Automated imaging of circulating fluorocytes for the diagnosis of erythropoietic protoporphyria: a pilot study for population screening

2008 ◽  
Vol 15 (4) ◽  
pp. 199-203 ◽  
Author(s):  
Kin-Chong Lau ◽  
Ching-Wan Lam
Keyword(s):  
Pilot Study ◽  
High Throughput ◽  
Imaging System ◽  
Blood Film ◽  
Population Screening ◽  
Fresh Blood ◽  
Erythropoietic Protoporphyria ◽  
High Throughput Analysis ◽  
Automated Imaging ◽  
Film Method

Objectives To improve the traditional fresh blood film method to a high-throughput analysis of the presence of circulating fluorescent red cells (fluorocytes) in erythropoietic protoporphyria (EPP) using an automated imaging system. Methods Based on the autofluorescence of protoporphyrin, we used an automatic image acquisition platform for examining fluorocytes in peripheral blood with minimal sample preparation. The image acquisition is easy-to-use under automated operations of excitation, focusing, detection and data analysis. Quality image and semi-quantitative fluorescence measurement of fluorocytes can be generated in a single step. For high-throughput analysis, the platform can image more than 200 96-well micro-plates, i.e. 19200 samples, in approximately 10 hours. Importantly, the reagent cost of analysis is negligible. Results In this pilot study, three EPP patients were diagnosed and 4000 normal individuals were screened for EPP by this method. Our results showed that the method can distinguish the overt case and asymptomatic carriers. It gives reliable evidence for rapid EPP screening. Conclusion This automated imaging system provides multiple advantages that improve the traditional fresh blood film method as a more effective diagnostic tool and facilitates population screening for EPP. As fluorocytes are present in the umbilical cord blood of EPP patients, this high-throughput method can be potentially used for newborn screening of EPP.

Combination of Free Software, Light Microscopy and Non-specific Dyes for a High-throughput Analysis in Live/Dead Cell Count

2015 ◽  
Vol 11 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Luciano Cardoso ◽  
Suellen Cordeiro ◽  
Marcio Fronza ◽  
Denise Endringer ◽  
Tadeu de Andrade ◽  
...  
Keyword(s):  
Light Microscopy ◽  
Cell Count ◽  
High Throughput ◽  
Free Software ◽  
Dead Cell ◽  
High Throughput Analysis ◽  
Throughput Analysis

Multiwell Combinatorial Hydrogel Array for High-Throughput Analysis of Cell–ECM Interactions

2021 ◽  
Author(s):  
Ruoxing Lei ◽  
Erin A. Akins ◽  
Kelly C. Y. Wong ◽  
Nicole A. Repina ◽  
Kayla J. Wolf ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Analysis ◽  
Throughput Analysis

scholarly journals A high-throughput screening assay based on automated microscopy for monitoring antibiotic susceptibility of Mycobacterium tuberculosis phenotypes

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sadaf Kalsum ◽  
Blanka Andersson ◽  
Jyotirmoy Das ◽  
Thomas Schön ◽  
Maria Lerm
Keyword(s):  
Mycobacterium Tuberculosis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Live Cell Imaging ◽  
Cell Imaging ◽  
Imaging System ◽  
Aggregate Size ◽  
Live Cell ◽  
Screening Assay ◽  
High Throughput Screening Assay

Abstract Background Efficient high-throughput drug screening assays are necessary to enable the discovery of new anti-mycobacterial drugs. The purpose of our work was to develop and validate an assay based on live-cell imaging which can monitor the growth of two distinct phenotypes of Mycobacterium tuberculosis and to test their susceptibility to commonly used TB drugs. Results Both planktonic and cording phenotypes were successfully monitored as fluorescent objects using the live-cell imaging system IncuCyte S3, allowing collection of data describing distinct characteristics of aggregate size and growth. The quantification of changes in total area of aggregates was used to define IC50 and MIC values of selected TB drugs which revealed that the cording phenotype grew more rapidly and displayed a higher susceptibility to rifampicin. In checkerboard approach, testing pair-wise combinations of sub-inhibitory concentrations of drugs, rifampicin, linezolid and pretomanid demonstrated superior growth inhibition of cording phenotype. Conclusions Our results emphasize the efficiency of using automated live-cell imaging and its potential in high-throughput whole-cell screening to evaluate existing and search for novel antimycobacterial drugs.

Development of pattern recognition based on the nanosheet-DNA probes and an extendible DNA library

The Analyst ◽  
2021 ◽  
Author(s):  
Jiawei Qi ◽  
Pinhua Rao ◽  
Lele Wang ◽  
Li Xu ◽  
Yanli Wen ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
High Throughput ◽  
Analysis Data ◽  
Dna Probes ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
Dna Library ◽  
Array Sensing

Pattern recognition, also called “array sensing” is a recognition strategy with a wide and expandable analysis range, based on the high-throughput analysis data. In this work, we constructed a sensor...

Highly Sensitive Detection of Paraquat with Pillar[5]arene as an aptamer in α-Hemolysin Nanopore

2021 ◽  
Author(s):  
Xiaojia Jiang ◽  
Mingsong Zang ◽  
Fei Li ◽  
Chunxi Hou ◽  
Quan Luo ◽  
...  
Keyword(s):  
Real Time ◽  
High Throughput ◽  
Single Molecule ◽  
Single Molecule Detection ◽  
Sensitive Detection ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
The Real ◽  
Highly Sensitive ◽  
Highly Sensitive Detection

Biological nanopore-based techniques have attracted more and more attention recently in the field of single-molecule detection, because they allow the real-time, sensitive, high-throughput analysis. Herein, we report an engineered biological...

scholarly journals A generalised approach for high-throughput instance segmentation of stomata in microscope images

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Hiranya Jayakody ◽  
Paul Petrie ◽  
Hugo Jan de Boer ◽  
Mark Whitty
Keyword(s):  
High Throughput ◽  
Imaging Techniques ◽  
Detection Algorithm ◽  
Input Image ◽  
Detection Methods ◽  
Sample Collection ◽  
High Throughput Analysis ◽  
General Applicability ◽  
Additional Image ◽  
Bounding Boxes

Abstract Background Stomata analysis using microscope imagery provides important insight into plant physiology, health and the surrounding environmental conditions. Plant scientists are now able to conduct automated high-throughput analysis of stomata in microscope data, however, existing detection methods are sensitive to the appearance of stomata in the training images, thereby limiting general applicability. In addition, existing methods only generate bounding-boxes around detected stomata, which require users to implement additional image processing steps to study stomata morphology. In this paper, we develop a fully automated, robust stomata detection algorithm which can also identify individual stomata boundaries regardless of the plant species, sample collection method, imaging technique and magnification level. Results The proposed solution consists of three stages. First, the input image is pre-processed to remove any colour space biases occurring from different sample collection and imaging techniques. Then, a Mask R-CNN is applied to estimate individual stomata boundaries. The feature pyramid network embedded in the Mask R-CNN is utilised to identify stomata at different scales. Finally, a statistical filter is implemented at the Mask R-CNN output to reduce the number of false positive generated by the network. The algorithm was tested using 16 datasets from 12 sources, containing over 60,000 stomata. For the first time in this domain, the proposed solution was tested against 7 microscope datasets never seen by the algorithm to show the generalisability of the solution. Results indicated that the proposed approach can detect stomata with a precision, recall, and F-score of 95.10%, 83.34%, and 88.61%, respectively. A separate test conducted by comparing estimated stomata boundary values with manually measured data showed that the proposed method has an IoU score of 0.70; a 7% improvement over the bounding-box approach. Conclusions The proposed method shows robust performance across multiple microscope image datasets of different quality and scale. This generalised stomata detection algorithm allows plant scientists to conduct stomata analysis whilst eliminating the need to re-label and re-train for each new dataset. The open-source code shared with this project can be directly deployed in Google Colab or any other Tensorflow environment.

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