scholarly journals Microfluidic Technologies for High Throughput Screening Through Sorting and On-Chip Culture of C. elegans

Molecules ◽  
2019 ◽  
Vol 24 (23) ◽  
pp. 4292 ◽  
Author(s):  
Daniel Midkiff ◽  
Adriana San-Miguel
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Model Organism ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
C Elegans ◽  
Drug Candidates ◽  
On Chip ◽  
And Behavior

The nematode Caenorhabditis elegans is a powerful model organism that has been widely used to study molecular biology, cell development, neurobiology, and aging. Despite their use for the past several decades, the conventional techniques for growth, imaging, and behavioral analysis of C. elegans can be cumbersome, and acquiring large data sets in a high-throughput manner can be challenging. Developments in microfluidic “lab-on-a-chip” technologies have improved studies of C. elegans by increasing experimental control and throughput. Microfluidic features such as on-chip control layers, immobilization channels, and chamber arrays have been incorporated to develop increasingly complex platforms that make experimental techniques more powerful. Genetic and chemical screens are performed on C. elegans to determine gene function and phenotypic outcomes of perturbations, to test the effect that chemicals have on health and behavior, and to find drug candidates. In this review, we will discuss microfluidic technologies that have been used to increase the throughput of genetic and chemical screens in C. elegans. We will discuss screens for neurobiology, aging, development, behavior, and many other biological processes. We will also discuss robotic technologies that assist in microfluidic screens, as well as alternate platforms that perform functions similar to microfluidics.

scholarly journals Mask R-CNN Based C. Elegans Detection with a DIY Microscope

Biosensors ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 257
Author(s):  
Sebastian Fudickar ◽  
Eike Jannik Nustede ◽  
Eike Dreyer ◽  
Julia Bornhorst
Keyword(s):  
Cell Biology ◽  
High Throughput Screening ◽  
Low Cost ◽  
Image Acquisition ◽  
Rapid Development ◽  
Model Organism ◽  
Large Data ◽  
Data Set ◽  
C Elegans ◽  
Do It Yourself

Caenorhabditis elegans (C. elegans) is an important model organism for studying molecular genetics, developmental biology, neuroscience, and cell biology. Advantages of the model organism include its rapid development and aging, easy cultivation, and genetic tractability. C. elegans has been proven to be a well-suited model to study toxicity with identified toxic compounds closely matching those observed in mammals. For phenotypic screening, especially the worm number and the locomotion are of central importance. Traditional methods such as human counting or analyzing high-resolution microscope images are time-consuming and rather low throughput. The article explores the feasibility of low-cost, low-resolution do-it-yourself microscopes for image acquisition and automated evaluation by deep learning methods to reduce cost and allow high-throughput screening strategies. An image acquisition system is proposed within these constraints and used to create a large data-set of whole Petri dishes containing C. elegans. By utilizing the object detection framework Mask R-CNN, the nematodes are located, classified, and their contours predicted. The system has a precision of 0.96 and a recall of 0.956, resulting in an F1-Score of 0.958. Considering only correctly located C. elegans with an [email protected] IoU, the system achieved an average precision of 0.902 and a corresponding F1 Score of 0.906.

scholarly journals High-throughput film-densitometry: an efficient approach to generate large data sets

2005 ◽  
Vol 149 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Dieter Typke ◽  
Robert A. Nordmeyer ◽  
Arthur Jones ◽  
Juyoung Lee ◽  
Agustin Avila-Sakar ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Efficient Approach

High-Throughput Microfluidic Sorting of C. elegans for Automated Force Pattern Measurement

2011 ◽  
Vol 700 ◽  
pp. 182-187 ◽  
Author(s):  
Shazlina Johari ◽  
Volker Nock ◽  
Maan M. Alkaisi ◽  
Wen Wang
Keyword(s):  
High Throughput ◽  
Force Measurement ◽  
Model Organism ◽  
Pdms Layer ◽  
Fluidic Channel ◽  
Motion Patterns ◽  
C Elegans ◽  
Area Of Interest ◽  
On Chip ◽  
Gas Layer

With a reduced set of 300 neurons and a fully sequenced genome, the multicellular nematodeCaenorhabditis eleganshas recently gained increasing interest as a model organism for neurobiological studies. One particular area of interest is related to worm locomotion and the investigation of the correlation between individual genes, neurons, muscle arms and the motion pattern of the nematodes. To characterize motion patterns of movingC. eleganswe have previously demonstrated an automated force measurement setup using microfabricated polydimethylsiloxane (PDMS) pillars and image processing. In this paper we introduce an integrated microfluidic device for worm sorting and force measurement. The device allows for high-throughput measurements by combining sorting functions on-chip with the existing force pattern measurement system. A horizontal sorting channel and branching vertical pillar array channels are utilized for worm sorting. Using the former, the nematodes can be flow-directed into arrays of 40 µm and 60 µm diameter pillars based on worm size and type. This improves animal survival and increases the relevance of the force measurement by allowing one to match the amplitude of the worm movement to the pillar spacing. The PDMS based device consists of three layers: a fluidic layer with pillars for force measurement at the bottom, a gas layer on top and a thin PDMS layer sandwiched between them. By applying pressure to the gas layer, the membrane in the middle will be deflected thus restricting the worms’ movement in the fluidic channel.

scholarly journals A Propagated Skeleton Approach to High Throughput Screening of Neurite Outgrowth for In Vitro Parkinson’s Disease Modelling

Cells ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 931
Author(s):  
Justus Schikora ◽  
Nina Kiwatrowski ◽  
Nils Förster ◽  
Leonie Selbach ◽  
Friederike Ostendorf ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Neurite Outgrowth ◽  
High Throughput ◽  
High Throughput Screening ◽  
Image Data ◽  
Large Data ◽  
Therapeutic Effects ◽  
Data Sets

Neuronal models of neurodegenerative diseases such as Parkinson’s Disease (PD) are extensively studied in pathological and therapeutical research with neurite outgrowth being a core feature. Screening of neurite outgrowth enables characterization of various stimuli and therapeutic effects after lesion. In this study, we describe an autonomous computational assay for a high throughput skeletonization approach allowing for quantification of neurite outgrowth in large data sets from fluorescence microscopic imaging. Development and validation of the assay was conducted with differentiated SH-SY5Y cells and primary mesencephalic dopaminergic neurons (MDN) treated with the neurotoxic lesioning compound Rotenone. Results of manual annotation using NeuronJ and automated data were shown to correlate strongly (R2-value 0.9077 for SH-SY5Y cells and R2-value 0.9297 for MDN). Pooled linear regressions of results from SH-SY5Y cell image data could be integrated into an equation formula (y=0.5410·x+1792; y=0.8789·x+0.09191 for normalized results) with y depicting automated and x depicting manual data. This automated neurite length algorithm constitutes a valuable tool for modelling of neurite outgrowth that can be easily applied to evaluate therapeutic compounds with high throughput approaches.

scholarly journals Artificial Neural Network—Based Analysis of High-Throughput Screening Data for Improved Prediction of Active Compounds

2009 ◽  
Vol 14 (10) ◽  
pp. 1236-1244 ◽  
Author(s):  
Swapan Chakrabarti ◽  
Stan R. Svojanovsky ◽  
Romana Slavik ◽  
Gunda I. Georg ◽  
George S. Wilson ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Back Propagation ◽  
Large Data ◽  
Classification Performance ◽  
Data Sets ◽  
Data Set ◽  
Active Compounds ◽  
Physicochemical Features ◽  
Artificial Neural

Artificial neural networks (ANNs) are trained using high-throughput screening (HTS) data to recover active compounds from a large data set. Improved classification performance was obtained on combining predictions made by multiple ANNs. The HTS data, acquired from a methionine aminopeptidases inhibition study, consisted of a library of 43,347 compounds, and the ratio of active to nonactive compounds, R A/N, was 0.0321. Back-propagation ANNs were trained and validated using principal components derived from the physicochemical features of the compounds. On selecting the training parameters carefully, an ANN recovers one-third of all active compounds from the validation set with a 3-fold gain in R A/N value. Further gains in RA/N values were obtained upon combining the predictions made by a number of ANNs. The generalization property of the back-propagation ANNs was used to train those ANNs with the same training samples, after being initialized with different sets of random weights. As a result, only 10% of all available compounds were needed for training and validation, and the rest of the data set was screened with more than a 10-fold gain of the original RA/N value. Thus, ANNs trained with limited HTS data might become useful in recovering active compounds from large data sets.

scholarly journals High-Throughput Tabular Data Processor – Platform independent graphical tool for processing large data sets

PLoS ONE ◽  
2018 ◽  
Vol 13 (2) ◽  
pp. e0192858
Author(s):  
Piotr Madanecki ◽  
Magdalena Bałut ◽  
Patrick G. Buckley ◽  
J. Renata Ochocka ◽  
Rafał Bartoszewski ◽  
...  
Keyword(s):  
High Throughput ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Tabular Data ◽  
Graphical Tool ◽  
Data Processor

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

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