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Serial Line Scan Encoding Imaging Cytometer for Both Adherent and Suspended Cells Xin Heng,* Frank Hsiung, Amir Sadri, and Paul Patt Gene Expression Division, Bio-Rad Laboratories, 2000 Alfred Nobel Drive, Hercules, California 94547, United States ABSTRACT: We present a new design of an imaging cytometer for high content bioanalysis, which is equipped with a low-cost linear complementary metal oxide semiconductor (CMOS) imager (running at g40kHz). The fluorescent signals are encoded in a series of line scans across the cellular body, while it streams through a precisely defined line-shaped focus spot. This bioanalysis platform enables the concurrent collection of multiple fluorescence channels, while maintaining both high resolution and excellent throughput (1000 cells/s). We develop several image processing routines for the on-the-fly quantitative analysis of subcellular structures. Finally, we characterize our prototype system by imaging both adherent cells (plate format) and suspended cells (microfluidics format).
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low cytometry and microscopy are probably two of the most widely used optical instruments in life sciences. A flow cytometer1 can perform quantitative multiparametric cellular analysis at a rate of >104 cells per second. Its sorting capability has tremendous usage in situations where one requires rapid separation/purification of a mixed cell population. The field of flow cytometry has witnessed some new developments,2-7 most of which are aimed at simplified design and more affordability. Nevertheless, flow cytometry can only provide the full-body information of individual cells, lacking in the resolving power to identify subcellular structures. On the other hand, modern optical microscopy, especially the emergence of superresolution microscopy,8 has taken a long stride toward sub-100 nm optical resolution, more than capable of distinguishing subcellular organelles. One of its limitations, however, lies in its throughput: these microscopes are not designed to image many cells within one field of view. The imaging cytometer (IC),9-13 the fusion of flow cytometry and microscopy, has recently gained considerable interest in both academia and the biotech industry, with a strong focus on maintaining the strengths of the two, i.e., high throughput and high resolution, and the unprecedented capability to correlate any given data point in a cytometric scatter plot to its corresponding microscope image. Imaging cytometers based on either laser point scanning (often called a laser scanning cytometer14) or white light illumination15 provide continuous frames of target cells. They are excellent systems to study adherent cells grown on a flat substrate16 or suspended cells moving at low speed.12 However, they lack the necessary temporal resolution to capture cells in high flow rate, a prerequisite for high throughput. Amnis (Seattle, WA) commercialized the first imaging flow cytometer17 by the use of a unique camera readout technology, time delay and integration (TDI). Furthermore, several academic laboratories have recently demonstrated various configurations of portable imaging cytometers,18-21 all of which have shown medium-level resolution and a decent throughput (10-100 cells/s per r 2011 American Chemical Society
channel). Although easily integrable with a microfluidic flow cell, the fluorescence version of such microscale cytometers has yet to become fully mature. In this paper, we will introduce a new technology into imaging cytometry, aimed at a simpler design and lower manufacturing cost. By introducing a specially designed line-shaped optical beam as the excitation source, we are able to make more efficient use of the laser power and maximize the throughput with no use of expensive cameras, like TDI-charge coupled device (CCD) and electron multiplying (EM)-CCD, and no sophisticated fluid or velocity control. In addition, the compact arrangement of the optical pathway allows for fast, simultaneous acquisition of multiple fluorescent channels.
’ MATERIALS AND METHODS Imaging Methodology: Serial Line Scan Encoding Imaging Cytometer (SLSE-IC). Conventional flow cytometers uti-
lize a weakly focused laser beam (with a diameter of ∼50 μm1) and a large-area single-pixel photomultiplier tube (PMT; Figure 1a1), thus being incapable of resolving two objects separated by less than a beam diameter. In the context of enhancing optical resolution, it usually requires at least one of the following: a more tightly focused laser spot (like in a confocal laser scanning microscope), a high image magnification, or a multielement detector (e.g., a camera). Our SLSE technology incorporates all of them. Figure 1 compares the SLSE (Figure 1a2) with flow cytometry (Figure 1a1). In the current prototype, we utilize a linear complementary metal oxide semiconductor (CMOS) camera (Basler SPL-2048-140km) and reshape the laser beam into a line (L: 100 μm, W: 0.5 μm) to accommodate both the reduction of Received: September 9, 2010 Accepted: January 4, 2011 Published: February 03, 2011 1587
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Figure 1. (a) Comparison of (a1) a conventional flow cytometer configuration with (a2) SLSE-IC. In (a2), the laser focus is shaped into a line (or elongated ellipse) that scans a slim portion of the cellular body sequentially; (a3) temporal fluorescent signals from four different y locations; (a4) final reconstructed image. (b) Block diagram of the SLSE setup. Two lasers (solid lines) are collimated and then combined by DM2. A cylindrical lens (CL, f = 100 mm) focuses the laser beams in only one direction, thus generating a line shaped focal spot at the object plane. Note that, in the direction perpendicular in paper plane, CL has no effect. The induced fluorescence emissions (dotted lines - - -) of two channels are split by DM5 and then refocused by the acrhomat imaging lens (f = 200 mm) onto the same linear CMOS sensor.
the sensor size in the x direction (coordinates given in Figure 1a1) and to maximize the excitation power. During the transit of the cell across the linearized detection region, the fluorescent emission from subcellular structures are encoded in the line scans across the cellular body (Figure 1a3). Upon the completion of the line scan, a cell image can be readily reconstructed by simply stacking up the individual time traces in y (Figure 1a4). Note that the lateral resolution of this imaging cytometer system is determined by the objective lens in use. SLSE-IC has a few favorable properties. In order to facilitate the following discussion, we assume that the biological cell has a diameter D = 10 μm and is stained with FITC (fluorescein isothiocyanate). The number of fluorophores in the volume of 0.25 μm 0.25 μm 4 μm (projecting onto a single image pixel) is two. The cell’s transport speed is v = 20 mm/sec, and the camera’s integration time is δt = 15 μs. We further assume the system’s overall collection efficiency is 2%,22 meaning that 50 emitted fluorescence photons would eventually generate one electron. The photophysical properties of FITC, when excited at λ = 488 nm, are listed at the bottom of Table 1. The system throughput can be estimated from the above-mentioned imaging conditions, a maximum throughput of 2 103 cells per second, when the samples are able to be maximally packed into a chain inside a focused fluid stream. Such a flow condition has become possible recently, owing to the acoustic focusing technology.23 One major benefit of the SLSE technology is attributed to its more economical usage of the excitation power. Recently, the
high power (g100mW) lasers have become increasingly affordable with the development of solid state photonics. However, higher excitation power does not translate into increased fluorescence signals in proportion, for the reason that the fluorescence excitation-emission cycle is accompanied with parasitic optical processes, namely, the intersystem crossing (e.g., singlet-triplet saturation) and photobleaching.24 These vicious effects set an upper bar on the usable excitation power. Our calculation indicates that severe fluorescence signal saturation would occur above 4 105W/cm2 (see the figure in Table 1). Such empirical optimal excitation value would be further reduced if there exists autofluorescence from endogenous fluorophores.24 In Table 1, we list out the order-of-magnitude estimates (derived from the theoretical formulation24) regarding the detected fluorescence photons at two excitation laser powers: 1 or 100 mW. We include four popular instrument types, namely, wide field microscope based on mercury lamp, laser scanning confocal microscopy (LSCM), flow cytometry, and our SLSE-IC. The existence of singlet-triplet saturation does not allow for the full utility of the 100 mW laser in LSCM, in that the excitation intensity is 100 times of the optimal value. Nearly 87% of the fluorophores are trapped in the triplet state, and the fluorescent signal is almost identical to the case of a 1 mW laser. On the other hand, the weak excitation power used in a wide field microscope cannot generate enough photons within δt = 15 μs integration time. In contrast, SLSE technology nicely incorporates the powerful laser sources by extending its overdue intensity in the 1588
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Table 1. Photon Budget Diagram of Four Fluorescence Detection Systems
y direction, so that the resulting excitation power is close to optimal. Moreover, the excitation intensity can be easily adjusted by modifying the line shape of the optical focus to accommodate different applications, which may require either higher power or longer field of view. A second important feature of SLSE technology is that it is much less affected by mechanical drifts that may occur. During image acquisition, any kind of cell tumbling or rotation would result in a distorted image and deteriorated SNR. When taking an image of a fast moving object, TDI based ICs are functioning by accumulating the photocharges that shift from column to column down the detector. The length of the photon-accumulation zone (Lc) equals the cell diameter (typically 10 μm for signal-tonoise ratio (SNR) concern) or the length of the shift register zone (typically 50-100 μm for image distortion concern).25 Image acquisition would require that cells remain stable during the transit distance equal to this Lc. As a comparison, SLSE is a local acquisition technique, meaning the accumulation length (Lc) is shorter, namely, 1 to 2 image pixels (for SNR concern) or the cell diameter (for distortion concern). Since the focal plane is at the middle of the entire height of the micro channel (H = 100 μm) and the cell diameter (D = 10 μm) is much less than H, the nearwall proximity effects on the object motion26,27 are negligible. Our calculation based upon the motion of a spheroidal cell in a parabolic flow28 finds the maximum rotation ((D/2)θmax) of a cell within one length of Lc is approximately Dhlc/H2 = (10 5 Lc)/(1002) = (Lc/200), where h is the cell’s deviation from the middle plane (i.e., depth of field); for the Gaussian-like focus beam that we use, h is about 5 μm. When this formula was applied, under SLSE operation, the cell rotation would not be more than 5 nm (SNR concern) or 0.05 μm (distortion concern). Both values are much smaller than one image pixel (0.1-0.5 μm), supporting our claim that the likelihood of cell tumbling being a detrimental factor is negligible. As verification,
we will later show several images of suspended HeLa cells in a microfluidic channel without the use of hydrodynamic focusing (Figure 3). We expect that a SLSE imaging flow cytometer would not require expensive mechanical controls or complicated motion recorrection firmware. Furthermore, within the 5 μm depth of field, the velocity fluctuation, which is approximately h2/H2, would be only 1%. It means, for a 10 μm cell, the resulting distortion in the flow direction is about 0.1 μm, smaller than the optical resolution. Thus, its influence on distorting cell morphology and image intensity would also be small. In addition, SLSE due to its local scanning would have better signal uniformity and less speckle noise issue. When a coherent light source was used, such unfavorable effects may occur in both wide field and TDI based high speed imaging platforms. Microfluidic Flow Cell and Its Operation. The microfluidic chip is fabricated using the soft lithography process. The mother mold is made in photoresist SU8 as follows: SU8 of a 100 μm thickness is spun on a silicon wafer and patterned using a mask aligner. The channel width is 200 μm after careful consideration of speed of liquid flow. Polydimethylsiloxane (PDMS) is then cast against the mold to yield an electrometric replica. The PDMS chip with 100 μm channel height is bonded to a polycarbonate substrate, which already has wells for loading and collecting different liquids. The homemade pumping system consists of numerous peristaltic pumps and valves, as well as a proportional-integralderivative (PID) feedback system to provide multioutput airpressure control. The pumping system can adjust the pressure between 0 and 15 psi. The polycarbonate manifold is connected to the pumping system via silicone tubing with a 1 mm internal diameter. When the wells of the polycarbonate substrate are loaded with liquid, they are hermetically sealed by a scotch tape from the top. When the pressure setting was varied, the velocity of the liquid inside the PDMS chip can be precisely controlled. 1589
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The advantage of having wells on the polycarbonate substrate is that liquid is loaded directly into to the PDMS chip which is then isolated from external disturbance, allowing for precise pressure and flow control. Cell Culture and Treatment. The suspended cell sample is HeLa cells, which were first grown in Dulbecco's modified eagle medium (DMEM) media (Gibco, #11965) with 10% fetal bovine serum (FBS), 1 mM glutamate, 1 mM sodium pyruvate, and non-essential amino acids (NEAA) supplements at 37 °C þ 5% CO2. Then, they were trypsinized with Trypsine (Gibco #25300) for 5 min at room temperature, followed by neutralization using growth medium. Cells were collected (centrifuged at 1000 rpm for 5 min), fixed in 4% formaldehyde for 15 min, and permeablized with 0.1% Triton X-100 (CalBiochem #648463) at room temp for 15 min. The fixed cells were then washed with 1 PBS and finally stained with a 1:40 dilution of phalloidin-Alexa555 (Molecular Probe, #34055) at room temp for 15 min. The stained cells were washed and resuspended in 1 PBS for bioanalysis. Dilutions of all reagents were done using 1 PBS. The adherent cell sample is a commercial cell sample slide, FluoCells #6, available from Invitrogen (Calsbad, CA). It contains fixed muntjac fibroblast cells with stained mitochondria (Ex545/Em565) and actin (Ex488/Em520). The nuclei are not imaged with the current prototype system. Image Processing and Analysis. Spectral Linear Unmixing. In a concurrent multichannel platform like a flow cytometer and high content screener, spectral overlap of fluorescent dyes (e.g., green fluorescent protein (GFP) and FITC) with similar emission spectra results in significant bleed-through (spillover) artifacts. Unfortunately, spectral overlap is difficult to eliminate with the already sophisticated optical components (e.g., dichroics and filters). Therefore, without the postmeasurement correction of the image data, the spectral spillover would inevitably add errors into the quantification results. A powerful image processing method, termed spectral linear unmixing, is used as convention to untangling fluorescent labels into their separate channels, thus eliminating the spillover artifacts.29 Using the simplest two-channel scenario as an example, the gray value (chm) at one spatial location (x, y) can be expressed as "
ch1 ch2
#
"
f11 ¼ f21
# " # " # f12 x1 ns1 þ f22 3 x2 ns2
or
CH ¼ FXþNS ð1Þ
where fmn is a predetermined matrix that represents the weight of the emission spectrum of fluorophore n into channel m; xn is weight (contribution) of fluorophore n, which is approximately linearly proportional to the fluorophore’s own characters like local concentration and inherent brightness (i.e., extinction cross section), as well as the excitation intensity; nsi is a noise term. This equation can be generalized for the M-channel mixing situation. The true fluorophore brightness (xn) is the unknown that is to be solved. Note that when F = I, i.e., zero spectral mixing, then CH = X. For a determinable system, the number of equations (detection channels) should be no smaller than the number of the unknowns. A variety of statistical methods, e.g., least-squares estimation in this paper, can be applied to solve this linear algebra problem. In our two-channel situation, ch1 for Alexa488 and ch2 for Alexa555, we have f12 = 0, because of zero spillover of Alexa555 emission into Alexa488 channel. The other fmn components, after normalization, are determined to be f22 = 1, f11 =
0.73, f21 = 1 - f11= 0.27. Note that the existence of background noise would affect the linear unmixing process. Therefore, we subtract the median value of the background from the original image before numerically solving the equations. Cytometric Image Analysis and Data Handling. Unlike a flow cytometer, imaging cytometer data analysis is based on the images, i.e., 2D data arrays. Our Matlab programs include image binarization and segmentation, feature extraction, and measurements on the regional properties. Image binarization is a practical approach for extracting cell features, i.e., separating cells from background. However, due to the usually substantial variation in the fluorescence brightness, i.e., coexistence of very bright and dim objects, a two-level threshold is inadequate. Therefore, we implement Liao’s multilevel threshold algorithm30 (four levels in our cases) and choose the lowest threshold as a binarization gate (see an example in Figure 3b). All the binarized images, together with their grayscale originals, are streamlined into another Matlab subroutine to obtain cell properties like total count, shape, and intensity. All the measurement values are appended to a text file for statistical cell analysis that follows. In the microfluidics experiment, we find that cell clumping rarely occurs; however, for the occasionally appearing doublets and triplets, we are able to split them using a distance-transform based watershed method (see Figure 3c). As a comparison, cell declumping is not possible in the flow cytometer’s data analysis. Inspired by the conventional cytometer’s gating functionality, we develop a multilevel gating scheme that can automatically divide cells into different groups (classes), for example, according to the effective cell diameter or the mean fluorescence intensity. Such a classification is made completely labor-free, meaning the computer program selects its own multilevel gates in a way that the between-class variance is maximized for each class, a basic principle in linear discriminant analysis.
’ RESULTS AND DISCUSSIONS Figure 1b illustrates the SLSE-IC configuration. Two continuous wave (cw) diode-pumped solid-state (DPSS) lasers (TEM00 mode, λ1= 473 nm (100 mW), λ2= 532 nm (50 mW), both from CNI Optoelectronics) are combined by a dichroic mirror (DM2) and focused into a line shape by a cylindrical lens (CL, f = 100 mm) at the entrance pupil of the objective lens. As a result, a line-shaped excitation beam (orthogonal to the original line shape) will be formed at the sample plane. The resulting line focus is along y direction, i.e., perpendicular to the moving direction (xB) of the biology samples. Currently, the prototype system can fit into a box of 50 cm 50 cm 80 cm. The microfluidic device can be readily fixed on the sample stage and connected with a fluid pumping system via 1 mm diameter silicone tubings. The line-shaped excitation beam excites fluorescence emission from the illuminated thin slice of the sample, which would then re-enter the objective lens. The separation of Alexa488 and Alexa555 fluorescence light rays (dotted lines) occurs at DM5 (Semrock Di01-R532), which is then filtered by the respective emission filters. In the end, the tube lens (achromatic doublet, f = 200 mm) refocuses the light rays onto the upper and lower halves of the linear CMOS camera. The image formation procedure has been explained in detail in the Image Methodology: Serial Line Scan Encoding Imaging Cytometer (SLSE-IC) section. Indeed, the two fluorescent channels are imaged at the same time. Note that SLSE-IC setup is nearly unchanged when operating in either 1590
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Figure 2. SLSE-IC for adherent cells. (a) Image of muntjac fibroblast; the dotted lines encircle one example cell, where the mitochondria image shows some spectral bleed-through from the L/actin channel. (b) Work flow of spectral linear unmixing for the original cell images. Images are shown on the same intensity scale. (c) Pseudocolor composite showing subcellular fluorescent components in their natural colors. (d) Detection of mitochondrial mass (Mi) for two separate cells. M1 = 1080, M2 = 1550. (e) Image of the same cell sample under a commercial microscope (Nikon Eclipse Ti with 60, NA = 0.85) and Andor interline CCD. The two-color composite of actin (green) and mitochondria (red) is also shown.
plate format or flow format. The stage can incorporate a microfluidic chip, a cell slide, or a 96-well plate. We first demonstrate SLSE-IC’s utility in adherent cell applications. Figure 2a shows the raw image of muntjac skin cells with stained actin and mitochondria, appearing on a single image frame simultaneously. The offset (in y direction) between these two channels is predetermined as 154 μm. The objective lens is a Nikon plan fluor 60 (NA = 0.85). The cover slide is mounted on a direct current (DC) motor stage (PI M.126.PD2), moving at v = 11.7 mm/sec. The linear CMOS camera is operated in the dual line mode (nominally, a 3 dB SNR improvement over the single line mode), and its integration time is δt = 14.2 μs. This image is an excellent demonstration of the IC prototype’s utility in revealing subcellular structures, with Alexa488 showing a robust labeling of the filamentous actin cables throughout the cells and Alexa555 marking the branching network of mitochondria in the cytoplasm. The nuclei can also be readily identified as the clear voids in the middle. The system throughput in adherent cell application is difficult to estimate, owing to its dependence on cell confluency. However, the current prototype can scan one well of a 96-well plate in 20 s. For drug screening type applications, we expect to improve the yield by another 5-fold through the use of an objective with smaller magnification and acquiring images at lower resolution (1.5 μm). The spectral spillover from actin channel (L) into the mitochondria channel (R) is also apparent, as shown in the encircled cell in Figure 2a. By resorting to the above-mentioned spectral linear unmixing method, we are able to remove the ghost image of actin structures out of the R channel (see Figure 2b). As a bonus, some of the lost photon energy is returned back to the L channel: its brightness is increased by about 20%. More importantly, linear unmixing is able to recover weak fluorescent signals that may have been buried in the spillover signals from neighboring channels, a vital step prior to any quantitative analysis. As a comparison, we also take an image of the same cell sample with a commercial microscope (Nikon Eclipse Ti with the same 60 objective, NA = 0.85) and an Andor interline
CCD (Figure 2e). The images taken under these two systems show a similar depth of focus and resolution. Note that the appearance of nucleus (a red-Cy5 channel) in the microscope image (Figure 2e) is the spectral spillover caused by the broadband white light source, while such an artifact is not present in the images taken by our SLSE system. Mitochondrial mass is a useful characteristic in cell health studies. Figure 2d demonstrates the calculation of mitochondrial mass (Mi) for two separate cells after spectral unmixing. Mi is defined as the total fluorescence intensity surrounded by the mitochondria boundary (light blue lines). Note that mitochondria are presented as discontinuous objects in an image. Therefore, morphological reconstruction operations (e.g., dilation, erosion) are first conducted to form connected components within the same cell. The cell boundaries are drawn on the basis of the treated image, and then, Mi is calculated. In the context of imaging flow cytometry, we also demonstrate the utility of the IC prototype in the microfluidics format. The HeLa cells are treated with the above-mentioned method. The concentration of the trypsinized cell solution is approximately 3 106 per mL. The objective lens is a Nikon plan fluor 40 (NA = 0.6). The integration time of the linear CMOS δt = 25 μs, and the desired image pixel size is 0.25 μm. As a result, the flow speed should be at v = 10 mm/sec. By running the system with spherical fluorescent beads and examining the bead images, we were able to decide that the appropriate setting of the fluid pump should be 0.2 psi. After the flow control system was initiated for 1 min, the fluid velocity was stable during the experiment. Figure 3a presents a few images of such actin-stained cells. For this proof-of-concept experiment, we do not utilize any 3D hydrodynamic focusing, and thus, a large portion of the cells would miss the best focal plane. The current throughput is 60 cells per second by counting the average number of cells in the movie clips. The imaging throughput can be improved drastically with the installation of fluid focusing in future development. It is also worth noting that the acquired images show little distortion caused by flow instability. This corroborates our previous claim 1591
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Figure 3. SLSE-IC for suspended HeLa cells. (a) Example images; bar = 15 μm. (b) Four-level thresholded image and subsequent binarization using the lowest threshold; some background objects are removed in the thresholded image due to their small size. (c) Example of a cell clump (“doublet”) followed by binarization and image segmentation (green lines, using distance transform).
on the advantage of local line scanning, in which the requirement on sophisticated fluid control is much alleviated. In addition, cell images here show a good contrast with the background. The cell periphery shows higher brightness than the internal, which is consistent with the fact that filamentous actin has a predominant cortical localization. To demonstrate multiparameter cell analysis, we apply the multilevel gating function to divide the cell population into three different classes according to the effective cell diameter: (4 area/π)1/2. This discriminant analysis identifies that the threshold levels are 11.9 and 16.3 μm (Figure 4a), and 60.3% of the cell population is between these two levels, which is consistent with our measurement under a microscope. The appearance of very large cells (g20 μm) is mostly due to cell clumps that are not successfully dissociated. The mean cell diameter (D) is 13.22 μm with standard deviation (stdev) = 2.9 μm. The same cell population is measured under two commercial cell counters, Bio-Rad TC10 that gives D1 = 14.0 μm and Invitrogen Countess that gives D2 = 13.5 μm. Note that the circle fitting algorithm used in cell counting usually overestimates the size of noncircular cells. It is clear that both tests match very well with our measurement under the SLSE system. An imaging cytometer, instead, can unravel morphology information with ease, and the side- and forward-scattering channels are no longer necessary. Figure 4b presents a scatter plot based on the mean fluorescence intensity and cell ellipticity. Ellipticity is defined as the ratio of the distance between the foci of the ellipse and its major axis length. The ellipticity value is between 0 (a line) and 1 (a perfect circle). The average ellipticity measured here is 0.72, close to an oval shape, as evident from
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Figure 4. (a) Cytometric histogram of effective cell diameter (mean = 13.22 μm, stdev = 2.9 μm); the two threshold levels divide the cell population into three classes. (b) A scatter plot based on mean fluorescence intensity and cell ellipticity (1 = circle).
Figure 3. Nearly perfect circular cells (ellipticity > 0.9) seldom occur with a percentage of only 4%. The scatter plot also reveals a weak correlation between the plotted parameters (correlation = 0.16). Note that there appears to be a certain level of variation in the mean fluorescence intensity, presumably due to two reasons: (1) each cell had a different amount of filamentous actin at the time the batch was fixed, resulting in each cell taking up different amounts of stain; (2) cells are likely to be positioned at different focal planes, causing some fluorescence to be slightly out of focus. We anticipate the spread of fluorescence intensity to be narrower with future implementation of fluidic focusing control. It is also worth noting the image based cytometers can provide a multidimensional set of parameters, as opposed to only the intensity information given by the flow counterparts. Thus, SLSE-IC can possibly free up several restrictions faced by flow cytometers, especially those that affect the accuracy of intensity levels. For example, shown in Figure 4b, the coefficient of variation based on shape is much smaller than that based on the intensity measurement. In conclusion, we have described a new technology in imaging cytometry, called serial line scan encoding. It makes an efficient use of the available high power lasers and a low-cost linear CMOS imager. Multiple fluorescent colors can be collected concurrently. Owing to rapid localized scanning, the images show little distortion even in an uncontrolled fluidics environment, thus weakening the restraints on expensive fluid control and intricate autofocus control. The demonstrated data analysis workflow is unique for an imaging cytometer, which has a great potential in applications like studying cell development, where apoptosis and 1592
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’ AUTHOR INFORMATION Corresponding Author
*E-mail:
[email protected].
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down. For image distortion concern, the whole cell should keep a stable motion during the photon accumulation time. (26) Staben, M. E.; Zinchenko, A. Z.; Davis, R. H. Phys. Fluids 2003, 15, 1711–1733. (27) Staben, M. E.; Zinchenko, A. Z.; Davis, R. H. J. Fluid Mech. 2006, 553, 187–226. (28) Chwang, A. T. J. Fluid Mech. 1975, 72, 17–34. (29) Zimmermann, T.; Rietdorf, J.; Pepperkok, R. FEBS Lett. 2003, 546, 87–92. (30) Liao, P. S.; Chew, T. S.; Chung, P. C. J. Inf. Sci. Eng. 2001, 17, 713–727.
’ ACKNOWLEDGMENT We are thankful for helpful discussions with Eli Hefner, Robert Archer, Tom McCollum, and Teresa Rubio, all from Bio-Rad. ’ REFERENCES (1) Givan, A. L. Flow Cytometry: First Principles, 2nd ed.; Wiley-Liss: New York, 2001. (2) Eisenstein, M. Nature 2006, 441, 1179–1185. (3) Godin, J.; Lien, V.; Lo, Y. H. Appl. Phys. Lett. 2006, 89, 061106-. (4) Tsai, C. H.; Hou, H. H.; Fu, L. M. Microfluid. Nanofluid. 2008, 5, 827–836. (5) Osborne, G. W. Cytometry, Part A 2010, 77A, 983–989. (6) Vines, A.; McBean, G. J.; Blanco-Fernandez, A. Cytometry, Part A 2010, 77, 1091–1097. (7) Chang, R. BioOptics World 2008, 1, 101–103. (8) Hell, S. W.; Schmidt, R.; Egner, A. Nat. Photonics 2009, 3, 381– 387. (9) Abraham, V. C.; Taylor, D. L.; Haskins, J. R. Trends Biotechnol. 2004, 22, 15–22. (10) Gough, A. H.; Johnston, P. A. Methods Mol. Biol. 2007, 356, 41–61. (11) Elliott, G. S. Comb. Chem. High Throughput Screening 2009, 12, 849–859. (12) Lee, H.; Alt, C.; Pitsillides, C. M.; Puoris’haag, M.; Lin, C. P. Opt. Express 2006, 14, 7789–7800. (13) Oheim, M. Br. J. Pharmacol. 2007, 152, 1–4. (14) Darzynkiewicz, Z.; Bedner, E.; Li, X.; Gorczyca, W.; Melamed, M. R. Exp. Cell Res. 1999, 249, 1–12. (15) Chan, G. K. Y.; Richards, G. R.; Peters, M.; Simpson, P. B. Assay Drug Dev. Technol. 2005, 3, 623–636. (16) Krivacic, R. T.; Ladanyi, A.; Curry, D. N.; Hsieh, H. B.; Kuhn, P.; Bergsrud, D. E.; Kepros, J. F.; Barbera, T.; Ho, M. Y.; Chen, L. B.; Lerner, R. A.; Bruce, R. H. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 10501– 10504. (17) George, T. C.; Basiji, D. A.; Hall, B. E.; Lynch, D. H.; Ortyn, W. E.; Perry, D. J.; Seo, M. J.; Zimmerman, C. A.; Morrissey, P. J. Cytometry, Part A 2004, 59A, 237–245. (18) Heng, X.; Erickson, D.; Baugh, L. R.; Yaqoob, Z.; Sternberg, P. W.; Psaltis, D.; Yang, C. Lab Chip 2006, 6, 1274–1276. (19) Zheng, G. A.; Cui, X. Q.; Yang, C. H. Proc. Natl. Acad. Sci. U.S.A. 2010, 107, 9043–9048. (20) Seo, S.; Su, T. W.; Tseng, D. K.; Erlinger, A.; Ozcan, A. Lab Chip 2009, 9, 777–787. (21) Schonbrun, E.; Abate, A. R.; Steinvurzel, P. E.; Weitz, D. A.; Crozier, K. B. Lab Chip 2010, 10, 852–856. (22) Schmidt, T.; Schutz, G. J.; Baumgartner, W.; Gruber, H. J.; Schindler, H. Proc. Natl. Acad. Sci. U.S.A. 1996, 93, 2926–2929. (23) Goddard, G.; Martin, J. C.; Graves, S. W.; Kaduchak, G. Cytometry, Part A 2006, 69A, 66–74. (24) Pawley, J. B. Handbook of Biological Confocal Microscopy, Second ed.; Plenum Press: New York, 1995. (25) For SNR concern, the subcellular organelle should remain stable during the photon accumulation time. Otherwise, the pixel intensity spills over to neighboring pixels and the intensity level goes 1593
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