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Adaptive movement compensation for in vivo imaging of fast cellular dynamics within a moving tissue.

Laffray S, Pagès S, Dufour H, De Koninck P, De Koninck Y, Côté D - PLoS ONE (2011)

Bottom Line: We describe a fast, non-contact adaptive movement compensation approach, applicable to rough and weakly reflective surfaces, allowing real-time functional imaging from intrinsically moving tissue in live animals.The strategy involves enslaving the position of the microscope objective to that of the tissue surface in real-time through optical monitoring and a closed feedback loop.The performance of the system allows for efficient image locking even in conditions of random or irregular movements.

View Article: PubMed Central - PubMed

Affiliation: Centre de Recherche Université Laval Robert-Giffard, Université Laval, Québec, Canada.

ABSTRACT
In vivo non-linear optical microscopy has been essential to advance our knowledge of how intact biological systems work. It has been particularly enabling to decipher fast spatiotemporal cellular dynamics in neural networks. The power of the technique stems from its optical sectioning capability that in turn also limits its application to essentially immobile tissue. Only tissue not affected by movement or in which movement can be physically constrained can be imaged fast enough to conduct functional studies at high temporal resolution. Here, we show dynamic two-photon Ca(2+) imaging in the spinal cord of a living rat at millisecond time scale, free of motion artifacts using an optical stabilization system. We describe a fast, non-contact adaptive movement compensation approach, applicable to rough and weakly reflective surfaces, allowing real-time functional imaging from intrinsically moving tissue in live animals. The strategy involves enslaving the position of the microscope objective to that of the tissue surface in real-time through optical monitoring and a closed feedback loop. The performance of the system allows for efficient image locking even in conditions of random or irregular movements.

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Movement compensation efficiency under in vivo conditions.(A) Imaging sequence sampled from a movie recorded at 30 fps of in vivo spinal lamina I neurons labelled with a structural dye (Calcein). Each image represents the mean of 5 consecutive raw images, reducing the actual sampling rate to 6 fps. Upper and lower rows were respectively acquired when the compensation was OFF and ON. When it is OFF, the time-stack projection (Average) results in an image containing information from planes situated above and below the plane of interest (arrows). When it is ON, the time-stack projection results in an image containing information only from the plane of interest, yielding a highly contrasted image (arrow heads). Scale bar, 10 µm. (B upper graph) Normalized fluorescence intensity time course plotted for one cell in the field of view presented in (a) (dashed ROI) with the system OFF (large intensity fluctuations) and then ON (fluctuations reduced to less than 8% of the initial amplitude). (B lower graph) Portion of the time profile of a cross correlation index Icorr computed between the first image of Video S1 and each of the following images throughout the entire movie. The presented 25 sec portion is centered around zero, time point where the movement compensation device is turned ON. The ratio of Icorr standard deviations with and without movement compensation (92% in this example) was used as a measure of the efficiency of movement compensation in in vivo conditions. (C) Extended field of view from which the frames in A are taken. This image is the first frame of the Video S1 (sequence with movement compensation ON). Each frame was divided into a 14×25 grid (each grid element is 13×24 pixels) to perform cross-correlation analysis of pairs of successive images throughout the entire movie. (D) Logarithmic plot of the displacement vector distribution across the image: 70% of the vectors are  while 95% are <2.5 µm. (E) Examples of 5 typical traces of objective movement during compensation. (F) Cumulative probability plot of the objective positions across each of the represented traces (individual traces in grey, average trace in black). The mean objective displacement (5–95%) was 32±6 µm.
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pone-0019928-g002: Movement compensation efficiency under in vivo conditions.(A) Imaging sequence sampled from a movie recorded at 30 fps of in vivo spinal lamina I neurons labelled with a structural dye (Calcein). Each image represents the mean of 5 consecutive raw images, reducing the actual sampling rate to 6 fps. Upper and lower rows were respectively acquired when the compensation was OFF and ON. When it is OFF, the time-stack projection (Average) results in an image containing information from planes situated above and below the plane of interest (arrows). When it is ON, the time-stack projection results in an image containing information only from the plane of interest, yielding a highly contrasted image (arrow heads). Scale bar, 10 µm. (B upper graph) Normalized fluorescence intensity time course plotted for one cell in the field of view presented in (a) (dashed ROI) with the system OFF (large intensity fluctuations) and then ON (fluctuations reduced to less than 8% of the initial amplitude). (B lower graph) Portion of the time profile of a cross correlation index Icorr computed between the first image of Video S1 and each of the following images throughout the entire movie. The presented 25 sec portion is centered around zero, time point where the movement compensation device is turned ON. The ratio of Icorr standard deviations with and without movement compensation (92% in this example) was used as a measure of the efficiency of movement compensation in in vivo conditions. (C) Extended field of view from which the frames in A are taken. This image is the first frame of the Video S1 (sequence with movement compensation ON). Each frame was divided into a 14×25 grid (each grid element is 13×24 pixels) to perform cross-correlation analysis of pairs of successive images throughout the entire movie. (D) Logarithmic plot of the displacement vector distribution across the image: 70% of the vectors are while 95% are <2.5 µm. (E) Examples of 5 typical traces of objective movement during compensation. (F) Cumulative probability plot of the objective positions across each of the represented traces (individual traces in grey, average trace in black). The mean objective displacement (5–95%) was 32±6 µm.

Mentions: We then substantiated the applicability and robustness of the movement compensation device for in vivo imaging in the adult rat spinal cord subject to breathing movement. Superficial dorsal horn neurons, from exposed spinal cord of anesthetized adult rats, were labeled in vivo by bulk loading of fluorescent dyes. Animals were placed under the laser-scanning video-rate two-photon microscope (Fig. 1A and Materials and Methods). Without any Z movement compensation, the imaging plane changed over time, with cells appearing and disappearing cyclically (Fig. 2A, upper and Video S1). In contrast, with the dynamic focus control activated, the imaging plane remained stable over time allowing gap-free imaging (Fig. 2A, lower and Video S1). The efficiency of the resulting image stabilization is illustrated by the fact that it yielded sharper, less noisy images when averaging over time (Fig. 2A, Average). The reduction in fluorescence fluctuation due to tissue movement is illustrated in figure 2B (upper graph). Next, we performed a quantitative measure of the movement compensation efficiency in in vivo conditions. First, we computed a cross correlation index (Icorr) between the first image of Video S1 (movement compensation OFF) and each of the following images throughout the entire movie. The time profile of Icorr is presented in Fig. 2B (lower graph). Then, for both parts of the graph (with and without movement compensation), Icorr standard deviations (σIcorr/ON and σIcorr/OFF) were computed. We used the expression 1−σIcorr/ON/σIcorr/OFF as a measure of the efficiency of the movement compensation, which was 92% in this example. Yet, the success of movement compensation is highly dependent on the homogeneity of tissue movement. To ensure that compensation is equally efficient over the entire image, we performed the following analysis: we divided each frame of a video (with movement compensation ON) into a grid (Fig. 2C). Then, we cross-correlated each grid element with its equivalent counterpart (in XY coordinates) in pairs of successive frames across the entire movie. We then derived a displacement vector and plotted the distribution of amplitude of these displacement vectors across the image (Fig. 2D). The analysis revealed that >70% of the vectors are and 95% of the vector displacements are <2.5 µm, indicating minimal distortion across the image and that the tissue moved uniformly within the chosen length scale. Finally, to illustrate the amplitude of physiological movements that are typically compensated for, we present 5 typical traces of objective movement during compensation (Fig. 2E). For each trace, we built a cumulative probability plot of the positions of the objective (Fig. 2F, grey traces) and extracted the movement amplitude corresponding to probabilities between 5 and 95%: the averaged objective movement amplitude was 32±6 µm. Gap-free imaging is thus achieved without any measure for tissue stabilization, resulting in significantly improved temporal resolution.


Adaptive movement compensation for in vivo imaging of fast cellular dynamics within a moving tissue.

Laffray S, Pagès S, Dufour H, De Koninck P, De Koninck Y, Côté D - PLoS ONE (2011)

Movement compensation efficiency under in vivo conditions.(A) Imaging sequence sampled from a movie recorded at 30 fps of in vivo spinal lamina I neurons labelled with a structural dye (Calcein). Each image represents the mean of 5 consecutive raw images, reducing the actual sampling rate to 6 fps. Upper and lower rows were respectively acquired when the compensation was OFF and ON. When it is OFF, the time-stack projection (Average) results in an image containing information from planes situated above and below the plane of interest (arrows). When it is ON, the time-stack projection results in an image containing information only from the plane of interest, yielding a highly contrasted image (arrow heads). Scale bar, 10 µm. (B upper graph) Normalized fluorescence intensity time course plotted for one cell in the field of view presented in (a) (dashed ROI) with the system OFF (large intensity fluctuations) and then ON (fluctuations reduced to less than 8% of the initial amplitude). (B lower graph) Portion of the time profile of a cross correlation index Icorr computed between the first image of Video S1 and each of the following images throughout the entire movie. The presented 25 sec portion is centered around zero, time point where the movement compensation device is turned ON. The ratio of Icorr standard deviations with and without movement compensation (92% in this example) was used as a measure of the efficiency of movement compensation in in vivo conditions. (C) Extended field of view from which the frames in A are taken. This image is the first frame of the Video S1 (sequence with movement compensation ON). Each frame was divided into a 14×25 grid (each grid element is 13×24 pixels) to perform cross-correlation analysis of pairs of successive images throughout the entire movie. (D) Logarithmic plot of the displacement vector distribution across the image: 70% of the vectors are  while 95% are <2.5 µm. (E) Examples of 5 typical traces of objective movement during compensation. (F) Cumulative probability plot of the objective positions across each of the represented traces (individual traces in grey, average trace in black). The mean objective displacement (5–95%) was 32±6 µm.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3101223&req=5

pone-0019928-g002: Movement compensation efficiency under in vivo conditions.(A) Imaging sequence sampled from a movie recorded at 30 fps of in vivo spinal lamina I neurons labelled with a structural dye (Calcein). Each image represents the mean of 5 consecutive raw images, reducing the actual sampling rate to 6 fps. Upper and lower rows were respectively acquired when the compensation was OFF and ON. When it is OFF, the time-stack projection (Average) results in an image containing information from planes situated above and below the plane of interest (arrows). When it is ON, the time-stack projection results in an image containing information only from the plane of interest, yielding a highly contrasted image (arrow heads). Scale bar, 10 µm. (B upper graph) Normalized fluorescence intensity time course plotted for one cell in the field of view presented in (a) (dashed ROI) with the system OFF (large intensity fluctuations) and then ON (fluctuations reduced to less than 8% of the initial amplitude). (B lower graph) Portion of the time profile of a cross correlation index Icorr computed between the first image of Video S1 and each of the following images throughout the entire movie. The presented 25 sec portion is centered around zero, time point where the movement compensation device is turned ON. The ratio of Icorr standard deviations with and without movement compensation (92% in this example) was used as a measure of the efficiency of movement compensation in in vivo conditions. (C) Extended field of view from which the frames in A are taken. This image is the first frame of the Video S1 (sequence with movement compensation ON). Each frame was divided into a 14×25 grid (each grid element is 13×24 pixels) to perform cross-correlation analysis of pairs of successive images throughout the entire movie. (D) Logarithmic plot of the displacement vector distribution across the image: 70% of the vectors are while 95% are <2.5 µm. (E) Examples of 5 typical traces of objective movement during compensation. (F) Cumulative probability plot of the objective positions across each of the represented traces (individual traces in grey, average trace in black). The mean objective displacement (5–95%) was 32±6 µm.
Mentions: We then substantiated the applicability and robustness of the movement compensation device for in vivo imaging in the adult rat spinal cord subject to breathing movement. Superficial dorsal horn neurons, from exposed spinal cord of anesthetized adult rats, were labeled in vivo by bulk loading of fluorescent dyes. Animals were placed under the laser-scanning video-rate two-photon microscope (Fig. 1A and Materials and Methods). Without any Z movement compensation, the imaging plane changed over time, with cells appearing and disappearing cyclically (Fig. 2A, upper and Video S1). In contrast, with the dynamic focus control activated, the imaging plane remained stable over time allowing gap-free imaging (Fig. 2A, lower and Video S1). The efficiency of the resulting image stabilization is illustrated by the fact that it yielded sharper, less noisy images when averaging over time (Fig. 2A, Average). The reduction in fluorescence fluctuation due to tissue movement is illustrated in figure 2B (upper graph). Next, we performed a quantitative measure of the movement compensation efficiency in in vivo conditions. First, we computed a cross correlation index (Icorr) between the first image of Video S1 (movement compensation OFF) and each of the following images throughout the entire movie. The time profile of Icorr is presented in Fig. 2B (lower graph). Then, for both parts of the graph (with and without movement compensation), Icorr standard deviations (σIcorr/ON and σIcorr/OFF) were computed. We used the expression 1−σIcorr/ON/σIcorr/OFF as a measure of the efficiency of the movement compensation, which was 92% in this example. Yet, the success of movement compensation is highly dependent on the homogeneity of tissue movement. To ensure that compensation is equally efficient over the entire image, we performed the following analysis: we divided each frame of a video (with movement compensation ON) into a grid (Fig. 2C). Then, we cross-correlated each grid element with its equivalent counterpart (in XY coordinates) in pairs of successive frames across the entire movie. We then derived a displacement vector and plotted the distribution of amplitude of these displacement vectors across the image (Fig. 2D). The analysis revealed that >70% of the vectors are and 95% of the vector displacements are <2.5 µm, indicating minimal distortion across the image and that the tissue moved uniformly within the chosen length scale. Finally, to illustrate the amplitude of physiological movements that are typically compensated for, we present 5 typical traces of objective movement during compensation (Fig. 2E). For each trace, we built a cumulative probability plot of the positions of the objective (Fig. 2F, grey traces) and extracted the movement amplitude corresponding to probabilities between 5 and 95%: the averaged objective movement amplitude was 32±6 µm. Gap-free imaging is thus achieved without any measure for tissue stabilization, resulting in significantly improved temporal resolution.

Bottom Line: We describe a fast, non-contact adaptive movement compensation approach, applicable to rough and weakly reflective surfaces, allowing real-time functional imaging from intrinsically moving tissue in live animals.The strategy involves enslaving the position of the microscope objective to that of the tissue surface in real-time through optical monitoring and a closed feedback loop.The performance of the system allows for efficient image locking even in conditions of random or irregular movements.

View Article: PubMed Central - PubMed

Affiliation: Centre de Recherche Université Laval Robert-Giffard, Université Laval, Québec, Canada.

ABSTRACT
In vivo non-linear optical microscopy has been essential to advance our knowledge of how intact biological systems work. It has been particularly enabling to decipher fast spatiotemporal cellular dynamics in neural networks. The power of the technique stems from its optical sectioning capability that in turn also limits its application to essentially immobile tissue. Only tissue not affected by movement or in which movement can be physically constrained can be imaged fast enough to conduct functional studies at high temporal resolution. Here, we show dynamic two-photon Ca(2+) imaging in the spinal cord of a living rat at millisecond time scale, free of motion artifacts using an optical stabilization system. We describe a fast, non-contact adaptive movement compensation approach, applicable to rough and weakly reflective surfaces, allowing real-time functional imaging from intrinsically moving tissue in live animals. The strategy involves enslaving the position of the microscope objective to that of the tissue surface in real-time through optical monitoring and a closed feedback loop. The performance of the system allows for efficient image locking even in conditions of random or irregular movements.

Show MeSH
Related in: MedlinePlus