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[Preprint]. 2023 Sep 29:2023.09.27.559876. [Version 1] doi: 10.1101/2023.09.27.559876

Fig 3.

Fig 3.

SoTILT3D demonstrates increased 3D acquisition speeds compared with conventional analysis methods. a, Projections of 3D super-resolution reconstructions of microtubules after acquiring 1,000, 10,000, and 50,000 image frames with 0.025 nM imager strands and analyzed with least-squares fitting-based analysis software (Easy-DHPSF, top row) or with 0.2 nM imager strands and analyzed using deep learning (DECODE, bottom row). Scale bar 1 μm. b, Quantitative comparison of the number of localizations per length of microtubule over time achieved with Easy-DHPSF (yellow) and DECODE (purple). The average localizations per μm per second are shown in text above each line, demonstrating a ten-fold increase in speed when using DECODE. Each data point on the graph represents the average localizations per length of microtubule for three different microtubule sections and error bars are ± standard deviations of these data sets. c, Fourier ring correlation (FRC) curves in the xy, yz, and xz planes after 50,000 frames for the Easy-DHPSF-analyzed data and d, the DECODE-analyzed data, demonstrating improved resolution.