Evidence map›Paper›PMID 32771078›Full record

ArticleBiophysical journal2020

Anomalous Diffusion Characterization by Fourier Transform-FRAP with Patterned Illumination.

Andreas C Geiger, Casey J Smith, Nita Takanti, Dustin M Harmon, Mark S Carlsen, Garth J Simpson

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In one paragraph

Article in Biophysical journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Andreas C GeigerDepartment of Chemistry, Purdue University, West Lafayette, Indiana.
Casey J SmithDepartment of Chemistry, Purdue University, West Lafayette, Indiana.
Nita TakantiDepartment of Chemistry, Purdue University, West Lafayette, Indiana.
Dustin M HarmonDepartment of Chemistry, Purdue University, West Lafayette, Indiana.
Mark S CarlsenJonathan Amy Facility for Chemical Instrumentation, Purdue University, West Lafayette, Indiana.
Garth J SimpsonDepartment of Chemistry, Purdue University, West Lafayette, Indiana. Electronic address: gsimpson@purdue.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Fourier transform fluorescence recovery after photobleaching (FT-FRAP) with patterned illumination is theorized and demonstrated for quantitatively evaluating normal and anomalous diffusion. Diffusion characterization is routinely performed to assess mobility in cell biology, pharmacology, and food science. Conventional FRAP is noninvasive, has low sample volume requirements, and can rapidly measure diffusion over distances of a few micrometers. However, conventional point-bleach measurements are complicated by signal-to-noise limitations, the need for precise knowledge of the photobleach beam profile, potential for bias due to sample heterogeneity, and poor compatibility with multiphoton excitation because of local heating. In FT-FRAP with patterned illumination, the time-dependent fluorescence recovery signal is concentrated to puncta in the spatial Fourier domain, with substantial improvements in signal-to-noise, mathematical simplicity, representative sampling, and multiphoton compatibility. A custom nonlinear optical beam-scanning microscope enabled patterned illumination for photobleaching through two-photon excitation. Measurements in the spatial Fourier domain removed dependence on the photobleach profile, suppressing bias from imprecise knowledge of the point spread function. For normal diffusion, the fluorescence recovery produced a simple single-exponential decay in the spatial Fourier domain, in excellent agreement with theoretical predictions. Simultaneous measurement of diffusion at multiple length scales was enabled through analysis of multiple spatial harmonics of the photobleaching pattern. Anomalous diffusion was characterized by FT-FRAP through a nonlinear fit to multiple spatial harmonics of the fluorescence recovery. Constraining the fit to describe diffusion over multiple length scales resulted in higher confidence in the recovered fitting parameters. Additionally, phase analysis in FT-FRAP was shown to inform on flow/sample translation.

Indexed as

LightingDiffusionFluorescence Recovery After PhotobleachingFourier AnalysisPhotobleaching

Identifiers

PMID32771078
PMCPMC7451902

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.