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Three-dimensional angular scattering simulations inform analysis of scattering from single cells

SIGNIFICANCE: Organelle sizes, which are indicative of cellular status, have implications for drug development and immunology research. At the single cell level, such information could be used to study the heterogeneity of cell response to drugs or pathogens. AIM: Angularly resolved elastic light sc...

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Detalles Bibliográficos
Autores principales: Dunn, Kaitlin J., Berger, Andrew J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10411915/
https://www.ncbi.nlm.nih.gov/pubmed/37564163
http://dx.doi.org/10.1117/1.JBO.28.8.086501
Descripción
Sumario:SIGNIFICANCE: Organelle sizes, which are indicative of cellular status, have implications for drug development and immunology research. At the single cell level, such information could be used to study the heterogeneity of cell response to drugs or pathogens. AIM: Angularly resolved elastic light scattering is known to be sensitive to changes in organelle size distribution. We developed a Mie theory-based simulation of angular scattering from single cells to quantify the effects of noise on scattering and size estimates. APPROACH: We simulated randomly sampled organelle sizes (drawn from a log normal distribution), interference between different organelles’ scattering, and detector noise. We quantified each noise source’s effect upon the estimated mean and standard deviation of organelle size distributions. RESULTS: The results demonstrate that signal-to-noise ratio in the angular scattering increased with the number of scatterers, cell area, and exposure time and decreased with the size distribution width. The error in estimating the mean of the size distributions remained below 5% for nearly all experimental parameters tested, but the widest size distribution tested (standard deviation of 600 nm) reached 20%. CONCLUSIONS: The simulator revealed that sparse sampling of a broad size distribution can dominate the mismatch between actual and predicted size parameters. Alternative estimation strategies could reduce the discrepancy.