scholarly journals Automated Raman Micro-Spectroscopy of Epithelial Cell Nuclei for High-Throughput Classification

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4767
Author(s):  
Kevin O’Dwyer ◽  
Katarina Domijan ◽  
Adam Dignam ◽  
Marion Butler ◽  
Bryan M. Hennelly
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Software Architectures ◽  
Cell Nuclei ◽  
Recording Time ◽  
Non Invasive ◽  
Glass Slides ◽  
Image Processing Algorithms ◽  
Clinical Adoption

Raman micro-spectroscopy is a powerful technique for the identification and classification of cancer cells and tissues. In recent years, the application of Raman spectroscopy to detect bladder, cervical, and oral cytological samples has been reported to have an accuracy greater than that of standard pathology. However, despite being entirely non-invasive and relatively inexpensive, the slow recording time, and lack of reproducibility have prevented the clinical adoption of the technology. Here, we present an automated Raman cytology system that can facilitate high-throughput screening and improve reproducibility. The proposed system is designed to be integrated directly into the standard pathology clinic, taking into account their methodologies and consumables. The system employs image processing algorithms and integrated hardware/software architectures in order to achieve automation and is tested using the ThinPrep standard, including the use of glass slides, and a number of bladder cancer cell lines. The entire automation process is implemented, using the open source Micro-Manager platform and is made freely available. We believe that this code can be readily integrated into existing commercial Raman micro-spectrometers.

scholarly journals Automated Raman micro-spectroscopy of epithelial cells for the high-throughput classification

2021 ◽  
Author(s):  
Kevin O’Dwyer ◽  
Katarina Domijan ◽  
Adam Dignam ◽  
Marion Butler ◽  
Bryan M. Hennelly
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Software Architectures ◽  
Recording Time ◽  
Non Invasive ◽  
Glass Slides ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Clinical Adoption

AbstractRaman micro-spectroscopy is a powerful technique for the identification and classification of cancer cells and tissues. In recent years, the application of Raman spectroscopy to detect bladder, cervical, and oral cytological samples has been reported to have an accuracy that is greater than standard pathology. However, despite being entirely non-invasive and relatively inexpensive, the slow recording time, and lack of reproducibility, have prevented the clinical adoption of the technology. Here we present an automated Raman cytology system that can facilitate high-throughput screening and improve reproducibility. The proposed system is designed to be integrated directly into the standard pathology clinic, taking into account their methodologies and consumables. The system employs image processing algorithms and integrated hardware/software architectures in order to achieve automation and is tested using the ThinPrep standard, including the use of glass slides, and a number of bladder cancer cell lines. The entire automation process is implemented using the open source Micro-Manager platform, and is made freely available. We believe this code can be readily integrated into existing commercial Raman micro-spectrometers.

scholarly journals High-throughput screening and classification of chemicals and their effects on neuronal gene expression using RASL-seq

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Jeremy M. Simon ◽  
Smita R. Paranjape ◽  
Justin M. Wolter ◽  
Gabriela Salazar ◽  
Mark J. Zylka
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
High Throughput Screening ◽  
Neuronal Gene

High-throughput screening and classification of layered di-metal chalcogenides

Nanoscale ◽  
2019 ◽  
Vol 11 (29) ◽  
pp. 13924-13933 ◽  
Author(s):  
Jinchen Wei ◽  
Chao Wang ◽  
Tao Zhang ◽  
Chen-Min Dai ◽  
Shiyou Chen
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
2D Materials ◽  
Metal Chalcogenides

450 layered di-metal chalcogenides are identified and may be exfoliated into novel 2D materials, including 142 semiconductors and 83 magnetic crystals.

scholarly journals Convolutional neural networks for high throughput screening of catalyst layer inks for polymer electrolyte fuel cells

RSC Advances ◽  
2021 ◽  
Vol 11 (51) ◽  
pp. 32126-32134
Author(s):  
Mohammad J. Eslamibidgoli ◽  
Fabian P. Tipp ◽  
Jenia Jitsev ◽  
Jasna Jankovic ◽  
Michael H. Eikerling ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Fuel Cells ◽  
Polymer Electrolyte ◽  
Convolutional Neural Networks ◽  
High Throughput ◽  
High Throughput Screening ◽  
Catalyst Layer ◽  
Polymer Electrolyte Fuel Cells

Deep learning enables the robust and accurate classification of the TEM images of catalyst layer inks for the polymer electrolyte fuel cells.

Second-generation MS-based high-throughput screening system for enantioselective catalysts and biocatalysts

2002 ◽  
Vol 80 (6) ◽  
pp. 626-632 ◽  
Author(s):  
Wolfgang Schrader ◽  
Andreas Eipper ◽  
D Jonathan Pugh ◽  
Manfred T Reetz
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Second Generation ◽  
Enantiomeric Excess ◽  
Microtiter Plate ◽  
Ion Source ◽  
Mass Spectrometric ◽  
Phenyl Ether ◽  
Recording Time ◽  
Metal Catalysis

A high-throughput method is described, where the enantioselectivity of approximately 10 000 catalysts or biocatalysts can be determined per day. The method is based on electrospray mass spectrometric techniques using an eight-channel multiplexed (MUX) sprayer system connected to a time-of-flight mass spectrometer. The inlet of the ion source is controlled by a stepping rotor that is continuously moving from one sprayer to the next with a recording time of 100 ms for each channel and a delay time of 50 ms, thus allowing a spectrum to be obtained from each channel every 1.2 s. One cycle, where eight samples are being sprayed in parallel, requires around 70 s, which allows a 96-well microtiter plate to be screened in 14 min. Integration of two pseudo-enantiomers (S)-glycidyl phenyl ether and (R)-D5-glycidyl phenyl ether is necessary to quantify the enantiomeric excess (ee-value), where one enantiomer is isotopically labeled to allow easy identification of the mass spectrometric signals. Errors of ~2% for the ee-values indicate that in addition to the significant improvement in sample throughput this is also a precise method for high-throughput screening. This second-generation assay is useful for combinatorial enantioselective transition-metal catalysis and in the directed evolution of enantioselective enzymes.

Sign in / Sign up

Export Citation Format

Share Document