Project description:Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.
Project description:UnlabelledThere is a strong and growing need in the biology research community for accurate, automated image analysis. Here, we describe CellProfiler 2.0, which has been engineered to meet the needs of its growing user base. It is more robust and user friendly, with new algorithms and features to facilitate high-throughput work. ImageJ plugins can now be run within a CellProfiler pipeline.Availability and implementationCellProfiler 2.0 is free and open source, available at http://www.cellprofiler.org under the GPL v. 2 license. It is available as a packaged application for Macintosh OS X and Microsoft Windows and can be compiled for Linux.Contactanne@broadinstitute.orgSupplementary informationSupplementary data are available at Bioinformatics online.
Project description:BackgroundMaize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets.ResultsOn the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625.ConclusionThe Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.
Project description:High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software programs for image analysis workflows typically do not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, nucleus registration, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new analysis modules for existing analysis pipelines and to adding new analysis modules. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI software platform for a variety of cell biology applications.
Project description:Biomarkers are nucleic acids, proteins, single cells, or small molecules in human tissues or biological fluids whose reliable detection can be used to confirm or predict disease and disease states. Sensitive detection of biomarkers is therefore critical in a variety of applications including disease diagnostics, therapeutics, and drug screening. Unfortunately for many diseases, low abundance of biomarkers in human samples and low sample volumes render standard benchtop platforms like 96-well plates ineffective for reliable detection and screening. Discretization of bulk samples into a large number of small volumes (fL-nL) via droplet microfluidic technology offers a promising solution for high-sensitivity and high-throughput detection and screening of biomarkers. Several microfluidic strategies exist for high-throughput biomarker digitization into droplets, and these strategies have been utilized by numerous droplet platforms for nucleic acid, protein, and single-cell detection and screening. While the potential of droplet-based platforms has led to burgeoning interest in droplets, seamless integration of sample preparation technologies and automation of platforms from biological sample to answer remain critical components that can render these platforms useful in the clinical setting in the near future. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > Diagnostic Nanodevices Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease.
Project description:Aqueous two-phase system (ATPS) droplet generation has significant potential in biological and medical applications because of its excellent biocompatibility. However, the ultralow interfacial tension of ATPS makes droplet generation extremely challenging when compared with the conventional water-in-oil (W/O) system. In this paper, we passively produced ATPS droplets with a wide range of droplet size and high production rate without the involvement of an oil phase and external forces. For the first time, we reported important information of the flow rate and capillary (Ca) number for passive, oil-free ATPS droplet generation. It was found that the range of Ca numbers of the continuous phase under the jetting flow regime is 0.3-1.7, as compared to less than 0.1 in the W/O system, indicating the ultralow interfacial tension in ATPS. In addition, we successfully generated ATPS droplets with a radius as small as 7 μm at the maximum frequency up to 300 Hz, which has not been achieved in previous studies. The size and generation frequency of ATPS droplets can be controlled independently by adjusting the inlet pressures and corresponding flow rates. We found that the droplet size is correlated with the pressure and flow rate ratios with the power-law exponents of 0.8 and 0.2, respectively.
Project description:A novel omics-like method referred to as "particle morphomics" has been proposed in the present study. The dynamic images of >2,000,000 particles per sample in sediments, soils and dusts were collected by a Sympatec GmbH QICPIC particle size and shape analyzer, and the morphological descriptors of each particle including equivalent diameter, sphericity, aspect ratio and convexity were extracted as the "particle morphome". Various multivariate analyses were adopted to process the high-throughput data of particle morphome including analyses of alpha and beta diversities, similarity, correlation, network, redundancy, discretion and principal coordinate. The outcome of particle morphomics could estimate the morphological diversity and sketch the profile of morphological structure, which aided to develop a morphological fingerprint for specific particle samples. The distribution and properties of particle assemblages of specific morphology could also be evaluated by selecting particles with respect to filter criteria. More importantly, the particle morphomics may be extended to investigate and explain the biogeochemical and environmental processes involved with particle morphology if linked with external variables.
Project description:Assessment of γH2AX expression for studying DNA double-strand break formation is often performed by manual counting of foci using immunofluorescence microscopy, an approach that is laborious and subject to significant foci selection bias. Here we present a novel high-throughput method for detecting DNA double-strand breaks using automated image cytometry assessment of cell average γH2AX immunofluorescence. Our technique provides an expedient, high-throughput, objective, and cost-effective method for γH2AX analysis.
Project description:Complete blood count and differentiation of leukocytes (DIFF) belong to the most frequently performed laboratory diagnostic tests. Here, a flow cytometry-based method for label-free DIFF of untouched leukocytes by digital holographic microscopy on the rich phase contrast of peripheral leukocyte images, using highly controlled 2D hydrodynamic focusing conditions is reported. Principal component analysis of morphological characteristics of the reconstructed images allows classification of nine leukocyte types, in addition to different types of leukemia and demonstrates disappearance of acute myeloid leukemia cells in remission. To exclude confounding effects, the classification strategy is tested by the analysis of 20 blinded clinical samples. Here, 70% of the specimens are correctly classified with further 20% classifications close to a correct diagnosis. Taken together, the findings indicate a broad clinical applicability of the cytometry method for automated and reagent-free diagnosis of hematological disorders.
Project description:Altered miRNA expression and DNA methylation have highly active and diverse roles in carcinogenesis. Simultaneous detection of the molecular aberrations may have a synergistic effect on the diagnosis of malignancies. Herein, we develop a high-throughput assay for detecting multiple miRNAs and DNA methylation using droplet digital PCR (ddPCR) coupled with a 96-microwell plate. The microplate-based ddPCR could absolutely and reproducibly quantify 15 miRNAs and 14 DNA methylation sites with a high sensitivity (one copy/µL and 0.1%, respectively). Analyzing sputum and plasma of 40 lung cancer patients and 36 cancer-free smokers by this approach identified an integrated biomarker panel consisting of two sputum miRNAs (miRs-31-5p and 210-3p), one sputum DNA methylation (RASSF1A), and two plasma miRNAs (miR-21-5p and 126) for the diagnosis of lung cancer with higher sensitivity and specificity compared with a single type of biomarker. The diagnostic value of the integrated biomarker panel for the early detection of lung cancer was confirmed in a different cohort of 36 lung cancer patients and 39 cancer-free smokers. The high-throughput assay for quantification of multiple molecular aberrations across sputum and plasma could improve the early detection of lung cancer.