We demonstrate a dark-field microscopy method based on Gabor-like filtering to measure subcellular dynamics within single living cells. The technique is sensitive to alterations in the structure of organelles, such as mitochondrial fragmentation.
We demonstrate a microscopic instrument that can measure subcellular texture arising from organelle morphology and organization within unstained living cells. The proposed instrument extends the sensitivity of label-free optical microscopy to nanoscale changes in organelle size and shape and can be used to accelerate the study of the structure-function relationship pertaining to organelle dynamics underlying fundamental biological processes, such as programmed cell death or cellular differentiation. The microscope can be easily implemented on existing microscopy platforms, and can therefore be disseminated to individual laboratories, where scientists can implement and use the proposed methods with unrestricted access.
The proposed technique is able to characterize subcellular structure by observing the cell through two-dimensional optical Gabor filters. These filters can be tuned to sense with nanoscale (10’s of nm) sensitivity, specific morphological attributes pertaining to the size and orientation of non-spherical subcellular organelles. While based on contrast generated by elastic scattering, the technique does not rely on a detailed inverse scattering model or on Mie theory to extract morphometric measurements. This technique is therefore applicable to non-spherical organelles for which a precise theoretical scatter description is not easily given, and provides distinctive morphometric parameters that can be obtained within unstained living cells to assess their function. The technique is advantageous compared with digital image processing in that it operates directly on the object’s field transform rather than the discretized object’s intensity. It does not rely on high image sampling rates and can therefore be used to rapidly screen morphological activity within hundreds of cells at a time, thus greatly facilitating the study of organelle structure beyond individual organelle segmentation and reconstruction by fluorescence confocal microscopy of highly magnified digital images of limited fields of view.
In this demonstration we show data from a marine diatom to illustrate the methodology. We also show preliminary data collected from living cells to give an idea of how the method may be applied in a relevant biological context.
1. Getting the cells ready
2. Getting the optical setup ready
3. Loading the filterbank and using the setup to acquire filtered-background images
4. Plating the cells
5. Conducting the experiment
6. Switching the medium to expose the cells to staurosporine (STS), and maintaining the medium throughout the experiment
7. Representative Results
At the conclusion of the experiment, the collected data will include a large number of filtered images that need to be processed to extract the subcellular structural data. Two examples are shown for an optical filter bank consisting of 9 Gabor-like filters with filter period S=0.95μm, Gaussian envelope standard deviation s=S/2=0.45μm, and orientations Φ=0° to Φ=160° in 20° increments. (See also [1] for more detail).
Example 1: Marine Diatom
We first applied our orientation sensitive filter bank to a marine diatom sample (Carolina Biological Supply Company) with oriented features that were clearly visible in dark-field (DF) imaging (Fig. 1). The optically filtered images are shown alongside the unfiltered image of the sample for comparison.
Figure 1: Dark field (DF) and optically filtered image of marine diatoms. We will analyze the diatom in the lower right of the image (white arrow in the left most panel).
The set of nine Gabor-filtered images of the diatom were processed pixel-by-pixel for object orientation and roundness. Processing consisted of (1) summing measured responses of all nine Gabor-filtered images at each pixel to determine the overall magnitude of the signal response thereby encoding response significance, and (2) finding the Gabor filter orientation, Φ, at which the response is maximized and taking the ratio of this maximal response to the average response for all angles thereby encoding the extent to which objects at each pixel have a preferred orientation. The degree of orientation is closely related to the geometric aspect ratio of the particle. In Fig. 2B, the overall response of the pixel to the filter bank (parameter 1) and the degree of orientation or aspect ratio (parameter 2) are encoded in the color saturation and hue, respectively. An aspect ratio near 1 (blue) is present in areas in which there is no preferred response angle, while greater values (red) indicate areas in which a higher preferred angle response is present. Substructure particle orientation is encoded in a quiver plot (Fig. 2C), where each line closely agreed with the underlying local object orientation visible in unfiltered dark-field (Fig. 2A).
Figure 2: A: Dark field image of diatom. B: Object orientation image. Color scale indicates degree of orientation (aspect ratio) while brightness encodes significance of the total Gabor filter response. C: Orientation of objects with response intensity ≥10% of maximum. Line segment indicates the corresponding structure’s long axis.
Example 2: Apoptotic cells
Here we show filtered images of bovine endothelial cells treated with staurosporine (STS) that were processed in the same way as the diatom. Fig. 3 shows an unfiltered dark-field (DF) image of the cells along with the nine filtered images at time T=-180 min. prior to STS treatment.
Figure 3: Dark field (DF) and optically filtered images of a field containing several living endothelial cells.
The filtered images were subsequently acquired every 20 minutes for a period of three hours after STS treatment. Fig. 4a shows an aspect ratio map of the cells as a function of time. In this case the color hue represents the degree of orientation (labeled orientedness ) as for the color hue in Fig. 2b above. However, the aspect ratio brightness was not weighted by the average filter response. By registering our aspect ratio maps with fluorescence images of the labeled mitochondria in these cells (Fig. 4b), we determined that the measured aspect ratio drop was confined to the cellular regions containing mitochondria and was concomitant with mitochondrial fragmentation which could be observed directly in the fluorescence images of the same cells. Fig. 5 shows time plots depicting the change in aspect ratio as a function of time in cells undergoing apoptosis. Within each cell, there is a drop in aspect ratio at T=60-100 min in the regions that register with fluorescent mitochondria, but not in regions that register with the dim background fluorescence areas.
Figure 4: Aspect ratio (a) and fluorescence (b) images of endothelial cells treated with the apoptosis inducer, staurosporine.
Figure 5: Time plots comparing the decrease in particle aspect ratio (orientedness) in endothelial cells treated with staurosporine. The individual traces represent time plots within single cells. The drop in orientedness is confined to the regions of the cells that register with fluorescent mitochondria (left panel) and is absent from the remaining background fluorescence regions (right panel).
Now that we have determined that the aspect ratio drop corresponds to mitochondrial fragmentation, we can induce apoptosis in these cells, measure the fragmentation using our optical scatter method without having to label the cells, and study the effect of different genetic and experimental conditions on this dynamic.
The method described above yields morphometric maps of the object that may encode particle size or orientation for example. This structural information can be used in several ways:
To date we have shown that the method is sensitive to differences in particle size on the order of 30-50nm [2]. We have shown that the method is sensitive to changes in particle orientation and aspect ratio [3] and to a decrease in particle aspect ratio consistent with apoptosis-induced mitochondrial fragmentation (Figs. 4-5 above).
The results of example 2 suggest that our method permits dynamic measurements of cellular function that can be interpreted in terms of specific organellar function and that can be collected without fluorescence labels or exogenous dyes. However, initial validation of the measured responses against specifically labeled organelles was necessary. Once this initial validation is completed, organellar dynamics may be probed directly with our label-free method.
Applying the method to multiple cellular conditions, and correlating our dynamic structural measurement with the location of the different organelles within the cell, as we did he with mitochondria, can ultimately lead to a library of dynamic structural behaviors that can uniquely characterize specific cellular states (e.g. apoptosis, oxidative and metabolic stress, inflammatory response etc ).. This information could further be incorporated into a “cell state analyzer”, which can be used in a variety of applications including drug discovery in clinical cell analysis.
It is important to note that the method outlined here represents a general approach in which optical scatter data acquired in a microscopic imaging system can be used to extract specific subcellular dynamics. However, the specific instrumentation used can be significantly improved. In particular, the current choice of spatial light modulator for spatial filtering may not be optimal to maximize the efficiency of data acquisition, spatial frequency resolution, and optical signal throughput. Chromatic and geometric aberrations associated with the digital micro-mirror device used here are discussed in [3]. We are currently investigating the potential advantages and drawbacks of a state-of-the-art liquid crystal device in place of the DMD to mitigate these issues. In addition, the device for data acquisition, spatial filtering and microscope control are actuated separately by the human user. This greatly limits acquisition time where a large number of filtered images need to be collected before processing. Thus, automation of the setup is imperative to make the acquisition time commensurate with the duration of the CCD exposure, which is expected to reach 10’s of milliseconds per filtered image for adequate signal-to noise. This increase in temporal resolution will also allow us to more precisely characterize the structural dynamics of organelles within living cells. We are therefore actively working on developing a customized graphic user interface that can unify the control of the hardware and streamline the actuation of its components, including microscope control, CCD image acquisition, and optical filtering at the spatial light modulator.
The authors have nothing to disclose.
The micro-mirror device in this research was funded by Whitaker Foundation grant RG-02-0682 to N. Boustany. Ongoing work is funded by grant NSF- DBI-0852857 to N. Boustany. R.M. Pasternack was partially supported by a Rutgers Presidential Graduate Fellowship. We would also like to thank Dr. E. White for the iBMK cells used in our studies and Dr. D.N. Metaxas for useful discussion regarding optical filtering strategies.
Material Name | Type | Company | Catalogue Number | Comment |
---|---|---|---|---|
DMEM | Invitrogen | Low glucose DMEM | ||
Liebowitz L15 medium | Invitrogen | Without phenol red | ||
L-glutamine | Invitrogen | |||
Mitotracker Green | Invitrogen | |||
Bovine Brain Extract | Clonetics | |||
Fetal Bovine Serum | Gemini Biosciences | |||
Heparin | Sigma | |||
Staurosporine | Sigma | |||
Dymethylsulfoxide | Sigma | |||
Inverted microscope | Carl Zeiss | Axiovert 200M | ||
DMD | Texas Instruments | TI 0.7 XGA DMD 1100 | ||
CCD | Roper Scientific | Cascase 512B | High (16 bit) dynamic range CCD | |
CCD | Roped Scientific | Coolsnap cf |