Dataset Information

High-throughput phenotyping of chlamydomonas swimming mutants based on nanoscale video analysis.

ABSTRACT: Studies on biflagellated algae Chlamydomonas reinhardtii mutants have resulted in significant contributions to our understanding of the functions of cilia/flagella components. However, visual inspection conducted under a microscope to screen and classify Chlamydomonas swimming requires considerable time, effort, and experience. In addition, it is likely that identification of mutants by this screening is biased toward individual cells with severe swimming defects, and mutants that swim slightly more slowly than wild-type cells may be missed by these screening methods. To systematically screen Chlamydomonas swimming mutants, we have here developed the cell-locating-with-nanoscale-accuracy (CLONA) method to identify the cell position to within 10-nm precision through the analysis of high-speed video images. Instead of analyzing the shape of the flagella, which is not always visible in images, we determine the position of Chlamydomonas cell bodies by determining the cross-correlation between a reference image and the image of the cell. From these positions, various parameters related to swimming, such as velocity and beat frequency, can be accurately estimated for each beat cycle. In the examination of wild-type and seven dynein arm mutants of Chlamydomonas, we found characteristic clustering on scatter plots of beat frequency versus swimming velocity. Using the CLONA method, we have screened 38 Chlamydomonas strains and detected believed-novel motility-deficient mutants that would be missed by visual screening. This CLONA method can automate the screening for mutants of Chlamydomonas and contribute to the elucidation of the functions of motility-associated proteins.

SUBMITTER: Fujita S

PROVIDER: S-EPMC4104059 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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High-throughput phenotyping of chlamydomonas swimming mutants based on nanoscale video analysis.

Fujita Shohei S Matsuo Takuya T Ishiura Masahiro M Kikkawa Masahide M

Biophysical journal 20140701 2

Studies on biflagellated algae Chlamydomonas reinhardtii mutants have resulted in significant contributions to our understanding of the functions of cilia/flagella components. However, visual inspection conducted under a microscope to screen and classify Chlamydomonas swimming requires considerable time, effort, and experience. In addition, it is likely that identification of mutants by this screening is biased toward individual cells with severe swimming defects, and mutants that swim slightly ...[more]

PMID: 25028875

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Project description:ObjectiveHigh-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs.Materials and methodsWe develop a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (aggregated vector representation), the standard sequential patterns (sequential pattern mining), the transitive sequential patterns (transitive sequential pattern mining), and 2 hybrid classes. Using EHR data on 10 phenotypes from the Mass General Brigham Biobank, we train and validate phenotyping algorithms.ResultsPhenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the standard representations in electronic phenotyping. The high-throughput algorithm's classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations.DiscussionThe proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. Transitive sequences offer more accurate characterization of the phenotype, compared with its individual components, and reflect the actual lived experiences of the patients with that particular disease.ConclusionSequential data representations provide a precise mechanism for incorporating raw EHR records into downstream machine learning. Our approach starts with user interpretability and works backward to the technology.

Project description:With the goal to generate and characterize the phenotypes of null alleles in all genes within an organism and the recent advances in custom nucleases, genome editing limitations have moved from mutation generation to mutation detection. We previously demonstrated that High Resolution Melting (HRM) analysis is a rapid and efficient means of genotyping known zebrafish mutants. Here we establish optimized conditions for HRM based detection of novel mutant alleles. Using these conditions, we demonstrate that HRM is highly efficient at mutation detection across multiple genome editing platforms (ZFNs, TALENs, and CRISPRs); we observed nuclease generated HRM positive targeting in 1 of 6 (16%) open pool derived ZFNs, 14 of 23 (60%) TALENs, and 58 of 77 (75%) CRISPR nucleases. Successful targeting, based on HRM of G0 embryos correlates well with successful germline transmission (46 of 47 nucleases); yet, surprisingly mutations in the somatic tail DNA weakly correlate with mutations in the germline F1 progeny DNA. This suggests that analysis of G0 tail DNA is a good indicator of the efficiency of the nuclease, but not necessarily a good indicator of germline alleles that will be present in the F1s. However, we demonstrate that small amplicon HRM curve profiles of F1 progeny DNA can be used to differentiate between specific mutant alleles, facilitating rare allele identification and isolation; and that HRM is a powerful technique for screening possible off-target mutations that may be generated by the nucleases. Our data suggest that micro-homology based alternative NHEJ repair is primarily utilized in the generation of CRISPR mutant alleles and allows us to predict likelihood of generating a null allele. Lastly, we demonstrate that HRM can be used to quickly distinguish genotype-phenotype correlations within F1 embryos derived from G0 intercrosses. Together these data indicate that custom nucleases, in conjunction with the ease and speed of HRM, will facilitate future high-throughput mutation generation and analysis needed to establish mutants in all genes of an organism.

Project description:BACKGROUND: A quantitative characterization of root system architecture is currently being attempted for various reasons. Non-destructive, rapid analyses of root system architecture are difficult to perform due to the hidden nature of the root. Hence, improved methods to measure root architecture are necessary to support knowledge-based plant breeding and to analyse root growth responses to environmental changes. Here, we report on the development of a novel method to reveal growth and architecture of maize root systems. RESULTS: The method is based on the cultivation of different root types within several layers of two-dimensional, large (50 × 60 cm) plates (rhizoslides). A central plexiglass screen stabilizes the system and is covered on both sides with germination paper providing water and nutrients for the developing root, followed by a transparent cover foil to prevent the roots from falling dry and to stabilize the system. The embryonic roots grow hidden between a Plexiglas surface and paper, whereas crown roots grow visible between paper and the transparent cover. Long cultivation with good image quality up to 20 days (four fully developed leaves) was enhanced by suppressing fungi with a fungicide. Based on hyperspectral microscopy imaging, the quality of different germination papers was tested and three provided sufficient contrast to distinguish between roots and background (segmentation). Illumination, image acquisition and segmentation were optimised to facilitate efficient root image analysis. Several software packages were evaluated with regard to their precision and the time investment needed to measure root system architecture. The software 'Smart Root' allowed precise evaluation of root development but needed substantial user interference. 'GiaRoots' provided the best segmentation method for batch processing in combination with a good analysis of global root characteristics but overestimated root length due to thinning artefacts. 'WhinRhizo' offered the most rapid and precise evaluation of root lengths in diameter classes, but had weaknesses with respect to image segmentation and analysis of root system architecture. CONCLUSION: A new technique has been established for non-destructive root growth studies and quantification of architectural traits beyond seedlings stages. However, automation of the scanning process and appropriate software remains the bottleneck for high throughput analysis.

Dataset Information

High-throughput phenotyping of chlamydomonas swimming mutants based on nanoscale video analysis.

Publications

High-throughput phenotyping of chlamydomonas swimming mutants based on nanoscale video analysis.

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