV) and a manned aerial vehicle (MAV) on soybean field that was prepared for a drought resistance study under two different water treatments. The gridding algorithm extracted field boundary and delivered plot-level spectral analysis from aerial images in 1–4 s depending on the image size and the number of plots. The high correlation (R2 = 0.90 in vegetation and 0.89 in leaf area) between UAV and MAV images indicated the consistent performance of IMAP software in calculating the plot-level metrics and proved successful deployment of a high throughput phenotyping pipeline through a series of image processing and gridding algorithm in a batch process.

A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis.

High-throughput phenotyping system has become more and more popular in plant science research. The data analysis for such a system typically involves two steps: plant feature extraction through image processing and statistical analysis for the extracted features. The current approach is to perform those two steps on different platforms. We develop the package “implant” in R for both robust feature extraction and functional data analysis. For image processing, the “implant” package provides methods including thresholding, hidden Markov random field model, and morphological operations. For statistical analysis, this package can produce nonparametric curve fitting with its confidence region for plant growth. A functional ANOVA model to test for the treatment and genotype effects on the plant growth dynamics is also provided.

High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP).

Phenotypes are the foundation for clinical and genetic studies of disease risk and outcomes. The growth of biobanks linked to electronic medical record (EMR) data has both facilitated and increased the demand for efficient, accurate, and robust approaches for phenotyping millions of patients. Challenges to phenotyping with EMR data include variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels. To address these challenges, we developed PheCAP, a high-throughput semi-supervised phenotyping pipeline. PheCAP begins with data from the EMR, including structured data and information extracted from the narrative notes using natural language processing (NLP). The standardized steps integrate automated procedures, which reduce the level of manual input, and machine learning approaches for algorithm training. PheCAP itself can be executed in 1-2 d if all data are available; however, the timing is largely dependent on the chart review stage, which typically requires at least 2 weeks. The final products of PheCAP include a phenotype algorithm, the probability of the phenotype for all patients, and a phenotype classification (yes or no).

High-throughput phenotyping of cell-to-cell interactions in gel microdroplet pico-cultures.

Microbiomes exert significant influence on our planet's ecology. Elucidating the identities of individual microbes within these communities and how they interact is a vital research imperative. Using traditional plating and culturing methods, it is impractical to assess even a small fraction of the interactions that exist within microbial communities. To address this technology gap, we integrated gel microdroplet technology with microfluidics to generate millions of microdroplet cultures (MDs) that sequester individual cells for phenotyping MDs, facilitating rapid analysis and viable recovery using flow cytometry. Herein, we describe a validated high-throughput phenotyping pipeline that elucidates cell-to-cell interactions for millions of combinations of microorganisms. Through iterative co-culturing of an algae and a pool of environmentally sourced microbes, we successfully isolated bacteria that improved algal growth.

Evoked Potential Recordings of Auditory Brainstem Activity in the Mouse: An Optimized Method for the Assessment of Hearing Function of Mice.

Hearing loss is a common sensory deficiency suffered by millions worldwide. It is a heterogeneous condition and genetics plays a critical role in its etiology. Gene variants can fundamentally alter hearing function, or predispose the auditory system towards loss of function resulting from other factors. In mouse studies of hearing loss and gene function, an evoked potential electrophysiological recording, the auditory brainstem response (ABR), is now considered the optimal way to screen large numbers of individuals, either with normal hearing sensitivity or with hearing impairment. Other routinely used methods to assess hearing function (such as acoustic startle responses, or otoacoustic emissions) do not allow assessment of the same broad spectrum of dysfunction nor readily allow the threshold sensitivity of the neural output of the cochlea to be assessed and are less ideal. An optimized recording system to rapidly and reproducibly record high-quality ABRs from mutant mice as part of a high-throughput phenotyping pipeline was developed. Click-evoked ABRs and ABRs evoked by pure-tone frequencies over a range of sound levels from 0 dB to 95 dB, sound pressure levels (SPL) are recorded. This takes approximately 15-20 min per mouse (with 5 tone frequencies), allowing a large number of mutant mice to be screened. This method has been used to measure ABRs on a high-throughput mutant mouse phenotyping pipeline and in laboratory tests to follow-up the hearing loss phenotypes identified on that pipeline.

The role of sex and body weight on the metabolic effects of high-fat diet in C57BL/6N mice

Background:Metabolic disorders are commonly investigated using knockout and transgenic mouse models on the C57BL/6N genetic background due to its genetic susceptibility to the deleterious metabolic effects of high-fat diet (HFD). There is growing awareness of the need to consider sex in disease progression, but limited attention has been paid to sexual dimorphism in mouse models and its impact in metabolic phenotypes. We assessed the effect of HFD and the impact of sex on metabolic variables in this strain.Methods:We generated a reference data set encompassing glucose tolerance, body composition and plasma chemistry data from 586 C57BL/6N mice fed a standard chow and 733 fed a HFD collected as part of a high-throughput phenotyping pipeline. Linear mixed model regression analysis was used in a dual analysis to assess the effect of HFD as an absolute change in phenotype, but also as a relative change accounting for the potential confounding effect of body weight.Results:HFD had a significant impact on all variables tested with an average absolute effect size of 29%. For the majority of variables (78%), the treatment effect was modified by sex and this was dominated by male-specific or a male stronger effect. On average, there was a 13.2% difference in the effect size between the male and female mice for sexually dimorphic variables. HFD led to a significant body weight phenotype (24% increase), which acts as a confounding effect on the other analysed variables. For 79% of the variables, body weight was found to be a significant source of variation, but even after accounting for this confounding effect, similar HFD-induced phenotypic changes were found to when not accounting for weight.Conclusion:HFD and sex are powerful modifiers of metabolic parameters in C57BL/6N mice. We also demonstrate the value of considering body size as a covariate to obtain a richer understanding of metabolic phenotypes.

Quantifying time-series of leaf morphology using 2D and 3D photogrammetry methods for high-throughput plant phenotyping

Conventional phenotyping methods impose a significant bottleneck to the characterization of genotypic and environmental effects on trait expression in plants. In particular, invasive and destructive sampling methods along with manual measurements widely used in conventional studies are labor-intensive, time-consuming, costly, and can lack consistency. These experimental features impede large-scale genetic studies of both crops and wild plant species. Here, we present a high-throughput phenotyping pipeline using photogrammetry and 3D modeling techniques in the model species, Arabidopsis thaliana. We develop novel photogrammetry and computer vision algorithms to quantify 2D and 3D leaf areas for a mapping population of 1050 Arabidopsis thaliana lines, and use 2D areas to analyze plant nastic movements and diurnal cycles. Compared to the 2D leaf areas, 3D leaf areas show an uncorrupted growth trend regardless of plant nastic movement. With optimized algorithms, our pipeline throughput is very computationally efficient for screening a large number of plants. The pipeline not only supports measurement of organ-level growth and development over time, but also enables analysis of whole-plant phenotypes and, thus, identification of genotype-specific performance. Further, the accuracy results evaluating the relationship between physical dimensions and 3D measurements indicate an R2=0.99, and the average 3D area processing time per plant is 0.02s. Our algorithms provide both high accuracy and throughput in plant phenotyping, thereby, enabling progress in plant genotypic modeling.

Identifying seedling root architectural traits associated with yield and yield components in wheat.

Plant roots growing underground are critical for soil resource acquisition, anchorage and plant-environment interactions. In wheat ( Triticum aestivum ), however, the target root traits to improve yield potential still remain largely unknown. This study aimed to identify traits of seedling root system architecture (RSA) associated with yield and yield components in 226 recombinant inbred lines (RILs) derived from a cross between the bread wheat Triticum aestivum 'Forno' (small, wide root system) and spelt Triticum spelta 'Oberkulmer' (large, narrow root system). A 'pouch and wick' high-throughput phenotyping pipeline was used to determine the RSA traits of 13-day-old RIL seedlings. Two field experiments and one glasshouse experiment were carried out to investigate the yield, yield components and phenology, followed by identification of quantitative trait loci (QTLs). There was substantial variation in RSA traits between genotypes. Seminal root number and total root length were both positively associated with grains m -2 , grains per spike, above-ground biomass m -2 and grain yield. More seminal roots and longer total root length were also associated with delayed maturity and extended grain filling, likely to be a consequence of more grains being defined before anthesis. Additionally, the maximum width of the root system displayed positive relationships with spikes m -2 , grains m -2 and grain yield. Ten RILs selected for the longest total roots exhibited the same effects on yield and phenology as described above, compared with the ten lines with the shortest total roots. Genetic analysis revealed 38 QTLs for the RSA, and QTL coincidence between the root and yield traits was frequently observed, indicating tightly linked genes or pleiotropy, which concurs with the results of phenotypic correlation analysis. Based on the results from the Forno × Oberkulmer population, it is proposed that vigorous early root growth, particularly more seminal roots and longer total root length, is important to improve yield potential, and should be incorporated into wheat ideotypes in breeding.

A high-throughput functional genomics workflow based on CRISPR/Cas9-mediated targeted mutagenesis in zebrafish.

The zebrafish is a popular model organism for studying development and disease, and genetically modified zebrafish provide an essential tool for functional genomic studies. Numerous publications have demonstrated the efficacy of gene targeting in zebrafish using CRISPR/Cas9, and they have included descriptions of a variety of tools and methods for guide RNA synthesis and mutant identification. However, most of the published techniques are not readily scalable to increase throughput. We recently described a CRISPR/Cas9-based high-throughput mutagenesis and phenotyping pipeline in zebrafish. Here, we present a complete workflow for this pipeline, including target selection; cloning-free single-guide RNA (sgRNA) synthesis; microinjection; validation of the target-specific activity of the sgRNAs; founder screening to identify germline-transmitting mutations by fluorescence PCR; determination of the exact lesion by Sanger or next-generation sequencing (including software for analysis); and genotyping in the F1 or subsequent generations. Using these methods, sgRNAs can be evaluated in 3 d, zebrafish germline-transmitting mutations can be identified within 3 months and stable lines can be established within 6 months. Realistically, two researchers can target tens to hundreds of genes per year using this protocol.

Plant high-throughput phenotyping using photogrammetry and imaging techniques to measure leaf length and rosette area

Plant phenotyping is central to understand causal effects of genotypes and environments on trait expression and is a critical factor in expediting plant breeding. Previously, plant phenotypic traits were quantified using invasive, time-consuming, labor-intensive, cost-inefficient, and often destructive manual sampling methods that were also prone to observer error. In recent years, photogrammetry and image processing techniques have been introduced to plant phenotyping, but cost efficiency issues remain when combining these two techniques within large-scale plant phenotyping studies. Using these high-throughput techniques in basic plant biology research and agriculture are still in the developmental stages but show great promise for rapid phenotyping, which will materially aid both science and crop improvement efforts.In this study, we introduce an automated high-throughput phenotyping pipeline using affordable imaging systems and image processing algorithms to build 2D mosaicked orthophotos. Chamber-based and ground-level field implementations are used to measure phenotypic traits such as leaf length and rosette area in 2D images. Our automated pipeline has cross-platform capabilities and a degree of instrument independence, making it suitable for various situations.

Automated interpretation of 3D laserscanned point clouds for plant organ segmentation.

BackgroundPlant organ segmentation from 3D point clouds is a relevant task for plant phenotyping and plant growth observation. Automated solutions are required to increase the efficiency of recent high-throughput plant phenotyping pipelines. However, plant geometrical properties vary with time, among observation scales and different plant types. The main objective of the present research is to develop a fully automated, fast and reliable data driven approach for plant organ segmentation.ResultsThe automated segmentation of plant organs using unsupervised, clustering methods is crucial in cases where the goal is to get fast insights into the data or no labeled data is available or costly to achieve. For this we propose and compare data driven approaches that are easy-to-realize and make the use of standard algorithms possible. Since normalized histograms, acquired from 3D point clouds, can be seen as samples from a probability simplex, we propose to map the data from the simplex space into Euclidean space using Aitchisons log ratio transformation, or into the positive quadrant of the unit sphere using square root transformation. This, in turn, paves the way to a wide range of commonly used analysis techniques that are based on measuring the similarities between data points using Euclidean distance. We investigate the performance of the resulting approaches in the practical context of grouping 3D point clouds and demonstrate empirically that they lead to clustering results with high accuracy for monocotyledonous and dicotyledonous plant species with diverse shoot architecture.ConclusionAn automated segmentation of 3D point clouds is demonstrated in the present work. Within seconds first insights into plant data can be deviated – even from non-labelled data. This approach is applicable to different plant species with high accuracy. The analysis cascade can be implemented in future high-throughput phenotyping scenarios and will support the evaluation of the performance of different plant genotypes exposed to stress or in different environmental scenarios.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0665-2) contains supplementary material, which is available to authorized users.

PhenStat: A Tool Kit for Standardized Analysis of High Throughput Phenotypic Data.

The lack of reproducibility with animal phenotyping experiments is a growing concern among the biomedical community. One contributing factor is the inadequate description of statistical analysis methods that prevents researchers from replicating results even when the original data are provided. Here we present PhenStat – a freely available R package that provides a variety of statistical methods for the identification of phenotypic associations. The methods have been developed for high throughput phenotyping pipelines implemented across various experimental designs with an emphasis on managing temporal variation. PhenStat is targeted to two user groups: small-scale users who wish to interact and test data from large resources and large-scale users who require an automated statistical analysis pipeline. The software provides guidance to the user for selecting appropriate analysis methods based on the dataset and is designed to allow for additions and modifications as needed. The package was tested on mouse and rat data and is used by the International Mouse Phenotyping Consortium (IMPC). By providing raw data and the version of PhenStat used, resources like the IMPC give users the ability to replicate and explore results within their own computing environment.

Genome-wide association mapping in plants exemplified for root growth in Arabidopsis thaliana.

Genome-wide association (GWA) mapping is a powerful technique to address the molecular basis of genotype to phenotype relationships and to map regulators of biological processes. This chapter presents a protocol for genome-wide association mapping in Arabidopsis thaliana using the user-friendly internet application GWAPP, and provides a specific protocol for acquiring root trait data suitable for GWA studies using the semi-automated, high-throughput phenotyping pipeline BRAT for early root growth.

Impact of temporal variation on design and analysis of mouse knockout phenotyping studies.

A significant challenge facing high-throughput phenotyping of in-vivo knockout mice is ensuring phenotype calls are robust and reliable. Central to this problem is selecting an appropriate statistical analysis that models both the experimental design (the workflow and the way control mice are selected for comparison with knockout animals) and the sources of variation. Recently we proposed a mixed model suitable for small batch-oriented studies, where controls are not phenotyped concurrently with mutants. Here we evaluate this method both for its sensitivity to detect phenotypic effects and to control false positives, across a range of workflows used at mouse phenotyping centers. We found the sensitivity and control of false positives depend on the workflow. We show that the phenotypes in control mice fluctuate unexpectedly between batches and this can cause the false positive rate of phenotype calls to be inflated when only a small number of batches are tested, when the effect of knockout becomes confounded with temporal fluctuations in control mice. This effect was observed in both behavioural and physiological assays. Based on this analysis, we recommend two approaches (workflow and accompanying control strategy) and associated analyses, which would be robust, for use in high-throughput phenotyping pipelines. Our results show the importance in modelling all sources of variability in high-throughput phenotyping studies.

Exploring the elephant: histopathology in high-throughput phenotyping of mutant mice.

Recent advances in gene knockout techniques and the in vivo analysis of mutant mice, together with the advent of large-scale projects for systematic mouse mutagenesis and genome-wide phenotyping, have allowed the creation of platforms for the most complete and systematic analysis of gene function ever undertaken in a vertebrate. The development of high-throughput phenotyping pipelines for these and other large-scale projects allows investigators to search and integrate large amounts of directly comparable phenotype data from many mutants, on a genomic scale, to help develop and test new hypotheses about the origins of disease and the normal functions of genes in the organism. Histopathology has a venerable history in the understanding of the pathobiology of human and animal disease, and presents complementary advantages and challenges to in vivo phenotyping. In this review, we present evidence for the unique contribution that histopathology can make to a large-scale phenotyping effort, using examples from past and current programmes at Lexicon Pharmaceuticals and The Jackson Laboratory, and critically assess the role of histopathology analysis in high-throughput phenotyping pipelines.