A quantitative fluorescence-imaging technique for studying acetylcholine receptor turnover at neuromuscular junctions in living animals

Journal

of Neuroscience Methods 64 ( 1996) 199-208

A quantitative fluorescence-imaging technique for studying acetylcholine receptor turnover at neuromuscular junctions in living animals Stephen G. Tumey *, Susan M. Culican, Jeff W. Lichtman Department

vfAnutomy

and

Neurobiology,

Campus

Box

8108,

660

South

Euclid

Avenue.

Washington

Unioersity

School

of Medicine,

St. Louis.

MO

(531 IO.

USA

Received 20 July 1995; revised 26 September 1995; accepted 28 September 1995

Abstract We have developed a technique to measure changes in the amount of fluorescently

labeled acetylcholine

receptors in living muscles

over long time periods.The measurements of fluorescenceare maderelative to a novel, photolytically stablefluorescencestandard (Spectralon)which allows changesin fluorescenceto he followed over days, even months. The method compensates for spatial and temporalvariationsin imagebrightnessdueto the light source, microscope,andcamera.We usethis approachto study the turnover of fluorescentlyla&led acetylcholinereceptorsat a singleneuromuscular junction in a living mouse by re-imaging the same junction in situ over a period of 3 weeks.In addition we showthat the SIT video camera,which is generallyconsideredinadequatefor quantitative imaging (in comparison to CCD cameras), is actually a very good quantitative device, especially in situations requiring both fast acquisition

and high resolution.

Keywords: Quantitative fluorescence microscopy; Microfluorometry; Fluorescence standard; SIT video camera; Neuromuscular junction; Acetylcholine receptor; u-Bungarotoxin; Spectralon .--

1. Introduction One method for studying the distribution of molecules present in small amounts in a biological sample is to tag the molecules with a fluorescent probe. More difficult than

detecting the presence of dye-tagged molecules, however,

or much longer, and changes are likely to occur in the intensity of the light source, the uniformity of illumination, the gain of the photodetector, and the background fluorescence of the specimen. A number of variables must be controlled m achieve

gathering efficiency of the optics. Over a brief interval (a few minutes), these proportions may remain steady enough to permit comparisons between samples. It is more challenging to compare the fluorescence of specimens which cannot be viewed in rapid sequence as occurs, for example, in long-term studies of neuromuscular junctions in

accurate measurements over extended periods. First, the camera gain and black level are adjustable and also are subject to temperature-related drift over time. In addition, the illumination intensity may change with time and temperature. If a lamp must be replaced during a longitudinal study, the intensity of the new lamp may be different. The focus of the light source may also change from one time to the next, altering the spread of illumination both within the specimen plane and in the surrounding volume. These variables make it impossible to simply fix the camera gain and black-level settings and take accurate measurements

living animals (Lichtman et al., 1987; Rich and Lichtman,

over time.

1989; Balice-Gordon and Lichtman, 1993, 1994). In these cases, the interval between comparative views can be days

One also needs to consider the variables that undermine the accuracy of measurements at any given time, in a single image, at any fixed gain and black-level setting. First, the illumination intensity usually is not totally uniform across the field (e.g., it might be higher near the

is estimating their amount. Although the intensity of detected fluorescence is proportional to the amount of fluo-

rescent probe, it is also proportional

to the illumination

intensity, the gain of the photodetector, and the light-

’ Corresponding author. Tel.: (314) 362-2504; Fax: 314-747- 1337; E-mail: [email protected]. 0 165-0270/96/$15.00 0 1996 Elsevier Science B.V. All rights reserved SSDI 01650270(95)00135-2

center of the field). Second, the responsivenessof the light

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sensor is not the same from point to point. This variability is especially pronounced in tube cameras, such as SIT video cameras (e.g., it is usually higher in the center). Finally, the microscope itself may cause non-uniform loss in the light returned from different regions in the specimen plane. Because our own work requires in situ imaging in living animals, we also required a method that calibrates quickly. A video camera is useful in regard to its real-time output; live feedback allows us to locate particular synapses quickly on different occasions, and we can capture an integrated image during the brief interval for which an anesthetized animal’s breathing can be interrupted (van Mier et al., 1994). The SIT video camera used in this study has both real-time output and better spatial resolution than the available alternative video cameras, including intensified CCD video cameras. We describe a microfluorometry system consisting of a fluorescence microscope, a video-intensified camera, and a digital image processor. In essence, we compare the image of a specimen to the image of a standard, which contains an unchanging amount of fluorophore. The amount of fluorophore at each point in the specimen is expressed as a proportion of the amount of fluorophore in the standard. With this system we are able to measure small changes (< 5%) in the amount of fluorescence over time. Finally, we use this method to quantify acetylcholine receptor (AChR) density changes at a neuromuscular junction viewed 5 times over 3 weeks.

2. Materials

and methods

We used an epifluorescence microscope (Leitz; Laborlux D equipped with a 75 W xenon-bulb arc lamp; N2.1 cube: 515-560 nm excitation bandpass and 580 nm emission longpass; 12 cube: 450-490 nm excitation and 5 15 nm emission longpass) and a SIT video camera (Dage-MTI, Michigan City, IN; Series 68). The camera video was fed into a digital image processor (Recognition Concepts, Carson City, NV; Trapix 5500 Series, RCIUTL software library). Application software was written for a custom image processing interpreter (J Voyvodic) on a VAX computer (Digital Equipment, MicroVAX-II, VAX/VMS V6.1). Additional software was written for a numeric computation package (The Mathworks, Natick, MA; Matlab 4.2~) which allowed images to be processed with floating-point precision. The camera was set for automatic black-level control (see Results) and for manual gain (amplifier) and KV (intensifier) control. Neutral density filters (Leica) and a filter slidebar containing a linear variable beamsplitter (Reynard, San Clemente, CA) were used to regulate the illumination intensity. The slidebar was positioned automatically by a custom programmable controller. The transmission values (0- 1) of the filters were measured by placing a photodetector (Newport,

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Irvine, CA; Model 818-SL) of an optical power meter (Model 8 15) under a microscope objective, taking readings for each filter, and dividing them by the reading in the absence of filtration. A highly fluorescent inorganic polymer (Red Spectralon Fluor, Labsphere, North Sutton, NH; CSTM-SFS-225) was used as the standard. The Spectralon Fluor was soft enough to be sliced with a razor. A thin piece was sliced ( < 0.5 mm thick) to minimize out-of-focus fluorescence. The dry wafer was sealed between two coverslips and placed on a compact rotation stage (Newport, Model RSP-1). The fluorescent dye solutions were made using fluorescein labeled dextran (MW: 150 000) and sulphorhodamine 101 (Sigma Chemical, St. Louis, MO). The methods used to visualize the living neuromuscular junctions in situ have been described previously (Rich and Lichtman, 1989; Balice-Gordon and Lichtman, 1994; van Mier et al., 1994). TRITC was conjugated to (Ybungarotoxin (‘rhodaminated’ a-bungarotoxin) using the technique of Ravdin and Axelrod (1977).

3. Results In order to develop a quantitative fluorescence-imaging technique for AChR studies, we have established a procedure to eliminate effects of variables in the microscope system that might change a fluorescence measurement for reasons other than real changes in sample fluorescence. Although this technique can be used with any linear camera, we used a SIT video camera because of its optimum qualities for in vivo imaging: low-light sensitivity, high resolution and intensified response. 3.1. Camera linearity

A critical set of simplifying assumptions can be used if the camera response is linear, that is, the output is a fixed proportion of the incoming light level. CCD cameras are well known linear detectors but for our purposes do not have the combined resolution, responsivity and sensitivity of a SIT video camera. A widespread perception is that the SIT video camera and other tube cameras are not particularly good choices for quantitative imaging; for example, Hiraoka et al. (1987) labeled conventional video cameras as ‘very non-linear’. However, according to the manufacturer’s specifications, the SIT video camera we are using (Dage-MT1 SIT-68) should be linear to within 1.5%, meaning that at any particular gain setting the response at each point on its faceplate should be approximately the same multiple of the number of photons impinging on each of these points. For a linear camera the graph of the signal output (ordinate) versus light input (abscissa) is a straight line with the slope representing the gain and the y-intercept the offset. In SIT video cameras as in CCD cameras, however, the gain is not necessarily the same for each point on the camera faceplate. In SIT video cameras, these

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differences are largely due to intrinsic ‘shading’ non-uniformities of the camera phototube. Furthermore, the offset may also vary from point to point because of variations in thermal noise. As a first step, we tested whether our SIT video camera was indeed linear by measuring its response to known changes in brightness at a few selected points in an image. To do this test, we acquired a series of images of a uniform dye solution (fluoresceinated-dextran, 10 PM) using a set of neutral density filters placed individually in the illumination light path. Pixel noise was reduced by frame averaging (128 frames). The images were divided into 16 blocks. and a pixel was chosen at random from each block (Fig. la). This procedure was repeated for a variety of gain and KV settings. For each setting, at each pixel, the values from the series of images fell on a straight line. In each case, the measured offset (obtained for this experiment by blocking light to the camera) closely matched the predicted y-intercept (obtained by linear regression) (Fig. lb). Thus, the response of the camera at each pixel seemed linear, but as expected the slopes (gains) and y-intercepts (offsets) at different pixels were independent of each other. At high-gain KV settings. we noticed a significant offset due to autofluorescence in the optics. Because in living animal studies the light level needs to be kept low in order to prevent photobleaching and also phototoxicity, we often needed to image at the highest gain setting. Therefore, it was routinely necessary for us to correct images for both an optical offset and the camera offset. A simple way to account for both offsets is to acquire an image of a black object (i.e., an object having no quantum yield) that is being illuminated under the same conditions used for the fluorescent sample. We thus imaged empty space, blocking stray light to the objective by lowering a piece of flat-black tape out of focus. This image is called the dark image. As a second step, we undertook a more rigorous test of linearity to see if we could discern the extent of the non-linear behavior of our SIT video camera. A way to demonstrate the linearity of the response at any pixel is to divide two brightness readings, after subtracting a dark measurement from each. This ratio cancels the effect of the gain (which effects the numerator and denominator equally), resulting in a measure of the fractional change in brightness. Thus. if the numerator is the reading of intensity using a filter and the denominator is the intensity reading without a filter in the illumination light path, then the ratio is the transmission value (O-1) of the filter. In order to test the linearity at every pixel in the camera image, we divided a series of dye solution images at different filter settings by the image of the dye solution at full. unfiltered brightness (first subtracting a dark image, as defined above, from each image). If the camera is linear, the value of every pixel in a ratio result should equal the transmission value of the operative filter. The histogram of Fig. lc shows the distribution of pixel values

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20 I

for each ratio result. From left to right, the transmission values of the corresponding filters are 18.2%, 39.9% and 62.5%. The rightmost distribution represents the ratio of two successive images of a test object under the same illumination and, as such, the mean of the ratio is 100%; the breadth of the ratio is representative of the camera noise (in images averaging 128 frames per image). The breadths of the other distributions are narrow ( u = 1%) and constant in proportion to their means. We found, however, that the arithmetic means show a small systematic error (= 2%; Fig. lc, inset); for example, the distribution for the 39.9% filter has a mean of 42%- and the 62.5% filter has a mean of 64.5%. This slight bowing (integral non-linearity) of the SIT video camera’s input-output transfer function corresponds to a gamma of approximately 0.96 (y = 1.O being perfect linearity). This gamma is considered a fixed property of the photocathode (J. Jones. Dage-MTI, personal communication). Thus, the response of our SIT video camera is nearly linear and empirically within 2% of being true. As shown in Fig. lb, a line through the end points (the black dots) at each pixel defines that pixel’s transfer function. Therefore, the gain and offset values for each point on the faceplate can be obtained by acquiring two images: an image of a brightness reference (e.g., a uniform dye solution) and a dark image. The gain is the difference between the end-point values divided by 100, and the offset is the low end-point value. When we first attempted to take these measurements, we observed that while the gain was comparatively stable. the offset drifted significantly upwards over time, even after an hour of warmup. Although the offset is a result of both electronic offsets (the camera’s black level and the digitizer’s input offset) and optical offsets (autofluorescence in the optics and light leak through the dichroic mirror), we found that the electronic offset of the camera was the source of the drift. This drift is problematic because, as the black level drifts upwards, the useful intrascene dynamic range of the camera is reduced. If the black level is adjusted too low, however, it can fall below the minimum input level of the digitizer, which makes its contribution to the signal level in the camera image ambiguous. To peg the black level safely above zero, we placed a piece of black tape on an edge of the faceplate and enabled automatic black level control of the camera (suggestion of J. Jones, Dage-MTI). In this mode. the video pedestal is locked to the black tape’s signal level. The tape can be on any edge (top, bottom, left or right) and should cover at least 10% of the scan area. This modification reduced the drift by at least an order of magnitude. 3.2. Making

a $‘uore.scence standard

In order to quantify the fluorescence of the labeled acetylcholine receptors, it was necessary to calculate the

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amount of fluorescence in the specimen as a proportion of an unchanging standard. Ideally, the fluorescence of the standard should be uniform so that the gains and offsets of the entire image can be calibrated at once. A dye solution

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is commonly employed for this purpose (Rost, 1991). In practice, however, we have found that variations in fluorescence of a dye solution image precluded its use in standardizing day to day comparisons of small fluores-

Flat-Field image

A Blo

B 255

t

Pixel in Block 11 /

Digitized SIT Camera Response

9

Pixel in Block 1

i”p” o-

c

0 18 40 62 Percent Illumination Intensity

100

8 7 6

1

60 Percent Ratio

60

100

10

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Dark lmaae

IJ =

mean of

‘I - B, ‘2 - B2

Fig. 2. The procedure for deriving the pixel correction factors of the non-uniform fluorescence standard. In the Spectralon image (I, j, the variability of gain in pixel response has 2 sources: the intrinsic fluorescence non-uniformities of the Spectralon and the working of the microscope system.In the image of a uniform volume of dye solution (I,), the fluorescence non-uniformities are assumed to be negligible. Therefore, the ratio of tke Sp+eetr&n image to the dye-solution image (less the offset images, B, and B,) eliminates the only variations that am in common - those due to the ef&cr of the microscope system, leaving only the variations due to the fluorescence non-uniformities of the Spectmlon. Speckling occurs in the region of the btack tape on the left edge of the faceplate because the ratio of low gray values is strongly affected by noise. To make this quotient independent oft&z actual brigMess levels in the Spectralon and dye-solution images, the quotient is divided by its arithmetic mean (F). These correction factors are useful because they allow specimens to appear anywhere in an image even though the standard is non-unifotm.

cence changes. These variations were due to small changes in dye concentaaaion, thickness of the solution, the plane of focus, the focus of the light source relative to the specimen plane, and bleaching. The fluorescence standard we opted for instead was a solid-solid dispersion of a proprietary base material of powdered t&on, called Spectralon, and an inorganic red f&n (a cadmium compound similar to the phosphor in CRT tubes>. The Spectralon is maximally excited at 322 nm, but shows substantial fluorescence with

excitation through at least 528.7 nm. The emission spectrum is also broad with a peak at 543 nm. Because this material is highly fluorescent in the range of all the excitation and emission wavelengths that we regularly use (standard fluorescein and rhodamine filter sets), we could use the same sample to calibrate at each fluorescence wavelength. Additionally, Spectralon is photolytically stable (when dry), that is, its quantum yield and extinction coefficient do not change over time (see below). An

Fig. 1. The SIT video camera response is linear everywhere in an image. a: A series of images of the dye solution were digitized, averaging 128 frames/image. The camera gain and KV settings were midrange (5.0-5.0). A different neutral density filter was inserted ia the ilumhr&on hght path for each image. One image was acquired with each filter. The images were partitioned into 16 blocks. A pixel was selected from each b&k. The piece of black tape on the faceplate is visible on the left edge (see Results). b: The values at 2 pixels (circled in blocks 1 and II) are shown. Each set of values fell on a straight line, although the lines had different slopes and intercepts. c: In order to test the linearity at every pixel in the camera image, we divided each of the images in the series of images of the dye solution by an image of the dye solution without filtration. A dark image (a measurement of the additive offset) was first subtracted from each image. The gain at each point on the faceplate changes negligibly over time. In other words, the gain is effectively constant at each pixel when the.images are acquired. As a result, the ratio of any 2 images (less their dark images) cancels the effect of gain at each pixel. If the camera response is linear, the ratio value at each pixel should be equal to the percent mmmission value of the filter used for the dividend image (18.2, 39.9, 62.5) because the full illumination intensity was used for the divisor image. The range of values (400 X 400 pixels) of each quotient is shown in the histogram. ‘Ihe distribution seen at 100% is the ratio of an image of a fluorescent sample to itself and is indicative of camera noise. The fit11spread of the distributions was made obvious by plotting the logarithm of the number of pixels. The means of the distributions (+ SD) are 18.7 f 0.6, 42.0 & 0.8, 64.5 -I- 1.0, and 99.9 f 1.3. The lengths of the superjacent bars are twice the standard deviations. The camem is not perfectly linear, but has a small integral non-linearity error of approximately 2%.

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alternative stable fluorescence material is uranyl glass (Rigler, 1966). H owever, changes in the focus of the lamp relative to the specimen plane can cause large variations in the intensity, as this material is translucent and thick. Spectralon is much more opaque and can be cut quite thin, making it less susceptible to changes in arc lamp focus. Moreover, uranyl glass is not fluorescent when illuminated with rhodamine-type excitation (550 nm>. The Spectralon material, however, is granular and not uniformly fluorescent. The fluorescence varies by as much as 40% (peak to peak) in the region we used as our standard and by approximately 10% (on average) from region to region. Thus, the only way we could use this substance as a fluorescence standard was to relocate the same physical region for each experiment. Because Spectralon is highly textured, identification of the exact same place on the material was straightforward. A further consideration for our experiments was that we required a calibration procedure which would allow accurate comparisons of a specimen’s fluorescence even if the specimen did not appear at the same position in different

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images. This stipulation was met by having a standard that is uniform across the field. We found that Spectralon could function as a uniform standard despite its having spatially non-uniform fluorescence. In particular, we could measure the fluorescence variations from point to point in a defined region on the Spectralon. Once the variations in fluorescence across this region were known, we could factor them out of subsequent images of the same region. The resulting images of the standard would then possess variations due solely to the action of the microscope system, that is, camera, light source, and optics. In detail, to measure the brightness variations in the Spectralon, we divided an image of the Spectralon (minus its dark image) by an image of a uniformly fluorescent standard (minus its dark image) made from a fluoresceinated-dextran dye solution (0.1 mM) enclosed in a chamber of uniform thickness (Fig. 2). To obtain a dark image which included the additive effects of light leak and autofluorescence in the microscope, a piece of flat-black tape was imaged out of focus under the prevailing illumination and optics. The division of the Spectralon image by

Matrix of Standardized Specimen Fluorescence

\

I, - B,’ I, - B, f

;

l

R,,,, =

l 1

h

G i;-

-

product of transmission values of filters used for Spectralon product of transmission values of filters used for Specimen

Matrix of Pixel Correction Factors (I,) imagescontain variationsdue to fluorescence

RNORM =

Fig. 3. The procedure for producing quantitative images. Both the Spectralon (I,) and specimen non-uniformities and the action of the microscope system. The ratio of these images (minus the dark images, B, and B,) is multiplied by the pixel correction factors (RNORM ) in order to remove the contribution of the static variations in fluorescence of the Spectralon (which is the same as dividing the image of the Spectralon). The particular region on the Spectralon for which the correction factors were derived must be precisely located each time the Spectralon is imaged. The subtractions remove the spatially non-uniform additive offset from each image; separate dark images are acquired because the offset drifts steadily. ?he division cancels the microscope system-induced variations in gain that each image has in common. The abiding variations in the ratio result are due solely to the fluorescence of the specimen. The ratio result is scaled up (divided by f,) and down (multiplied by fi) by the transmission values (O-l) of the filters used when acquiring the images of the specimen and standard, respectively. Measurements (percent of standard) can be taken directly from the final result.

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the dye image eliminated the systematic unevenness in response due to the microscope system. Because the dye solution was uniform, the variations in the quotient were due entirely to the fluorescence non-uniformities of the Spectralon (see Fig. 2). To make the quotient independent of the brightness of the dye solution but relative to the average brightness of the Spectralon, each pixel value of the quotient was normalized to the arithmetic mean of all the pixel values of the quotient. The normalized values were then used as factors to correct the brightness variations in an image of the Spectralon by dividing each pixel value in the image by its own factor. As a result, brightness values which were greater than the mean were scaled down to the mean, and vice versa. Thus, an image of the Spectralon was made which lacked all traces of the intrinsic fluorescence variations of the material. The correction factors obtained by the above procedure were applicable only to the one region on the Spectralon used for calibration and for the particular microscope objective which was used to image it. Thus, the Spectralon had to be imaged by the same objective and in the same place each time we calibrated our specimen to the Spectralon standard. To make the Spectralon region easier to locate, a digitized image of the site was displayed as an overlay on the live camera video. The alignment of the live camera image to the fixed overlay image had to be exact in order to permit proper scaling by the correction factor of each pixel. After roughly aligning to a stored image of the scene (displayed as an overlay), the alignment was finetuned by raking advantage of the fact that, when the Spectraion image was divided by the pixel correction factors, the ratio resembled an image of a uniform standard only when the Spectralon was in its proper position. Thus, the stage position was translated and rotated as necessary to make the Spectralon appear uniformly fluorescent in a display of the real-time ratio of the live camera video and the pixel correction factors. The precise alignment would have been difficult to fine-tune without the benefit of this visual cue. Once the alignment was exact, a frame-averaged image of the Spectralon was digitized and divided by the pixel correction factors. The ratio result appropriately preserved the brightness inhomogeneities due to the camera, light source, and optics. The microscope system remained stable for a period lasting an hour, after which the alignment procedure had to be repeated. 3.3. Measuring

sample fluorescence

Once we had a reliable standard, we could use it to measure the fluorescence of a sample, expressing the fluorescence of a sample as a proportion of the fluorescence of the standard. The fluorescence measurements we took were therefore quoted as a percentage of the standard’s fluorescence rather than in terms of absolute energy. The standardization process was performed in two steps: first, dark images were subtracted from a sample image and an image of the standard (to eliminate the offset), and, sec-

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ond, the difference images were divided (to cancel the gain) (Fig. 3). Fluorescence readings were then taken directly from the ratio result. Given the widely varying brightnesses of our samples, it was often necessary to attenuate the illumination intensity when viewing the Spectralon by using neutral density filters. The ratio result was scaled for the effect of filters accordingly. 3.4. Verijication

of the stability of the standard

Because our measurements were calibrated to the fluorescence of the standard, it was important to confirm the manufacturer’s claim that the Spectralon’s fluorescence did not change over time. As an initial test we attempted to bleach a region of Spectralon by illuminating it for one hour with full intensity rhodamine-type illumination (see methods) using a 75 W xenon bulb and a 50 X ob,jective. The test site was on a piece of Spectralon that was separate from the one we used as the standard. As a control, we attempted to bleach a dried residue of the activity-dependent nerve terminal dye sulphorhodamine (Lichtman et al.. 198.5) in the same manner. Images of the Spectralon test site and the sulphorhodamine were norrr&ized to an image of the Spectralon standard. This test ~~~~~~~ no loss of fluorescence at the Spectralon test site after one hour of illumination (measurement values of 82.80 and 82.97. before and after exposure, respectively). This result was dramatically different from that for the ~lp&hodamine, which lost 47% of its fluorescence (110.93-58.89). As a longer-term test, we measured the fluorescence of a white ceramic substance (from a broken d&h). The ceramic had a signal strength which was compamble to our dimmest samples. As a matter of came, its fhmrescence has been measured regularly before a&d. each inraging session. Over a seven month period, its fluorescence value was constant at 0.028% of the Spectralon standard (a = 0.0007% of the standard). Moreover, this check has been a useful indicator of untoward circumstances which would have otherwise gone undetected, such as an air bubble under the objective, and debris on the objective or cover slip. Thus, we believe the Spectralon’s fluorescence does not change over time because it is highly unlikely that the two different materials (the highly fluorescent Spectralon and the weakly fluorescent ceramic), which were illuminated for different durations and at different light levels, would degrade identically over time. In fact, the only way changes could be occurring in the Spectralon is if the dark and bright areas of the Spectralon are decreasing and increasing proportionally. However, no changes in the relative brightnesses (non-uniformities) across the Spectralon sample have been observed thus far (7 months) in the one region that is used for calibration (4000 pm*). 3.5. Measurement accuracy and precision

To check the accuracy of the technique, we measured the fluorescence of a single test object at two extreme

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camera gain and KV settings. In principle the outcome should be independent of the settings if the system is linear. The gain and KV settings that we chose were at the top and bottom of the adjustable range (10.0 and 10.0, 2.0 and 1.0). These settings correspond to the camera operating near its narrowest and widest input light ranges, respectively. The only non-fading fluorescent material we had which could be detected at the minimum-gain KV setting (the widest input light range) was Spectralon, so we ran our test at a site separate from that used as the standard. Different combinations of neutral density filters were required to match the fluorescence of the Spectralon to the input light range of the camera at the two extreme settings. The net filtration differed by more than three orders of magnitude. Two quantitative images of the test region were acquired: one at each of the camera settings, averaging 128 frames per image. Two blocks (20 pixels/block) were selected for measurement. The mean of the pixel values in each block was computed. The final values in both images at each block were in close agree-

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ment (92.5 and 92.03, 116.28 and 116.88). The accuracy error was 1.5%. Repeated measurements at either camera setting had a precision error of 1%. Thus, the quantitative images of the same fluorescent sample at different camera gain and KV settings produced the same measurement outcomes. As a test of the long-term accuracy of the technique, we regularly measured the fluorescence of a separate piece of Spectralon over a period of 4 months. In that time, the arc lamp bulb was replaced once. Nevertheless, measurements of its fluorescence were able to be held constant within 2.5% (maximum error from a mean value, averaging 128 frames/image). Under normal operating conditions, therefore, the expected deviation of a measurement (the accuracy error) was relatively low, meaning that we can measure changes in fluorescence that are as small as 5%. 3.6. Longitudinal

study in vivo

We have begun to use this technique to follow the turnover of fluorescently labeled acetylcholine receptors at

Fig. 4. Example of a longitudinal study. Acetylcholine receptors in a mouse stemomastoid muscle were labeled with rhodaminated a-bungarotoxin (5 pg/ml for 20 min). The labeled receptors at a single neuromuscular junction were imaged in vivo 5 times over a 22-day period. Photobleaching was mitigated by using neutral density filters and short exposure times (< 10 s). Comparable illumination of futed muscle samples did not cause bleaching. The camera gain and black level were adjusted to maximize the image contrast, making the images appear equally bright, although in reality the fluorescence became progressively dimmer. An intensity profile was taken at a portion of the junction which was in focus in every image (see white lines on receptor images). To determine the density of labeled receptors, we measured the fluorescence intensity from the baseline off of each cross-sectional profile as indicated by the dashed lines. This intensity represents the density of AChRs at the bottom of the primary synaptic gutter which is roughly planar and orthogonal to the optical axis. The rate of decay of receptor density was consistent with an exponential drop (see inset).

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neuromuscular junctions in the sternomastoid muscle of living mice. Fig. 4 shows an example of a neuromuscular junction whose AChRs were labeled once with a saturating dose of rhodaminated a-bungarotoxin, bath applied, several days before the first view. This junction was relocated and imaged 5 times over a period of 22 days. At each timepoint the camera gain was adjusted to provide maximum image contrast. We were thus able to maintain the image signal level despite the progressively decaying fluorescence. Once the gain was set, we acquired images of the junction and the Spectralon, as well as corresponding dark images, to generate a normalized image as described above (see Fig. 3). In this example the fluorescence was measured from an intensity profile along a line crossing a branch of the junction (Fig. 4, white lines). We chose an area on the junction where the length and curvature of the branch did not change significantly from one view to the next. In this way we minimized changes in fluorescence intensity due to changes in the orientation of the synaptic cleft rather than actual changes in AChR number. Additionally we obtained the intensity reading from the position on the profile corresponding to the place where the synaptic cleft is perpendicular to the optical axis and where only one layer of the secondary folds is contributing to the fluorescence in the optical section. Each profile was of course scaled for the filters used during image acquisition. The intensity of the image itself, therefore, does not reflect its absolute value. For example, in Fig. 4 the image at r = 22 days appears to have a greater level and range of intensity than at earlier times. This is a result of the high camera gain, which produced large apparent changes in image intensity from the very small changes in fluorescence intensity at this timepoint. The intensity profiles show a gradual decline in the amount of fluorescence at this neuromuscular junction. The value at each timepoint is expressed as a percentage of the value at time zero. The exponential curve that best fit the data from this junction (see Fig. 4, inset) demonstrated a drop of approximately 10.3%/day (r,,2 = 6.4 days). Comparable results were obtained for other neuromuscular junctions analyzed in the same way.

4. Discussion

This technique was developed to allow studies of receptor turnover and migration at neuromuscular junctions in living mice over time. In previous work, estimates of the time course of AChR loss at neuromuscular junctions in normal adult muscle have been made by analyzing plots of receptor loss from single time-point data (see Salpeter, 1987). In one approach, receptors are labeled in situ with iodinated cY-bungarotoxin (I-BTX). Following some interval the muscle is removed, its end-plate band excised, and the radioactive counts from the piece of tissue are totaled.

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To eliminate counts from non-specific binding and any extrajunctional receptors, counts from a piece of muscle that did not contain junctions were totaled (on a per weight basis) and subtracted proportionally from the number of junctional counts. The difference is an estimate of the total number of AChRs in all of the neuromuscular junctions in the end-plate band. Using this method the time course of AChR loss was established by staggering the times at which mice were killed after applying the radioactive toxin. The whole-muscle method produces results which are subject to some uncertainty due to, for example, variability in the number of junctions per muscle and likely changes in junctional size resulting from growth and other manipulations (Rich and Lichtman, 1989: Balice-Gordon and Lichtman. 1990). Other approaches that circumvent some of these uncertainties include analysis of transmission electron micrographs of I-BTX labeled junctions that have been processed for autoradiography (Shyng and Salpeter, 1989: Salpeter and Harris, 1983) and in vitro studies of release of degraded I-BTX, for studies of denervated material (Bevan and Steinbach, 1983). Our initial results from quantitative in vivo imaging of normal adult muscle are consistent with those obtained by radioactive techniques (Stanley and Drachman, 1983; Loring and Salpeter, 1980; Salpeter and Harris, 1983). In addition, fluorescence imaging measurements allow investigation of questions not easily studied with radioactive probes. Two examples are: (1) studies of variation in receptor maintenance between junctions or between different regions within a junction, and (2) simultaneous study of different populations of receptors in the same muscle using different colored probes. While a calibrated in vivo imaging technique opens up new possibilities for studies of AChR maintenance. a new set of technical issues needs to be addressed. The instrumentation issues which confound quantitative fluorescence imaging are well known, having been documented by many investigators (Cohen and Lesher, 1986; Gross, 1990; Boone et al., 1991; Rost. 1991; Bright, 1993). The algorithm we developed to eliminate the inhomogeneities in the camera response is similar to the one commonly used to produce a uniform flat-field response in CCD cameras. Our algorithm also closely parallels methods of ratio imaging (Bright et al., 1989). Ideally, in any approach to quantitative imaging, variations in camera response must be removed at the level of individual pixels in the digitized images. This level of fine control is necessary because the optics and illumination introduce spatial non-uniformities in the microscope field. Additionally, camera response is not perfectly uniform. A CCD camera is often recommended as the camera of choice for quantitative imaging because its response is linear and also because the variability of its response is systematic and straightforward to correct. The implicit suggestion is that the response of other cameras is not comparably linear. This criticism is often made explicitly

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with respect to conventional video cameras (e.g., Spring and Lowy, 1989; Hiraoka et al., 1987). Yet when it comes to a consideration of SIT video cameras there is little evidence to support this view. In fact, there is evidence to suggest that several tube cameras are indeed linear (e.g., Csorba, 198.5; InouC, 1986). We determined, consistent with this evidence, that our SIT video camera is linear (to within 2%). It is true that the flat-field response of a SIT video camera is grossly non-uniform (i.e., the center is ‘hot’), but the response at each pixel is linear, and the pixel-by-pixel correction, which is already required to compensate for variability of the illumination and optics, also corrects for the non-uniformity in the camera response (such a correction is also necessary for a scientific CCD camera). Thus, in terms of system complexity, it makes no difference whether a linear CCD camera or a SIT video camera (or some other linear camera) is used. The sensor can also be a non-imaging photodetector, such as a photomultiplier tube in a laser-scanning confocal microscope. Although linear detectors are not strictly necessary to acquire quantitative measurements, the calibration of their response is simplest (Baldock and Poole, 1993). In conclusion, we have developed a method to view changes in acetylcholine receptor number over time at the level of the resolution limit of the optical microscope. We have found that a non-uniform stable fluorescent substance (Spectralon) can be used reliably as a fluorescent standard. Finally, the SIT video camera although not generally considered a quantitative imaging device is highly linear and is suitable for quantitative imaging in vivo. Because we can label AChRs with different-colored a-bungarotoxin, we believe this method will be a powerful tool to study a variety of complex issues concerning neurotransmitter receptor regulation. For example, this approach will make it possible to directly study the turnover of newly inserted and pre-existing AChRs at the same junction during development and following denervation.

Acknowledgements We wish to thank Dr. Mark Goldberg, Dr. Robert Wilkinson, ‘Thomas Deckwerth and Lisa Evans for critical comments on the manuscript and Quyen Nguyen for providing the calibrated results of variations in Spectralon fluorescence. This work was supported by grants from the NM (# 50652H) and MDA to J.W.L.

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