scholarly journals Determination of Clonality and Relatedness of Vibrio cholerae Isolates by Genomic Fingerprinting, Using Long-Range Repetitive Element Sequence-Based PCR

2008 ◽  
Vol 74 (17) ◽  
pp. 5392-5401 ◽  
Author(s):  
Nipa Chokesajjawatee ◽  
Young-Gun Zo ◽  
Rita R. Colwell
Keyword(s):  
Vibrio Cholerae ◽  
Long Range ◽  
High Throughput ◽  
Large Scale ◽  
Rapid Screening ◽  
Genomic Fingerprinting ◽  
High Throughput Analysis ◽  
Consensus Sequences ◽  
Specialized Equipment ◽  
Long Range Pcr

ABSTRACT A high-throughput method which is applicable for rapid screening, identification, and delineation of isolates of Vibrio cholerae, sensitive to genome variation, and capable of providing phylogenetic inferences enhances environmental monitoring of this bacterium. We have developed and optimized a method for genomic fingerprinting of V. cholerae based on long-range PCR. The method uses a primer set directed to enterobacterial repetitive intergenic consensus sequences, a high-fidelity DNA polymerase, and analysis via conventional agarose gel electrophoresis. Long (∼10 kb), highly reproducible amplicons were generated from V. cholerae isolates, including those from different geographical locations and historical strains isolated during the period 1931-2000. The amplicons yielded reduced variability in their densitometric band patterns to ≤10% and clonal distinction at <90% similarity. Rapid band-matching analysis was accomplished for fingerprints with ≥90% similarity, discriminating O serotypes and biotypes (classical versus El Tor) as well as pathogenic and nonpathogenic strains. Compared to genome similarity measured by DNA-DNA hybridization, the results showed good correlation (r = 0.7; P < 0.001), with five times less measurement error and without bias. The method permits both phylogenetic inference and clonal differentiation of individual V. cholerae strains, enables robust, high-throughput analysis, and does not require specialized equipment to perform. With access to a curated public database furnished with appropriate analytical software applications, the method should prove useful in large-scale multilaboratory surveys, especially those designed to detect specific pathogens in the natural environment.

scholarly journals ZF-AutoML: An Easy Machine-Learning-Based Method to Detect Anomalies in Fluorescent-Labelled Zebrafish

Inventions ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 72
Author(s):  
Ryota Sawaki ◽  
Daisuke Sato ◽  
Hiroko Nakayama ◽  
Yuki Nakagawa ◽  
Yasuhito Shimada
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Fluorescent Protein ◽  
Learning Program ◽  
High Throughput Analysis ◽  
Easy Method ◽  
High Fecundity

Background: Zebrafish are efficient animal models for conducting whole organism drug testing and toxicological evaluation of chemicals. They are frequently used for high-throughput screening owing to their high fecundity. Peripheral experimental equipment and analytical software are required for zebrafish screening, which need to be further developed. Machine learning has emerged as a powerful tool for large-scale image analysis and has been applied in zebrafish research as well. However, its use by individual researchers is restricted due to the cost and the procedure of machine learning for specific research purposes. Methods: We developed a simple and easy method for zebrafish image analysis, particularly fluorescent labelled ones, using the free machine learning program Google AutoML. We performed machine learning using vascular- and macrophage-Enhanced Green Fluorescent Protein (EGFP) fishes under normal and abnormal conditions (treated with anti-angiogenesis drugs or by wounding the caudal fin). Then, we tested the system using a new set of zebrafish images. Results: While machine learning can detect abnormalities in the fish in both strains with more than 95% accuracy, the learning procedure needs image pre-processing for the images of the macrophage-EGFP fishes. In addition, we developed a batch uploading software, ZF-ImageR, for Windows (.exe) and MacOS (.app) to enable high-throughput analysis using AutoML. Conclusions: We established a protocol to utilize conventional machine learning platforms for analyzing zebrafish phenotypes, which enables fluorescence-based, phenotype-driven zebrafish screening.

HLA-DRB1, -DRB3, -DRB4 and -DRB5 genotyping at a super-high resolution level by long range PCR and high-throughput sequencing

2013 ◽  
Vol 83 (1) ◽  
pp. 10-16 ◽  
Author(s):  
Y. Ozaki ◽  
S. Suzuki ◽  
A. Shigenari ◽  
Y. Okudaira ◽  
E. Kikkawa ◽  
...  
Keyword(s):  
High Resolution ◽  
Long Range ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Resolution Level ◽  
Long Range Pcr

scholarly journals Advances in Development of New Treatment for Leishmaniasis

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Juliana Perrone Bezerra de Menezes ◽  
Carlos Eduardo Sampaio Guedes ◽  
Antônio Luis de Oliveira Almeida Petersen ◽  
Deborah Bittencourt Mothé Fraga ◽  
Patrícia Sampaio Tavares Veras
Keyword(s):  
High Throughput ◽  
Drug Targets ◽  
Scientific Community ◽  
Large Scale ◽  
High Throughput Analysis ◽  
Large Scale Analysis ◽  
Parasite Biology ◽  
New Treatment ◽  
Novel Drug ◽  
Chemotherapeutic Interventions

Leishmaniasis is a neglected infectious disease caused by several different species of protozoan parasites of the genusLeishmania. Current strategies to control this disease are mainly based on chemotherapy. Despite being available for the last 70 years, leishmanial chemotherapy has lack of efficiency, since its route of administration is difficult and it can cause serious side effects, which results in the emergence of resistant cases. The medical-scientific community is facing difficulties to overcome these problems with new suitable and efficient drugs, as well as the identification of new drug targets. The availability of the complete genome sequence ofLeishmaniahas given the scientific community the possibility of large-scale analysis, which may lead to better understanding of parasite biology and consequent identification of novel drug targets. In this review we focus on how high-throughput analysis is helping us and other groups to identify novel targets for chemotherapeutic interventions. We further discuss recent data produced by our group regarding the use of the high-throughput techniques and how this helped us to identify and assess the potential of new identified targets.

scholarly journals VAPPER: High-throughput variant antigen profiling in African trypanosomes of livestock

GigaScience ◽  
2019 ◽  
Vol 8 (9) ◽  
Author(s):  
Sara Silva Pereira ◽  
John Heap ◽  
Andrew R Jones ◽  
Andrew P Jackson
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Variant Calling ◽  
Gene Families ◽  
Automated Analysis ◽  
Surface Glycoprotein ◽  
Sequencing Data ◽  
High Throughput Analysis ◽  
African Trypanosomes ◽  
Variant Antigen

Abstract Background Analysing variant antigen gene families on a population scale is a difficult challenge for conventional methods of read mapping and variant calling due to the great variability in sequence, copy number, and genomic loci. In African trypanosomes, hemoparasites of humans and animals, this is complicated by variant antigen repertoires containing hundreds of genes subject to various degrees of sequence recombination. Findings We introduce Variant Antigen Profiler (VAPPER), a tool that allows automated analysis of the variant surface glycoprotein repertoires of the most prevalent livestock African trypanosomes. VAPPER produces variant antigen profiles for any isolate of the veterinary pathogens Trypanosoma congolense and Trypanosoma vivax from genomic and transcriptomic sequencing data and delivers publication-ready figures that show how the queried isolate compares with a database of existing strains. VAPPER is implemented in Python. It can be installed to a local Galaxy instance from the ToolShed (https://toolshed.g2.bx.psu.edu/) or locally on a Linux platform via the command line (https://github.com/PGB-LIV/VAPPER). The documentation, requirements, examples, and test data are provided in the Github repository. Conclusion By establishing two different, yet comparable methodologies, our approach is the first to allow large-scale analysis of African trypanosome variant antigens, large multi-copy gene families that are otherwise refractory to high-throughput analysis.

scholarly journals High-throughput Analysis of Arabidopsis Stem Vibrations to Identify Mutants with Altered Mechanical Properties

2018 ◽  
Author(s):  
Miyuki T. Nakata ◽  
Masahiro Takahara ◽  
Shingo Sakamoto ◽  
Kouki Yoshida ◽  
Nobutaka Mitsuda
Keyword(s):  
Mechanical Properties ◽  
High Throughput ◽  
High Speed ◽  
Large Scale ◽  
Growth Traits ◽  
Damping Ratio ◽  
Free Vibrations ◽  
Video Data ◽  
High Throughput Analysis ◽  
Throughput Analysis

AbstractMechanical properties are rarely used as quantitative indices for the large-scale mutant screening of plants, even in the model plant Arabidopsis thaliana. The mechanical properties of plant stems generally influence their vibrational characteristics. Here, we developed Python-based software, named AraVib, for the high-throughput analysis of free vibrations of plant stems, focusing specifically on Arabidopsis stem vibrations, and its extended version, named AraVibS, to identify mutants with altered mechanical properties. These programs can be used without knowledge of Python and require only an inexpensive handmade setting stand and an iPhone/iPad with a high-speed shooting function for data acquisition. Using our system, we identified an nst1 nst3 double-mutant lacking secondary cell walls in fiber cells and a wrky12 mutant displaying ectopic formation of secondary cell wall compared with wild type by employing only two growth traits (stem height and fresh weight) in addition to videos of stem vibrations. Furthermore, we calculated the logarithmic decrement, the damping ratio, the natural frequency and the stiffness based on the spring-mass-damper model from the video data using AraVib. The stiffness was estimated to be drastically decreased in nst1 nst3, which agreed with previous tensile test results. However, in wrky12, the stiffness was significantly increased. These results demonstrate the effectiveness of our new system. Because our method can be applied in a high-throughput manner, it can be used to screen for mutants with altered mechanical properties.

scholarly journals Massively parallel encapsulation of single cells with structured microparticles and secretion-based flow sorting

2020 ◽  
Author(s):  
Joseph de Rutte ◽  
Robert Dimatteo ◽  
Mark van Zee ◽  
Robert Damoiseaux ◽  
Dino Di Carlo
Keyword(s):  
High Throughput ◽  
Cell Attachment ◽  
Single Cells ◽  
Chinese Hamster Ovary Cells ◽  
Chinese Hamster ◽  
Cell Secretion ◽  
High Throughput Analysis ◽  
Ovary Cells ◽  
Hydrogel Microparticles ◽  
Specialized Equipment

AbstractTechniques to analyze and sort single cells based on secreted products have the potential to transform our understanding of cellular biology as well as accelerate the development of next generation cell and antibody therapies. However, secretions are rapidly transported away from cells, such that specialized equipment and expertise has been required to compartmentalize cells and capture their secretions. Herein we demonstrate the use of cavity-containing hydrogel microparticles to perform functional single-cell secretion analysis and sorting using only commonly accessible lab infrastructure. These microparticles act as a solid support which facilitates cell attachment, templates formation of uniform aqueous compartments which prevent cross-talk between cells, and captures secreted proteins. Using this platform we demonstrate high-throughput analysis and sorting of Chinese Hamster Ovary cells based on their relative production of human IgG using commercially available flow sorters. Microparticles are easily distributed and used, democratizing access to high-throughput functional cell screening.

scholarly journals GREPore-seq: A Robust Workflow to Detect Changes after Gene Editing through Long-range PCR and Nanopore Sequencing

2021 ◽  
Author(s):  
Zi-Jun Quan ◽  
Si-Ang Li ◽  
Zhi-Xue Yang ◽  
Juan-Juan Zhao ◽  
Guo-Hua Li ◽  
...  
Keyword(s):  
Long Range ◽  
Large Scale ◽  
Gene Editing ◽  
Analysis Data ◽  
Error Rates ◽  
Nanopore Sequencing ◽  
Large Fragment ◽  
Genetic Changes ◽  
Long Range Pcr ◽  
Plasmid Backbone

To achieve the enormous potential of gene-editing technology in clinical therapies, both the on-target and unintended editing consequences need to be thoroughly evaluated. However, there is a lack of a comprehensive, pipelined, large-scale and economical workflow for detecting genome editing outcomes, in particular insertion or deletion of a large fragment. Here, we describe an approach for efficient and accurate detection of multiple genetic changes after CRISPR-Cas9 editing by pooled nanopore sequencing of barcoded long-range PCR products. To overcome the high error rates and indels of nanopore sequencing, we developed a pipeline to capture the barcoded sequences by grepping reads of nanopore amplicon sequencing (GREPore-seq). GREPore-seq can detect NHEJ-mediated double-stranded oligodeoxynucleotide (dsODN) insertions with comparable accuracy to Illumina next-generation sequencing (NGS). GREPore-seq also identifies HDR-mediated large gene knock-in, which excellently correlates with FACS analysis data. Low-level plasmid backbone insertion after HDR editing was also detected. We have established a practical workflow to identify genetic changes, including quantifying dsODN insertions, knock-ins, plasmid backbone insertions, and large fragment deletions after CRISPR editing. This toolkit for nanopore sequencing of pooled long amplicons should have broad applications in assessing on-target HDR editing and inadvertent large indels of over 1 kb. GREPore-seq is freely available at GitHub (https://github.com/lisiang/GREPore-seq).

scholarly journals High-throughput cell transplantation establishes that tumor-initiating cells are abundant in zebrafish T-cell acute lymphoblastic leukemia

Blood ◽  
2010 ◽  
Vol 115 (16) ◽  
pp. 3296-3303 ◽  
Author(s):  
Alexandra C. H. Smith ◽  
Aubrey R. Raimondi ◽  
Chris D. Salthouse ◽  
Myron S. Ignatius ◽  
Jessica S. Blackburn ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Cell Transplantation ◽  
High Throughput ◽  
Large Scale ◽  
Lymphoblastic Leukemia ◽  
Rapid Screening ◽  
Tumor Initiating Cells ◽  
Self Renewal ◽  
Large Numbers

Abstract Self-renewal is a feature of cancer and can be assessed by cell transplantation into immune-compromised or immune-matched animals. However, studies in zebrafish have been severely limited by lack of these reagents. Here, Myc-induced T-cell acute lymphoblastic leukemias (T-ALLs) have been made in syngeneic, clonal zebrafish and can be transplanted into sibling animals without the need for immune suppression. These studies show that self-renewing cells are abundant in T-ALL and comprise 0.1% to 15.9% of the T-ALL mass. Large-scale single-cell transplantation experiments established that T-ALLs can be initiated from a single cell and that leukemias exhibit wide differences in tumor-initiating potential. T-ALLs also can be introduced into clonal-outcrossed animals, and T-ALLs arising in mixed genetic backgrounds can be transplanted into clonal recipients without the need for major histocompatibility complex matching. Finally, high-throughput imaging methods are described that allow large numbers of fluorescent transgenic animals to be imaged simultaneously, facilitating the rapid screening of engrafted animals. Our experiments highlight the large numbers of zebrafish that can be experimentally assessed by cell transplantation and establish new high-throughput methods to functionally interrogate gene pathways involved in cancer self-renewal.

scholarly journals High-throughput analysis of yeast replicative aging using a microfluidic system

2015 ◽  
Vol 112 (30) ◽  
pp. 9364-9369 ◽  
Author(s):  
Myeong Chan Jo ◽  
Wei Liu ◽  
Liang Gu ◽  
Weiwei Dang ◽  
Lidong Qin
Keyword(s):  
High Resolution ◽  
High Throughput ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Single Cell Analysis ◽  
Retention Rate ◽  
Yeast Cells ◽  
High Throughput Analysis ◽  
Replicative Lifespan

Saccharomyces cerevisiaehas been an important model for studying the molecular mechanisms of aging in eukaryotic cells. However, the laborious and low-throughput methods of current yeast replicative lifespan assays limit their usefulness as a broad genetic screening platform for research on aging. We address this limitation by developing an efficient, high-throughput microfluidic single-cell analysis chip in combination with high-resolution time-lapse microscopy. This innovative design enables, to our knowledge for the first time, the determination of the yeast replicative lifespan in a high-throughput manner. Morphological and phenotypical changes during aging can also be monitored automatically with a much higher throughput than previous microfluidic designs. We demonstrate highly efficient trapping and retention of mother cells, determination of the replicative lifespan, and tracking of yeast cells throughout their entire lifespan. Using the high-resolution and large-scale data generated from the high-throughput yeast aging analysis (HYAA) chips, we investigated particular longevity-related changes in cell morphology and characteristics, including critical cell size, terminal morphology, and protein subcellular localization. In addition, because of the significantly improved retention rate of yeast mother cell, the HYAA-Chip was capable of demonstrating replicative lifespan extension by calorie restriction.

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