Image correlation microscopy for uniform illumination

Abstract

Image cross-correlation microscopy (ICM) is a technique that quantifies the motion of fluorescent features in an image by measuring the temporal autocorrelation function decay in a time-lapse image sequence. ICM has traditionally employed laser-scanning microscopes because the technique emerged as an extension of laser-based fluorescence correlation spectroscopy (FCS). In this work, we show that image correlation can also be used to measure fluorescence dynamics in uniform illumination or wide-field imaging systems and we call our new approach uniform illumination image correlation microscopy (UI-ICM). Wide-field microscopy is not only a simpler, less expensive imaging modality, but it offers the capability of greater temporal resolution over laser-scanning systems. In traditional laser-scanning ICM, lateral mobility is calculated from the temporal de-correlation of an image, where the characteristic length is the illuminating laser beam width. In wide-field microscopy, the diffusion length is defined by the feature size using the spatial autocorrelation function (SACF). Correlation function decay in time occurs as an object diffuses from its original position. We show that theoretical and simulated comparisons between Gaussian and uniform features indicate the temporal autocorrelation function (TACF) depends strongly on particle size and not particle shape. In this report, we establish the relationships between the SACF feature size, TACF characteristic time and the diffusion coefficient for UI-ICM using analytical, Monte-Carlo and experimental validation with particle tracking algorithms. Additionally, we demonstrate UI-ICM analysis of adhesion molecule domain aggregation and diffusion on the surface of human neutrophils.

Use of TIRF to Monitor T-Lymphocyte Membrane Dynamics with Submicrometer and Subsecond Resolution

“…The most convenient way of determining the background was found to use the mode , i.e., the most frequent pixel brightness on a full image of 1002 × 1004 pixels. The temporal autocorrelation function as defined by Gaborski et al 12. was calculated with a custom-made ImageJ plugin (source is available on request).…”

Dynamics of adhesion molecule domains on neutrophil membranes: surfing the dynamic cell topography

“…Neutrophils were labeled and prepared for microscopy as previously described (Gaborski et al 2010; Gaborski et al 2008). Briefly, cells were labeled by diluting whole blood into Hank's Balanced Salt Solution (HBSS) with 10 mM HEPES containing with fluorescently labeled for L-selectin (anti-CD62L; DREG-56, eBioscience, San Diego, CA) PSGL-1 (anti-CD162; PL1/3E2-25-5; Ancell, Bayport, MN), LFA-1 (anti-α L or CD11a; HI111; eBioscience, San Diego, CA) or Mac-1 (anti-α M or CD11b; ICRF44; eBioscience, San Diego, CA).…”

“…The temporal decay of the image autocorrelation function was calculated using established algorithms (Petersen et al 1993; Wiseman et al 2000). The time constant for autocorrelation decay was used in combination with a length scale calculated from the spatial autocorrelation function (Gaborski et al 2010). …”

“…When RAAMs and β 2 integrins were co-labeled on the same cell, it was evident that β 2 integrin domains move more rapidly over the neutrophil surface than RAAM domains. Because FRAP data also revealed RAAMs to be less mobile than β 2 integrins, we developed uniform-illumination image correlation microscopy (UI-ICM) (Gaborski et al 2010) to calculate the mobilities of receptor domains in epi-fluorescent time-lapsed sequences and compare them to lateral mobilities determined using FRAP.…”

“…UI-ICM differs from traditional ICM in that it determines the mobility of molecules in uniform illumination. The spatial dimension over which diffusion must occur for decorrelation is the feature size in the image itself, which is determined by spatial autocorrelation of the fluorescence image (Gaborski et al 2010). With the diffusion length scale defined, cluster diffusion coefficients are then determined from the decay of the autocorrelation function with time.…”