High-throughput scanning confocal microscope for single molecule analysis

2004 ◽  
Vol 84 (7) ◽  
pp. 1216-1218 ◽  
Author(s):  
Chandran R. Sabanayagam ◽  
John S. Eid ◽  
Amit Meller
Keyword(s):  
High Throughput ◽  
Single Molecule ◽  
Confocal Microscope ◽  
Single Molecule Analysis

scholarly journals High-Throughput Single-Molecule Analysis of Nucleic Acid Enzyme Activity by Tethered Particle Motion

2012 ◽  
Vol 102 (3) ◽  
pp. 383a
Author(s):  
Thomas Plenat ◽  
Catherine Tardin ◽  
Philippe Rousseau ◽  
Laurence Salome
Keyword(s):  
Enzyme Activity ◽  
Nucleic Acid ◽  
High Throughput ◽  
Single Molecule ◽  
Particle Motion ◽  
Tethered Particle Motion ◽  
Single Molecule Analysis

scholarly journals High-Throughput Single-Molecule Analysis via Divisive Segmentation and Clustering

2019 ◽  
Author(s):  
David S. White ◽  
Marcel P. Goldschen-Ohm ◽  
Randall H. Goldsmith ◽  
Baron Chanda
Keyword(s):  
High Throughput ◽  
Single Molecule ◽  
Data Sets ◽  
Binding Domains ◽  
High Throughput Experiments ◽  
Single Molecule Analysis ◽  
Analysis Platform ◽  
Improved Accuracy ◽  
Insight Into ◽  
Divisive Clustering

ABSTRACTSingle-molecule approaches provide insight into the dynamics of biomolecules, yet analysis methods have not scaled with the growing size of data sets acquired in high-throughput experiments. We present a new analysis platform (DISC) that uses divisive clustering to accelerate unsupervised analysis of single-molecule trajectories by up to three orders of magnitude with improved accuracy. Using DISC, we reveal an inherent lack of cooperativity between cyclic nucleotide binding domains from HCN pacemaker ion channels embedded in nanophotonic zero-mode waveguides.

scholarly journals Top-down machine learning approach for high-throughput single-molecule analysis

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
David S White ◽  
Marcel P Goldschen-Ohm ◽  
Randall H Goldsmith ◽  
Baron Chanda
Keyword(s):  
High Throughput ◽  
Single Molecule ◽  
Viterbi Algorithm ◽  
Binding Domains ◽  
Model Free ◽  
Pacemaker Channel ◽  
Multiple Binding ◽  
Single Molecule Analysis ◽  
Analysis Platform ◽  
Improved Accuracy

Single-molecule approaches provide enormous insight into the dynamics of biomolecules, but adequately sampling distributions of states and events often requires extensive sampling. Although emerging experimental techniques can generate such large datasets, existing analysis tools are not suitable to process the large volume of data obtained in high-throughput paradigms. Here, we present a new analysis platform (DISC) that accelerates unsupervised analysis of single-molecule trajectories. By merging model-free statistical learning with the Viterbi algorithm, DISC idealizes single-molecule trajectories up to three orders of magnitude faster with improved accuracy compared to other commonly used algorithms. Further, we demonstrate the utility of DISC algorithm to probe cooperativity between multiple binding events in the cyclic nucleotide binding domains of HCN pacemaker channel. Given the flexible and efficient nature of DISC, we anticipate it will be a powerful tool for unsupervised processing of high-throughput data across a range of single-molecule experiments.

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