Patent Abstract:
A method and system for the imaging and localization of fluorescent markers such as fluorescent proteins or quantum dots within biological samples is disclosed. The use of recombinant genetics technology to insert “reporter” genes into many species is well established. In particular, green fluorescent proteins (GFPs) and their genetically-modified variants ranging from blue to yellow, are easily spliced into many genomes at the sites of genes of interest (GoIs), where the GFPs are expressed with no apparent effect on the functioning of the proteins of interest (PoIs) coded for by the GoIs. One goal of biologists is more precise localization of PoIs within cells. The invention is a method and system for enabling more rapid and precise PoI localization using charged particle beam-induced damage to GFPs. Multiple embodiments of systems for implementing the method are presented, along with an image processing method relatively immune to high statistical noise levels.

Full Description:
The present application is a continuation of U.S. patent application Ser. No. 13/681,746, with a filing date of Nov. 20, 2012, which is a continuation of U.S. patent application Ser. No. 13/017,016, with a filing date of Jan. 30, 2011, all of which are hereby incorporated by reference. 
    
    
     TECHNICAL FIELD OF THE INVENTION 
     The present invention relates generally to focused charged particle beam systems and, in particular, to systems used to excite and localize fluorescent markers in a sample. 
     BACKGROUND OF THE INVENTION 
     Biological research today is increasingly focusing on determining the positions within the cell of various cellular components to ever higher spatial resolutions. This involves many different techniques for enhancing resolution and contrast in images, both for electron microscopes (TEMs, STEMs, SEMs, etc.), as well as all types of light microscopes, including the latest super-resolution techniques. One powerful technique that has gained wide acceptance for research into cellular structure, transport, metabolism, and motility is the application of recombinant genetic techniques to link “reporter” genes to “genes of interest” (GoIs). Thus, when a particular GoI is expressed during normal genetic transcription/translation processes, the reporter gene will also be expressed, producing a small protein which ends up attached to the “protein of interest” (PoI) encoded for by the GoI. One widely-accepted reporter gene is that encoding for a green fluorescent protein (GFP), these reporter genes being available in wild and genetically-modified versions, and the expressed GFPs having fluorescence that extends from blue to yellow in emission wavelengths. The GFP is relatively small (29.6 kDa, 3 nm in diameter by 4 nm long) with its chromophore well protected inside and not requiring any co-factors for light emission. All that is needed to “light up” a GFP is illumination by a laser with a slightly shorter wavelength than the GFP emission wavelength. GFPs appear to be essentially “inert” to the proper functioning of their attached PoI—this is ensured in some cases by connecting the GFP to the PoI with a short flexible polypeptide “linker” which enables the GFP to swing around free from the protein, which may be part of some intracellular structure or mechanism that must not be interfered with in order to preserve the cellular functions being studied by the researcher. 
     Clearly, if the X-Y-Z location of the GFP can be determined precisely within a cell (say, to 10 nm accuracy) then the location of the PoI would be known to a similar accuracy. The fluorescing GFP can be observed through a light microscope and so the location of the PoI can be seen in the microscope relative to observable structures in the sample. Several techniques in the prior art have been proposed and, in some cases, demonstrated, for achieving high positional information from various fluorescent markers (FMs) such as GFPs and also quantum dots. In one technique, a green laser is used to excite a small portion of the fluorescent markers (FMs) in a sample, and the sample is then imaged. Using Gaussian curve fitting, the locations of the FMs may be determined within a FWHM of 20 nm, substantially smaller than the diffraction limit of the imaging system. Using multiple green laser flashes, alternating with red laser flashes which extinguish the fluorescence, the locations of a larger number of FMs may be determined in a process which may typically take tens of minutes. In another technique, described in U.S. Pat. No. 7,317,515 to Buijsse et al. for “Method of Localizing Fluorescent Markers,” which is assigned to the assignee of the present application and which is hereby incorporated by reference, a charged particle beam scans the surface of the sample, damaging the markers and extinguishing the fluorescence when the beam hits the FM. The location of the FM corresponds to the position of the charged particle beam when the fluorescence is extinguished. Because the charged particle beam can be focused to a much smaller point than the laser that illuminates the marker, and the position of the charged particle beam at any time during its scan can be determined with great accuracy, the position of the FM, and therefore the position of the PoI, can be determined with similar accuracy. 
     Throughout all descriptions herein of the present invention, the term GFP will be used to represent the larger class of FMs which can be damaged by a charged particle beam (comprising electrons or ions), including GFPs, organic dyes, as well as inorganic markers such as quantum dots (which may typically be functionalized to enable selective attachment to particular intracellular components such as proteins, nucleic acids, etc.). 
     Many of the prior art methods for localization of FMs within biological samples work only for relatively small numbers of FMs, from which a small subset are activated at any one time—thus imaging times can be many minutes and still suffer from some of the limitations of light optical imaging. Prior art methods employing charged particle beams to selectively damage FMs within samples have utilized image processing methods capable of dealing only with relatively small numbers of FMs—for these methods, the statistical signal-to-noise ratio limits their application to FMs which do not inherently appear in large densities. For GFPs, in particular, this may be a hindrance, since any type of expressible tag (as opposed to a functionalized tag such as a quantum dot) can be created in very large numbers through normal cellular process of gene transcription to mRNA, followed by translation to proteins (GoI+linker+GFP). Thus, there is a need for a fast method for localization of very large numbers (≧10000) of FMs such as GFPs within cells, or sections of cells, in time frames, for example, of the order of a minute. 
     SUMMARY OF THE INVENTION 
     An object of the invention is to provide an improved method and apparatus for locating proteins of interest in a sample. 
     A preferred embodiment includes a charged particle apparatus and method capable of imaging samples containing fluorescent markers (FMs), such as green fluorescent proteins (GFPs) or quantum dots, using standard electron microscopic signals such as secondary electrons (SEs) or transmitted electrons (unscattered, elastically-scattered, and/or inelastically-scattered), while simultaneously exciting the FMs with a laser and collecting emitted light from the excited FMs. 
     One embodiment comprises a detector optics configuration which presents a very large collection solid angle for both secondary electrons and emitted light, without interference between the two types of detectors which would tend to reduce the respective collection solid angles for both SEs and light. 
     Some embodiments of the present invention comprise an exemplary image processing method potentially enabling larger (e.g., &gt;10000) numbers of FMs to be simultaneously localized (during a single imaging scan of roughly a minute duration) than was possible with simpler image processing schemes in the prior art. 
     The foregoing has outlined rather broadly the features and technical advantages of the present invention in order that the detailed description of the invention that follows may be better understood. Additional features and advantages of the invention will be described hereinafter. It should be appreciated by those skilled in the art that the conception and specific embodiments disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes of the present invention. It should also be realized by those skilled in the art that such equivalent constructions do not depart from the spirit and scope of the invention as set forth in the appended claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more thorough understanding of the present invention, and advantages thereof, reference is now made to the following descriptions taken in conjunction with the accompanying drawings, in which: 
         FIG. 1  shows a schematic diagram of a protein of interest (PoI), with a green fluorescent protein (GFP) attached by a linker. 
         FIG. 2  is a schematic diagram of an X-Y scan raster, illustrating pixels containing GFPs and pixels without GFPs. 
         FIG. 3  is a schematic diagram of a first embodiment of the present invention comprising detector optics above the sample. 
         FIG. 4A  is a side view of both light and secondary electron trajectories for the detector optics in  FIG. 3 . 
         FIG. 4B  is a cutaway isometric view of the detector optics in  FIGS. 3 and 4A . 
         FIG. 5  is a schematic diagram of a second embodiment of the present invention comprising detector optics below the sample. 
         FIG. 6  is a schematic diagram of a third embodiment of the present invention comprising detector optics both above and below the sample. 
         FIG. 7  is a graph of the signal as a function of the time during a raster scan for a scan field containing 100 GFPs. 
         FIG. 8  is a graph showing a close-up of the beginning of the graph in  FIG. 7 , showing damage to the first eight GFPs out of the total of 100. 
         FIG. 9  is a graph of the signal as a function of the time during a raster scan for a scan field containing 10000 GFPs. 
         FIG. 10  is a graph showing a close-up of the beginning of the graph in  FIG. 9 , showing damage to the first eight GFPs out of the total of 10000. 
         FIG. 11  is a graph showing a raw signal with statistical noise and a smoothed signal as a function of the time during a raster scan, showing damage to the first three GFPs out of a total of 100. 
         FIG. 12  is a graph showing the smoothing function and the derivative function. 
         FIG. 13  is a graph showing the raw signal with statistical noise, the smoothed signal, and the smoothed derivative as a function of the time during a raster scan, showing damage to the first three GFPs out of a total of 100. 
         FIG. 14  is a graph showing a raw signal with statistical noise and a smoothed signal as a function of the time during a raster scan, showing damage to the first three GFPs out of a total of 10000. 
         FIG. 15  is a graph showing the raw signal with statistical noise, the smoothed signal, and the smoothed derivative as a function of the time during a raster scan, showing damage to the first three GFPs out of a total of 10000. 
         FIG. 16  is a histogram showing the performance of an image processing method for locating GFPs within the scan signal in the presence of large amounts of statistical noise during the detection of the first three GFPs out of a total of 10000 GFPs in the scan field. 
         FIG. 17  is a graph of the optimization results for the image processing method. 
         FIG. 18  is a flow chart for the method of the present invention for localizing expressible tags such as GFPs within a sample. 
         FIG. 19  is a flow chart for the method of the present invention for localizing functionalized tags such as quantum dots within a sample. 
         FIG. 20  is a schematic diagram of a combined secondary electron and fluorescent marker image. 
         FIG. 21  is a flow chart for an image processing method for localizing fluorescent markers. 
     
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     Some embodiments of the present invention provide charged particle systems comprising detector optics systems optimized for very high collection efficiencies for both secondary electrons and light simultaneously, without spatial interference between the two types of detectors. This is accomplished in some embodiments by at least one mirror, preferably a paraboloidal mirror, above and/or below the sample, such that the point on the sample surface impacted by the charged particle beam is at or in proximity to the focal point of the paraboloid(s) (either one or two). By the focal point being “in proximity to” the charged particle is meant that the area illuminated by the light reflected from the mirror includes, and is larger than, the area impacted by the charged particle beam. 
     In addition, a conducting surface, typically metallic, of the paraboloidal light mirror is electrically biased (typically a few hundred volts negative) to provide an electric field between the sample and the mirror to deflect secondary electrons so that they do not impact the mirror and to reflect the secondary electrons to a detector. Thus, both the photons and secondary electrons (SEs) emitted from the sample may be collected into large solid angles, preferably greater than π/4 steradians, more preferably greater than π/2 steradians, and even more preferably greater than π steradians, providing efficiencies (and resultant higher signal-to-noise ratios) previously unattainable for detector systems in which the light and SE solid angles are spatially separated and mutually interfere. The secondary electron detector is preferably positioned below the point at which the charged particle beam exits mirror  314  in  FIGS. 3, 4A and 4B . 
     In addition to these highly efficient light detection systems, issues of statistical (stochastic) noise in the light signal are addressed in some embodiments. This noise arises because the imaging mode of the present invention utilizes selective damage of single FMs such as GFPs due to the energetic charged particle (electron or ion) beam to localize each FM. Damaging a single FM results in an incremental loss in total light emission from the sample, e.g., if one FM out of a total of 10000 FMs is damaged, then the emitted light will decrease by roughly 0.01%. To detect such a small decrease in light emission, it is necessary that the stochastic noise due to fluctuations in light emission averaged over the pixel dwell time not be substantially larger than 0.01% in this example. Similar methods have been described in the prior art for smaller numbers of FMs, as in U.S. Pat. No. 7,317,515, assigned to the assignee of the present invention. In all these cases, the numbers of FMs which could be localized were limited. 
     The benefits of this improved signal-to-noise ratio in the light optical signal arising due to fluorescent emission from markers (such as GFPs, organic dyes, or quantum dots) in the sample are further amplified by another aspect of the present invention—an image processing method enabling the timing (and thus the locations) of FM damage events to be determined, even in the case of very large stochastic noise in the raw imaging signal. 
     Fluorescent Markers as Expressible Tags 
       FIG. 1  shows a schematic diagram  100  of a protein of interest (PoI)  102 , with a green fluorescent protein (GFP)  104  attached by a linker peptide  110 . A typical GFP has a diameter  106  of 3 nm and a length  108  of 4 nm—details of the “beta barrel” structure of the GFP  104  are omitted here. In general, the PoI  102  will be larger than the GFP  104 , as shown. One key consideration in the use of expressible tags is that the tag does not interfere with the proper functioning of the PoI  102  within the cell, whether that function is metabolic, transport, structural, etc. Thus, a short peptide linker  110  is often used to attach the fairly rigid GFP  104  to the PoI  102 , enabling the GFP  104  to swing around on an arc  112  of radius  114 , as shown. Note that this radius  114  determines the maximum precision at which the GFP  104  can be located since GFP  104  is free to occupy any position on circle  112 . Thus, it is likely that locating the GFP  104  to within 5 to 8 nm is sufficient for any position-determining methodology, as in the present invention, as well as in the prior art such as described in U.S. Pat. No. 7,317,515 assigned to the assignee of the present invention and incorporated herein by reference. 
     Methods for recombinantly-linking genes for reporter genes, such as for various versions of the “green fluorescent proteins” (GFPs) originating from the hydrozoan jellyfish species  Aequorea victoria  are well known. Since the original discovery of GFPs in the 1950s, a number of genetic variants have been developed with improved fluorescence spectra spanning an emission range from blue to yellow light, with simplified spectral absorption distributions. GFPs are relatively small cylinders (“beta barrels”, 3 nm diameter by 4 nm long) comprising 238 amino acids (26.9 kDa), which appear to be essentially “inert” to the overall cellular mechanisms of species which can be far removed from jellyfish. Because of this, the use of GFPs is widely accepted in the biological community. What is particularly important is that the wide acceptance of GFPs as expressible markers makes the present invention potentially highly useful for the biological community in applications where current GFP localization methodologies are insufficiently precise. Throughout the descriptions of the operation of the three embodiments below, the GFPs should be understood to comprise a multiplicity of GFP variants, representing multiple recombinant reporter genes being expressed simultaneously in the biological samples being examined. Similarly, the light detectors in the three embodiments should be understood to comprise multiple detectors operating independently, and in parallel, each detecting light from a particular GFP type within the multiplicity of GFP types in the sample. 
     The present invention is applicable both to cases where a smaller number (10 to several 100) of GFPs are within the imaging field of view, as well as cases where there are many more (up to at least 10000) GFPs. The improved image processing method of the invention is applicable to both cases. Each GFP variant has its own unique spectral absorption and emission characteristics. An example is for the original “wild-type” GFPs (wtGFP) which has an absorption peak at 395 nm and an emission peak at 509 nm. Because the wtGFP has an undesirable second absorption peak at 475 nm, efforts were made to develop improved versions, such as the S65T mutation, having an absorption peak at 484 nm and an emission peak at 507 nm, with no secondary absorption peak. A key aspect in employing GFPs as expressible tags is that they may be present in very high numbers within the sample, necessitating the efficient light detection and image processing of the present invention. 
     Imaging Methodology for Fluorescent Markers in a Charged Particle System 
       FIG. 2  is a schematic diagram of a 32×32 X-Y scan raster  200 , illustrating pixels containing GFPs  208  and pixels without GFPs  206 . The fast scan axis  202  is along the X-direction, while the slow scan axis  204  is along the Y-direction. For normal raster scanning, the beam would first be positioned at the upper left and then moved to the upper right along the top row. Next, the beam would “retrace” back to the left side and move down one row, followed by scanning horizontally to the right again. This process is repeated until all 32×32=1024 pixels have been imaged. In the example here, white pixels  206  represent those not containing a GFP, while black pixels  208  contain a single GFP. It is assumed that the GFP density is low enough that Poisson statistics apply and we can make the approximation that no pixels contain more than one GFP. Since the GFPs are expressible tags representing the locations within a cell (or slice of a cell) of a particular Protein of Interest (PoI), the distribution of GFPs will often be non-uniform, representing the non-uniform distribution of PoIs due to their required locations within the cell for proper functioning. 
     Three exemplary system configurations are presented below: a first configuration which is applicable to thick samples and collects all signals from the front surface of the sample (i.e., operating in SEM mode); a second configuration with the detector optics for electrons and light below the sample (i.e., operating in a TEM or STEM mode); and a third configuration with a combined detector system both above and below the sample to give the maximum possible collection efficiency for light emitted from excited FMs within the sample. 
     First Embodiment 
     Detector Optics Above the Sample 
       FIG. 3  is a schematic diagram of a first embodiment  300  of the present invention comprising detector optics above the sample  306 . Sample  306  may include a biological sample including fluorescent markers that are expressed by genes linked to genes of interest or including inorganic markers that selectively attach to particular intracellular components. Sample  306  may also include a biological sample including dyes or other inorganic markers, such as quantum dots, that are functionalized to enable selective attachment to particular intracellular components. Sample  306  sits on a sample stage at a sample position that defines a sample plane. 
     A charged particle column  302 , such as an electron beam column or a focused ion beam column, generates a beam  304  of charged particles that is focused by column  302  onto the surface of a sample  306  at a location  308 . Electrons in the beam typically have energies of between 1,000 eV and 25,000 eV. Ions typically have energies of between 5,000 eV and 50,000 eV. An X-Y beam deflector  310 , which may comprise magnetic coils, electrostatic multipoles, or a combination of both magnetic coils and electrostatic multipoles, is configured to move the beam  304  around on the surface of the sample  306 , typically in an X-Y raster pattern for imaging, as in  FIG. 2 . 
     In this first embodiment  300  of the invention, the sample  306  is assumed to be thick enough to prevent penetration of the beam  304  through the sample  306 , thus all imaging signals (both light and charged particles) are collected above the sample surface as shown. A paraboloidal mirror  314  is positioned between the deflector  310  and the sample  306 . A hole  312  in mirror  314  allows passage of beam  304  downwards to the sample  306 . Below the mirror  314  is a flat shield plate  316 , typically biased to the same voltage as the sample  306 . In order to achieve maximum collection efficiency for light, mirror  314  is configured to subtend the largest possible solid angle, preferably greater than π steradians, at location  308 . Then the maximum possible amount of light emitted from the fluorescent markers (FMs) at location  308  will be collected and transmitted through the beam splitter  326 , then through color filter  372 , and finally to detector  360 —this maximizes the achievable signal-to-noise ratio in the optical signal. 
     The fluorescent markers (FMs) are excited by light  324  from laser  322 , which is emitted upwards, reflected first off beam splitter  326  and then reflected and focused by the paraboloidal mirror  314  onto the surface of sample  306  at location  308 . Note that it is desirable to have the largest possible transmission of emitted light from the FMs through beam splitter  326  in order to increase the amount of light reaching detector  360 . If the transmission of beam splitter  326  is 50%, then half the signal light  327  from location  308  will get to the detector  360 , and half of the excitation light  324  from laser  322  will reach location  308 . Thus, to focus 3 mW from laser  322  onto location  308  would require a 6 mW laser output  324  (ignoring other reflective losses). If ample laser power is available, it may be preferred to increase the transmission (and thus reduce the reflectivity) of beam splitter  326 , for example to 80%. Then 80% of the light from location  308  (again, ignoring reflective losses) will pass through the beam splitter  326 , while only 20% of the light  324  from laser  322  will reach location  308 —thus, to focus 3 mW at location  308 , a 15 mW laser output power  324  would be required (12 mW would pass through beam splitter  326 , to be absorbed in a beam dump (not shown) above splitter  326 ). In this embodiment, the light from laser  322  is reflected by mirror  314  onto the top surface of sample  306 , that is, the light does not first pass through sample  306  before being reflected by mirror  314  and the light from the source illuminates the sample initially from above the sample. Similarly, light emitted by fluorescent markers within sample  306  are emitted through the top surface of sample  306 , collected by mirror  314  above sample  306 , and reflected to light detector  360  without passing completely through sample  306  and being collected on the opposite side of the mirror, as in U.S. Pat. No. 7,317,515. 
     The second function of paraboloidal mirror  314  is to provide a conductor that can be electrically biased to provide an electric field that prevents secondary electrons from impacting mirror  314  by reflecting secondary electrons (SEs)  332  emitted from location  308  due to the impact of the primary charged particle beam  304 . This is shown in more detail in  FIGS. 4A and 4B . SEs  332  are deflected by a several hundred volt negative potential applied to the (conducting) mirror surface. Note that the SEs do not reflect the same way that the light  328  does, because the SEs are reflected by the electrostatic field created by the voltage applied to the mirror  314  or other conductor, and this field extends throughout the entire volume of the paraboloidal mirror  314  (see  FIG. 4B  for an isometric view of mirror  314 ). The SEs  332  are deflected toward a detector  320  and collected by the detector  320  as shown to the side of the sample  306 . Thus, both the light and secondary electrons are collected from location  308  with high efficiencies since there is no conflict between the collection solid angles for light and SEs. The size of hole  312  is preferably kept to a minimum to reduce both loss in light reflection and any perturbations to the electrostatic field deflecting the secondary electrons. The focal point of the paraboloidal mirror  314  is approximately at location  308  on the surface of sample  306 —thus light emitted from the vicinity of location  308  will be focused into roughly parallel light beams  328 , directed towards the right of  FIG. 3 . While it is preferred that the electric field that directs the SEs away from the mirror be produced by the conductive mirror, the electric field can be produced by a conductor that is separate from the mirror. An electrical bias can also be applied to the entrance of the charged particle detector  320 . 
     A system controller  362  is electrically connected to column  302  through cable  370 , to X-Y deflector  310  through cable  368 , to mirror  314  through cable  366 , to shield plate  316  through cable  376 , to sample  306  through cable  378 , to SE detector  320  through cable  380 , and to laser  322  through cable  374 . The system controller  362  coordinates the scanning of beam  304  by the X-Y deflector  310  with the display of an image on a monitor (not shown), as well as performing the image processing calculations described below to locate FMs on the sample surface. 
     Charged particle beams typically must travel in a vacuum, thus a vacuum enclosure  334  contains the exit of column  302 , X-Y deflector  310 , mirror  314 , shield plate  316 , and sample  306 , as shown. Typically, it is much easier to locate as much of the light optical instrumentation outside the vacuum as possible, thus a viewport  356  allows the light  327  from laser  322  (reflected off beam splitter  326 ) to pass into enclosure  334 , while the light emitted from FMs at location  308  is allowed to pass out from enclosure  334 , through beam splitter  326 , then through color filter  372  and into detector  360 . Color filter  372  serves to reduce the amount of laser excitation light  324  which can pass into detector  360 . Since the excitation light always has a shorter wavelength than the emitted light from the FMs, it is possible to tune the passband of filter  372  to transmit most of the light from the FMs, while blocking most of the laser light. In some cases, additional light filtering may take place within detector  360 . Electrical feedthrough  354  allows the passage of cables  366 ,  368  and  370  into and out of enclosure  334 , while feedthrough  352  allows the passage of cables  376 ,  378  and  380  into and out of enclosure  334 . 
     Detector Optics for High Efficiency Collection of Light and Secondary Electrons 
       FIG. 4A  is a side cross-sectional view  400  generated using the SIMION ray-tracing program showing both light and secondary electron trajectories for the detector optics in  FIG. 3 . The primary beam  304  can be seen passing downwards through hole  312  in the paraboloidal mirror  314 . Impact of beam  304  with sample  306  at location  308  induces the emission of secondary electrons  332  into a cosine (Lambert Law) distribution. Shield plate  316  and sample  306  generally have the same voltage applied by system controller  362  (see  FIG. 3 ). Several hundred volts negative bias is applied to the conductive mirror surface  314  to repel the (0 to 50 eV) secondary electrons  332  as shown. This reflection differs from that of the light reflecting specularly off mirror  314 , thus the SEs are collected on detector  320  to the side of sample  306 . The collection solid angle at location  308  is very high, preferably greater than π steradians, in this configuration, giving a good signal-to-noise SE image. Light emitted from the fluorescent markers (FMs) in the sample  306  is also emitted into a cosine distribution, a large fraction of which is directed towards mirror  314 , as shown. Since location  308  on sample  306  is the focal point of paraboloidal mirror  314 , light  328  reflecting off mirror  314  is generally parallel passing to the right of the  FIG. 4A . It will be understood that the benefits of the mirror  314  can be used in other applications in which light is directed toward a sample or detected from a sample in a charged particle beam system. Such systems that would benefit from mirror  314  include systems that collect light for an optical microscope that is coaxial with a charged particle beam, such as the system described in U.S. Pat. No. 6,373,070 to Rasmussen for “Method apparatus for a coaxial optical microscope with focused ion beam,” and systems that collect light from photo luminescence caused by the charged particle beam, or luminescence. 
       FIG. 4B  is a cutaway isometric view of the detector optics in  FIG. 4A , also generated using SIMION. In addition, the beam splitter  326  is shown at the lower right. The elliptical pattern of SE  332  impacts at detector  320  can be seen, thus the area of detector  320  need not be excessively large—smaller detector areas may increase the detector bandwidth (at least for solid-state detectors) and thus are generally preferred. 
     Second Embodiment 
     Detector Optics Below the Sample 
       FIG. 5  is a schematic diagram of a second embodiment  500  of the present invention comprising detector optics below a sample  506 . A charged particle column  502 , such as an electron beam column or a focused ion beam column, generates a beam  504  of charged particles which is focused by column  502  onto the surface of the sample  506  at a location  508 . Beam  504  typically includes electrons having energies between about 50 keV and 300 keV. An X-Y beam deflector  510 , which may comprise magnetic coils, electrostatic multipoles, or a combination of both magnetic coils and electrostatic multipoles, is configured to move the beam  504  around on the surface of the sample  506 , typically in an X-Y raster pattern for imaging. In this second embodiment  500  of the invention, the sample  506  is assumed to be thin enough to permit penetration of the beam  504  through the sample  506 , thus all imaging signals (both light and charged particles) are collected below the sample surface as shown. A paraboloidal mirror  580  is positioned below the sample  506 . A hole  582  in mirror  580  allows the travel of transmitted charged particle beam  584  downwards after passage through sample  506 . Beam  584  may typically comprise unscattered particles from the primary beam  504 , elastically-scattered particles, inelastically-scattered particles, secondary electrons and/or ions, and particles which have scattered both elastically and inelastically in the sample  506 . After passing through hole  582 , beam  584  enters detector  586  which may comprise energy filters to differentiate between transmitted particles of the various types cited above, and possibly multiple detectors operating in parallel. 
     In order to achieve maximum collection efficiency for light, mirror  580  is configured to subtend the largest possible solid angle (typically &gt;π steradians) at location  508 . Thus, the maximum possible amount of light emitted from the fluorescent markers (FMs) at location  508  will be collected and transmitted through beam splitter  596 , then through color filter  598 , and finally to light detector  590 —this maximizes the achievable signal-to-noise ratio in the optical signal. The FMs are excited by light  594  from laser  522 , which is emitted upwards, reflected first off beam splitter  596  and then reflected and focused by paraboloidal mirror  580  through sample  506  at location  508 . Note that it is desirable to have the largest possible transmission of light through beam splitter  596  in order to increase the amount of light reaching detector  590 —the same percentage transmission considerations apply here as for  FIG. 3 , above. It is important that the size of hole  582  be kept to a minimum to reduce loss in light reflection, while remaining large enough to accommodate the elastically-scattered electrons within beam  584 . The focal point of the paraboloid  580  is at approximately location  508  on sample  506 —thus light emitted from the vicinity of location  508  will be focused into roughly parallel light beams  587 , directed towards the right of the figure. 
     A system controller  562  is electrically connected to column  502  through cable  570 , to X-Y deflector  510  through cable  568 , to sample  506  through cable  566 , to laser  522  through cable  574 , to detector  590  through cable  564 , and to detector  586  through cable  588 . System controller  562  coordinates the scanning of beam  504  by X-Y deflector  510  with the display of an image on a monitor (not shown), as well as performing the image processing calculations described below to locate FMs on the sample surface. 
     Charged particle beams typically must travel in a vacuum, thus a vacuum enclosure  534  contains the exit of column  502 , X-Y deflector  510 , sample  506 , mirror  580 , and detector  586 , as shown. Viewport  556  allows the light  587  from laser  522  (reflected off beam splitter  596 ) to pass into enclosure  534 , while the light emitted from FMs at location  508  is allowed to pass out of enclosure  534 , through beam splitter  596 , then through color filter  598 , and into detector  590 . Color filter  598  serves to reduce the amount of laser excitation light  594  which can pass into detector  590 , as for the first embodiment in  FIG. 3 . The same reflectivity considerations apply here for beam splitter  596  as for beam splitter  326  in  FIG. 3 . Electrical feedthrough  554  allows the passage of cables  566 ,  568  and  570  into and out of enclosure  534 , while feedthrough  552  allows the passage of cable  588  into and out of enclosure  534 . 
     Third Embodiment 
     Detector Optics Both Above and Below the Sample 
       FIG. 6  is a schematic diagram of a third embodiment  600  of the present invention comprising detector optics both above and below the sample  606 . A charged particle column  602 , such as an electron beam column or a focused ion beam column, generates a beam  604  of charged particles which is focused by column  602  onto the surface of a sample  606  at a location  608 . An X-Y beam deflector  610 , which may comprise magnetic coils, electrostatic multipoles, or a combination of both magnetic coils and electrostatic multipoles, is configured to move the beam  604  around on the surface of the sample  606 , typically in an X-Y raster pattern for imaging. In this third embodiment  600  of the invention, the sample  606  is assumed to be thin enough to permit penetration of the beam  604  through the sample  606 . To achieve larger collection efficiencies for light, two paraboloidal mirrors  614  and  680  are positioned above and below the sample  606 , respectively. A hole  612  in mirror  614  allows passage of beam  604  to the sample  606 . A hole  682  in mirror  680  allows passage of transmitted charged particle beam  684  downwards after passage through sample  606 . Beam  684  may typically comprise unscattered particles from primary beam  604 , elastically-scattered particles, inelastically-scattered particles, secondary electrons and/or ions, and particles which have scattered both elastically and inelastically in sample  606 . After passing through hole  682 , beam  684  enters detector  686  which may comprise energy filters to differentiate between transmitted particles of the various types cited above, and possibly multiple detectors operating in parallel. The considerations for collection of SEs  632  emitted from location  608  due to the impact of primary beam  604  into detector  620  are the same as in  FIGS. 3, 4A and 4B . 
     In order to achieve maximum collection efficiency for light, both mirrors  614  and  680  are configured to subtend the largest possible solid angles (typically &gt;π steradians for each of mirrors  614  and  680 , giving a total &gt;2π steradians) at location  608 . The maximum possible amount of upwards-emitted light emitted from the fluorescent markers (FMs) at location  608  will be collected and transmitted through the beam splitter  626 , then through color filter  672 , and into detector  660 —this maximizes the achievable signal-to-noise ratio in the optical signal. Similarly, the maximum possible amount of downwards-emitted light from the FMs at location  608  will be collected and transmitted through color filter  698  and then to detector  690 . The FMs are excited by light  624  from laser  622 , which is emitted upwards, reflected first off beam splitter  626  and then reflected and focused by paraboloidal mirror  614  onto sample  606  at location  608 . Note that it is desirable to have the largest possible transmission of light through beam splitter  626  in order to increase the amount of light reaching detector  660 —the same percentage transmission considerations apply here as for  FIGS. 3 and 5 , above. It is important that the size of holes  612  and  682  be kept to a minimum to reduce loss in light reflection. The focal points of paraboloids  614  and  680  are at approximately location  608  on sample  606 —thus light emitted from the vicinity of location  608  will be focused into roughly parallel light beams  628  and  687 , respectively, directed towards the right of the figure. 
     A system controller  662  is electrically connected to column  602  through cable  670 , to X-Y deflector  610  through cable  668 , to mirror  614  through cable  666 , to detectors  660  and  690  through cable  664 , to shield plate  616  through cable  676 , to sample  606  through cable  678 , to detector  686  through cable  688 , and to laser  622  through cable  674 . Detectors  690  and  660  are shown interconnected through cable  699 , however, an alternative configuration would have separate cables to system controller  662 . System controller  662  coordinates the scanning of beam  604  by the X-Y deflector  610  with the display of an image on a monitor (not shown), as well as performing the image processing calculations described below to locate FMs on the sample surface. 
     Charged particle beams typically must travel in a vacuum, thus a vacuum enclosure  634  contains the exit of column  602 , X-Y deflector  610 , mirror  614 , shield plate  616 , sample  506 , mirror  680 , and detector  686 , as shown. It is much easier to locate as much of the light optical instrumentation outside the vacuum as possible, thus a viewport  656  allows the light  624  from laser  622  (reflected off beam splitter  626 ) to pass into enclosure  634 , while the upwards-emitted light from FMs at location  608  is allowed to pass out of enclosure  634 , through beam splitter  626 , then through color filter  672  and into detector  660 . The downwards-emitted light from FMs at location  608  passes out of enclosure  634  through viewport  656 , through color filter  698 , and then into detector  690 . Color filters  672  and  698  serve to reduce the amount of laser excitation light  624  which can pass into detectors  660  and  690 , respectively, as for the first embodiment in  FIGS. 3 and 5 . Electrical feedthrough  654  allows the passage of cables  666 ,  668  and  670  into and out of enclosure  334 , while feedthrough  652  allows the passage of cables  676 ,  678 ,  688 , and  680  into and out of enclosure  634 . Note that in this dual paraboloidal mirror configuration, light from laser  622  which passes through sample  606  unabsorbed will reflect off mirror  680  towards detector  690 —thus color filter  698  must be configured to withstand a potentially high level of laser illumination, higher than would be the case in  FIGS. 3 and 5 . 
     Imaging of Smaller Numbers of Fluorescent Markers in the Scan Field 
       FIG. 7  is a graph  700  of the signal  704  (number of photons collected per pixel) as a function of the time  702  during a raster scan for a scan field containing 100 GFPs. The overall scan time is 60 s, distributed over 512×512 (256 k) pixels, with a pixel dwell time of 229 μs. Curve  706  represents the number of photons collected per pixel for all the undamaged GFPs within the illuminated area. At the far left, all 100 GFPs are assumed to be emitting light in response to laser excitation. As curve  706  descends towards the lower right, the number of damaged GFPs is gradually increasing from 0 to 100, with eventually all GFPs damaged at the end of the 60 s raster. Because the GFPs are randomly located, curve  706  has some irregularities while following an overall descent from 0 s to 60 s. The laser power of 3 mW is distributed over a 28 μm 2  area at the sample—in this example, the raster is assumed to have this same area, thus at the end of the scan, no GFPs remain undamaged. In general, the illuminated area may be larger than the raster, thus some GFPs would remain undamaged at the end of the scan at 60 s. 
       FIG. 8  is a graph  800  showing a close-up of the beginning of the graph  700  in  FIG. 7 , showing damage to the first eight GFPs out of the total of 100. The most difficult point in the localization of the GFPs within the area illuminated by the laser is at the beginning when there is the maximum number of GFPs emitting (and the minimum number of GFPs already damaged). This is because with the largest number of undamaged GFPs emitting light, the statistical fluctuations in the total collected light from all GFPs will be the largest (calculated as the square root of the number of photons collected in the pixel dwell time). Graphs  700  and  800  were made with the assumptions listed in Table I. Curve  806  represents the mean number of photons collected from all the undamaged GFPs in the illuminated area as a function of time into the scan—only the first 8 s are shown, during which time eight GFPs are struck and damaged by the charged particle beam (electrons or ions). Each of these damage events is represented by a vertical drop in the signal, such as drop  812  at the upper left. Above curve  806  is the +3 σ curve  808  (long dashes), representing expected signal fluctuations three standard deviations above the mean signal level  806 —a relatively unlikely event. Similarly, below curve  806  is the −3 σ curve  810  (short dashes), representing expected signal fluctuations three standard deviations below the mean signal level  806 —also a relatively unlikely event. The key thing to note here is that at jump  812 , representing the loss (due to damage) of one GFP, curve  810  at the left of jump  812  is well above curve  808  at the right of jump  812 —in other words, it is extremely unlikely that the inherent statistical signal-to-noise arising from the number of photons collected from all the undamaged GFPs will make it difficult to detect a single GFP damage event, in the case where there are only 100 GFPs being illuminated (and thus emitting) within the laser focused area. 
                                   TABLE I               Assumptions for Graphs 700 and 800 in FIGS. 7 and 8.                                Total Imaging time   60.00   s       Image dimension   512   # pixels/side       Total # pixels   262144       Pixel time   228.9   us       Laser power at substrate   0.0030   W = J/s       Wavelength   550.00   nm       Energy/photon   2.25   eV = (in J)       Incident photons/s   8.306E+15   /s       Diameter of illuminated area   6.00   um       Area of illuminated area   28.27   um{circumflex over ( )}2       Diameter of GFP   3.00   nm       Area of GFP   7.07   nm{circumflex over ( )}2       Incident photons/s/GFP   2.077E+09   /s       quantum efficiency estimate   0.50       collection efficiency estimate   0.25       photons collected/s/GFP   2.596E+08   /s/GFP       photons collected/pixel/GFP   59410.21       statistical fluctuation in #photons/pixel/GFP   243.74       Number of GFP in illuminated area   100       photons collected/pixel time/ilium. area   5.941E+06       statistical fluctuation in #photons/illum. area   2437.42       Signal/Noise estimate   24.37                    
Imaging of Larger Numbers of Fluorescent Markers in the Scan Field
 
       FIG. 9  is a graph  900  of the signal  904  (number of photons collected per pixel) as a function of the time  902  during a raster scan for a scan field containing 10000 GFPs. The overall scan time is 60 s, distributed over 512×512 (256 k) pixels, with a pixel dwell time of 229 μs, as in  FIG. 7 . Curve  906  represents the number of photons collected per pixel for all the undamaged GFPs within the illuminated area. At the far left, all 10000 GFPs are assumed to be emitting light in response to laser excitation. As curve  906  descends almost linearly towards the lower right, the number of damaged GFPs is gradually increasing from 0 to 10000, with eventually all GFPs damaged at the end of the 60 s raster. Because 10000 is such a large number, even though the GFPs were randomly distributed in the field of view, curve  906  is approximately a straight line. The laser power of 3 mW is distributed over a 28 μm 2  area at the sample—in this example, the raster is assumed to have this same area, thus at the end of the scan, no GFPs remain undamaged. In general, the illuminated area may be larger than the scan raster, thus some GFPs would remain undamaged at the end of the raster. 
       FIG. 10  is a graph  1000  showing a close-up of the beginning of the graph  900  in  FIG. 9 , showing damage to the first eight GFPs out of the total of 10000. As was the case for graph  700  in  FIG. 7 , the most difficult point in the localization of the GFPs within the area illuminated by the laser is at the beginning when there is the maximum number of GFPs emitting (and the minimum number of GFPs already damaged). Graphs  900  and  1000  represent a hundred times more GFPs in the area illuminated by the laser than was the case in  FIGS. 7 and 8 —thus the total light collected (see the three alternative detector geometries in  FIGS. 3, 5, and 6 ) will be a hundred times higher, with √100=10 times higher absolute statistical fluctuations. Since the light emitted by a single GFP is independent of the total number of illuminated GFPs, this means that the change in total light collected (from all the undamaged GFPs) whenever a single GFP is damaged by the charged particle beam will be 10 times smaller in comparison with the statistical fluctuations than was the case for 100 GFPs total ( FIGS. 7 and 8 ). This can be seen from the qualitative differences between graphs  800  and  1000 . 
     Graphs  900  and  1000  were made with the assumptions listed in Table II. Curve  1006  represents the mean number of photons collected from all the undamaged GFPs in the illuminated area as a function of time into the scan—only the first 0.09 s are shown, during which time eight GFPs are struck and damaged by the charged particle beam (electrons or ions). Each of these damage events is represented by a vertical drop in the signal, such as drop  1012  at the upper left. Above curve  1006  is the +3 σ curve  1008  (long dashes), representing expected signal fluctuations three standard deviations above the mean signal level  1006 —a relatively unlikely event. Similarly, below curve  1006  is the −3 σ curve  1010  (short dashes), representing expected signal fluctuations three standard deviations below the mean signal level  1006 —also a relatively unlikely event. The key thing to note here is that at the jump  1012 , representing the loss (due to damage) of one GFP, curve  1010  at the left of jump  1012  is now below curve  1008  at the right of jump  1012 —this situation differs qualitatively from that shown in  FIG. 8  where there was no overlap. Although ±3 σ is a fairly stringent criterion, it is clear that distinguishing individual GFP damage events from out of the overall statistical noise in the light signal (such as from detector  360  in  FIG. 3 ) will be more difficult in this case. 
                                   TABLE II               Assumptions for Graphs 900 and 1000 in FIGS. 9 and 10.                                Total Imaging time   60.00   s       Image dimension   512   # pixels/side       Total # pixels   262144       Pixel time   228.9   us       Laser power at substrate   0.0030   W = J/s       Wavelength   550.00   nm       Energy/photon   2.25   eV=       Incident photons/s   8.306E+15   /s       Diameter of illuminated area   6.00   um       Area of illuminated area   28.27   um{circumflex over ( )}2       Diameter of GFP   3.00   nm       Area of GFP   7.07   nm{circumflex over ( )}2       Incident photons/s/GFP   2.077E+09   /s       quantum efficiency estimate   0.50       collection efficiency estimate   0.25       photons collected/s/GFP   2.596E+08   /s/Qdot       photons collected/pixel/GFP   59410.21       statistical fluctuation in #photons/pixel/GFP   243.74       Number of GFP in illuminated area   10000       photons collected/pixel time/ilium. area   5.941E+08       statistical fluctuation in #photons/illum. area   24374.21       Signal/Noise estimate   2.44                    
Image Processing to Improve FM Localization for Smaller Numbers of FMs
 
     In this section, we will examine further the localization of fluorescent markers (FMs) such as green fluorescent proteins (GFPs), as first discussed in  FIGS. 7 and 8 , above, for the case of 100 GFPs in the laser illumination area.  FIG. 11  is a graph  1100  showing a raw signal  1106  with statistical noise and a smoothed signal  1108  as a function of the time  1102  during a raster scan, showing damage to the first three GFPs out of the total of 100. Both curves  1106  and  1108  are plotted against a vertical axis  1104  representing the numbers of photons collected from all GFPs per pixel. The noise is assumed to be entirely stochastic, i.e., fluctuations in the signals per pixel will have a standard deviation equal to the square root of the number of photons collected during the pixel time, in this example, 229 μs. With only 100 GFPs being illuminated by the laser, as was discussed for  FIG. 8 , curves  808  and  810  were close to the mean number of photons curve  806 , meaning that for very few pixels will there be enough noise to make it hard to distinguish a GFP damage event. This is further illustrated here, where the small plus and minus signal noise fluctuations cause no problems is locating the GFP damage events at  1110 ,  1112 , and  1114 . An image processing method comprising a smoothing step, followed by a differentiation step, is illustrated in  FIGS. 11-13 . In  FIG. 11 , curve  1108  is a smoothed version of the raw data curve  1106 —the downward steps at each of the three GFP damage events  1110 ,  1112 , and  1114  can clearly be seen. The smoothing function (kernel)  1206  is shown in  FIG. 12 . 
       FIG. 12  is a graph  1200  showing a Gaussian smoothing function  1206  centered at  1208  and the derivative function  1210  centered at  1212 , plotted against the time  1202  from the center (i.e., the particular pixel data being smoothed) in units of pixels (229 μs dwell time in this example)—the vertical axis  1204  is the values of the two functions (unitless). The sum of the 13 weights (solid squares) in curve  1206  is 1.000, with a maximum value at the center of approximately 0.11. Although in this embodiment, a Gaussian smoothing function  1206  is shown, other smoothing functions are also within the scope of the invention, including, but not limited to, binomial distributions and bell curves. After the raw signal data has been convolved or combined with curve  1206 , the resultant smoothed data, such as curve  1108  in  FIG. 11 , is then autocorrelated with a second, “derivative function” curve  1210 , which is the derivative of curve  1206  in this example. Although in this embodiment, curve  1210  is the derivative of a Gaussian curve, other types of “derivative function” curves are possible, including, but not limited to, the derivatives of binomial distributions or bell curves. The full-width half-maximum (FWHM) of Gaussian curve  1206  is a parameter to be optimized, as discussed in  FIG. 16 , below, and is 10.0 pixels in this example. A simplification of this process would be to first convolve curves  1206  and  1210 , which is allowed since both convolution and autocorrelation are associative, and then convolve this resultant curve with the raw image data. Curves  1206  and  1210  are kept separate here to clarify the process. 
       FIG. 13  is a graph  1300  showing the raw signal  1106  and the smoothed signal  1108  (both from  FIG. 11 ), and the smoothed derivative  1306  as a function of the time  1302  during a raster scan, showing damage to the first three GFPs out of the total of 100. The vertical axis  1304  at the left is for curves  1106  and  1108  in units of photons per pixel from all undamaged GFPs, while the vertical axis  1305  at the right is for the derivative  1306 , also in units of the numbers of photons per pixel from all undamaged GFPs. The derivative curve  1306  has three deep downward-going peaks: a first at  1310  corresponding to GFP damage event  1110 , a second at  1312  corresponding to GFP damage event  1112 , and a third at  1314  corresponding to GFP damage event  1114 —note the excellent locational agreement along the time axis. Thus, for small numbers of GFPs being excited by the laser, the image processing routine can easily locate GFP damage events from the raw imaging signal  1106 , as shown. The threshold line  1320  defines the maximum height for peaks in the derivative curve  1306  which are counted as GFP damage events. There are thus two parameters in the image processing method of the invention: the FWHM of the smoothing curve (such as curve  1206  in  FIG. 12 ), and the threshold value  1320 . Choices for these two parameters are discussed in  FIG. 17 , below. 
     Image Processing to Improve FM Localization for Larger Numbers of FMs 
     In this section, we will examine further the localization of fluorescent markers (FMs) such as green fluorescent proteins (GFPs), as first discussed in  FIGS. 9 and 10 , above, for the case of a hundred times as many GFPs (i.e., now 10000) in the laser illumination area.  FIG. 14  for the 10000 GFP case corresponds to  FIG. 11  for the 100 GFP case—graph  1400  shows a raw signal  1406  with statistical noise and a smoothed signal  1408  as a function of the time  1402  during a raster scan, showing damage to the first three GFPs out of the total of 10000. Both graphs are plotted against a vertical axis  1404  representing the numbers of photons collected from all undamaged GFPs per pixel. As for the 100 GFP example, the noise is assumed to be entirely stochastic, i.e., fluctuations in the signals per pixel will have a standard deviation equal to the square root of the number of photons collected during the pixel time, in this example, 229 μs. With such a large number of GFPs being illuminated by the laser, as was discussed for  FIG. 10 , curves  1008  and  1010  were much farther from the mean number of photons curve  1006 , meaning that it may potentially be difficult to distinguish individual GFP damage events from the general noise background—for this reason, the image processing method discussed herein was developed. This method is exemplary and is included here to illustrate that, with sufficient image processing of the proper type, the locations of most GFPs, even from a large number within a sample, should be fairly accurate, thus extending the techniques first described in U.S. Pat. No. 7,317,515 to the much higher fluorescent marker densities which may be typical for expressible tags such as GFPs. The same image processing routine illustrated in  FIGS. 11-13  was used here. In  FIG. 14 , curve  1408  is a smoothed version of the raw data curve  1406 , calculated using a smoothing curve  1206  having a FWHM of 10.0 pixels—the exact locations of the downward steps at each of the three GFP damage events  1410 ,  1412 ,  1414  are difficult to see in the raw data curve  1406 . The smoothing function (kernel)  1206  is shown in  FIG. 12 , generating the smoothed curve  1408 , in which the GFP damage events are more apparent. 
       FIG. 15  is a graph  1500  showing the raw signal  1406  and the smoothed signal  1408  (both from  FIG. 14 ), and the smoothed derivative  1506  as a function of the time  1502  during a raster scan, showing damage to the first three GFPs out of the total of 10000. The vertical axis  1504  at the left is for curves  1406  and  1408  in units of photons per pixel, while the vertical axis at the right  1505  is for the derivative, also in units of the numbers of photons per pixel. The derivative curve  1506  has three deep downward-going peaks: a first at  1510  corresponding to GFP damage event  1410 , a second at  1512  corresponding to GFP damage event  1412 , and a third at  1514  corresponding to GFP damage event  1414 —note the excellent agreement, in spite of the relatively noisy raw signal data  1406  in this example, compared with curve  1106  in  FIG. 11 . The threshold line  1520  defines the maximum height for peaks in the derivative curve  1506  which are counted as GFP damage events (compare with threshold  1320  in  FIG. 13 ). There are thus two parameters in the image processing method of the invention for 10000 GFPs, as for 100 GFPs: the FWHM of the smoothing curve (such as curve  1206  in  FIG. 12 ), and the threshold value  1520 . Choices for these two parameters are discussed in  FIG. 17 , below. Thus, for larger numbers of GFPs being illuminated by the laser, the image processing routine can still locate GFP damage events by processing the raw imaging signal  1406 , as shown. 
     Optimization of the Image processing Method 
     We now discuss the optimal choice of FWHM and threshold parameters for the image processing method. This analysis is for exemplary purposes only since it uses simulated noisy data—for actual experimental data, the FWHM and threshold values may be determined empirically from samples with known quantities of FMs (using a regular array of quantum dots or GFPs, for example) by adjusting the FWHM and threshold values to make the detected number of FMs match the actual number of FMs.  FIG. 16  is a histogram  1600  showing the performance of an image processing method for locating the first three GFPs in the presence of large amounts of statistical noise and large numbers of GFPs (10000) being illuminated in the scan field. For an example in which there are exactly three GFPs (as in  FIGS. 11, 13-15 ), histogram  1600  shows that for a FWHM of 10.0 pixels and a threshold of −3625, that 94% of the time  1608 , the routine will locate exactly the correct number of transitions, with no false positives (i.e., extraneous GFPs) and no false negatives (i.e., no missed GFPs). In 3% of the cases  1606 , one out of the three GFPs is missed, while in another 3% of the cases  1610 , an extraneous GFP is recorded (3 actual+1 extraneous=4 total). 
       FIG. 17  is a graph  1700  of the optimization results for the image processing method, illustrating the GFPs found  1706  as a function of the FWHM (in pixels)  1702 . The left axis  1704  corresponds to curve  1706  in percent. Curve  1708  illustrates the percentage of false negatives (i.e., the missed GFPs) using axis  1704  magnified by 10×. The sum of curves  1706  and  1708  always equals 100%. Curve  1710  illustrates the percentage of false positives (i.e., extraneous GFP locations not corresponding to real GFPs), also using axis  1704  magnified by 10×. The right axis  1720  corresponds to the curve  1712  of the optimized threshold level for the derivative (i.e., the values for line  1320  in  FIG. 13  and line  1520  in  FIG. 15 ). An extensive series of modeling calculations was performed, varying both the FWHM and threshold to determine the optimum values to maximize the level of curve  1706  while reducing and equalizing the percentages of false negatives and false positives. The results are shown in Table III, below. The threshold curve  1712  continues to rise as the FWHM is increased—this is intuitively reasonable, since clearly as the amount of smoothing is increased (with larger FWHM values), the peaks in the derivative will be “blunted” and will not extend as far downwards, requiring smaller thresholds (i.e., higher on the graph) to avoid cutting off those peaks which correspond to actual GFPs. 
     The four columns in Table III listing the “% of Times Each Number of GFPs Detected” show that in all cases, either two, three or four peaks were detected (never more or less), although in all cases the correct number of peaks was three. When two peaks were detected, it was found that both locations corresponded to actual GFPs, but the peak for the third GFP location did not extend below the threshold and was lost. Thus, for FWHM=6.0 pixels, a 9.50% rate of detection of two peaks corresponds to (9.50%)/3=3.17% rate of false negatives (i.e., missed GFPs), and a (9.50%) 2/3=6.37% rate of correctly detecting GFPs, which adds to the 78.25% rate of detecting the correct number of GFPs (at the correct locations). Similarly, when four peaks were detected, it was found that three locations corresponded to actual GFPs, but an additional peak due to smoothed noise fell below the threshold and was counted as an extraneous GFP. Thus, the 12.25% rate of detecting four peaks corresponds to (12.25%)/4=3.06% rate of false positives, and a (12.25%) 3/4=9.19% rate of correctly detecting GFPs, which adds to the 78.25% rate. Thus the total percent of GFPs found correctly is: 3.17%+78.25%+9.19%=96.83%, as shown in Table III. 
     From this analysis, it appears that a FWHM of 10 pixels with a threshold of −3625 provides a good balance of a minimum number of false positives (0.75%) and false negatives (1.00%), while giving a high rate (99%) of correct GFP localization. In general, it is preferable to use the smallest possible FWHM for smoothing, subject to the constraint of minimizing the error rate, since larger FWHM values may cause the loss of data in the rare cases where GFPs are very close together along the scan line (i.e., only a few pixels apart)—thus a FWHM of 10.0 pixels was chosen, instead of a FWHM of 13.0 pixels which would give the same error rate. Hundreds of simulations with random noise have shown surprising consistency in the results shown in  FIG. 17  and Table III. Clearly, the optimum value for the FWHM may be a function of various characteristics of the image. It is expected that this optimization process will be integral to the overall charged particle beam system used to acquire the raw imaging signal and to perform subsequent image processing to produce the final image containing the coordinates of the GFPs in the sample. For actual biological samples, with variations in light emittance from GFPs, and many other issues, theoretical errors rates as demonstrated here are almost certainly more than adequate. 
     Flowchart of Method for Localizing Expressible Tags Such as GFPs 
       FIG. 18  is a flow chart  1800  for the method of the present invention for localizing expressible tags such as GFPs within a biological sample. In block  1802 , the reporter gene for GFP is attached to the regulatory sequence of a particular gene of interest (GoI) in an animal, plant or cell culture which is the subject of research interest, thus whenever the GoI is expressed within the cell (consistent with the cell&#39;s need for the protein encoded for by that GoI), the GFP (and the peptide linker, if present) will also be expressed and will remain attached to the protein of interest (PoI). In block  1804 , the cell is allowed to express the GFP genes (producing the PoI+linker+GFP amino acid sequence, with the normal secondary, tertiary, and possibly quaternary structures for the PoI). The sample is then prepared for charged particle microscopy in block  1806  in a manner familiar to those skilled in the art—since the GFPs are typical proteins, no special treatment should be necessary to preserve the optical emission properties of the GFPs within the sample. In parallel with blocks  1802 - 1806 , in block  1808 , a charged particle beam system is configured for both the laser illumination of the sample (with the required excitation wavelength based on the choice of mutant or wild-type GFP), as well as the efficient collection of emitted fluorescence from the excited GFPs. The three embodiments of the invention illustrated in  FIGS. 3, 5 and 6  are exemplary of systems having this required capability, however other systems also having this capability are also possible for implementation of the present invention. 
     Once the sample has been prepared in block  1806 , and the charged particle system has been properly configured in block  1808 , the sample can be inserted into the charged particle beam system in block  1810  and positioned under the charged particle beam. The efficient dual imaging capability enabled by the detector optics illustrated in  FIGS. 4A and 4B  may enable this process to be performed with low levels of damage to the specimen (because imaging doses can be minimized). Now, in block  1812 , the sample is illuminated by a laser beam tuned to optimally excite the GFPs within the sample. Preferably almost immediately, rastering of the charged particle beam (comprising either electrons or ions) is started in block  1814  while the light signal from the excited GFPs is collected and stored in an image storage device, such as a frame grabber. Block  1818  represents the operator selecting image processing parameters, such as the FWHM for smoothing and the threshold, as discussed above. This step is optional, and if skipped, block  1816  will use previously-defined image processing parameters. In block  1816 , the raw noisy signal data from the sample are processed to determine the locations of GFPs in the sample, and thus the locations of the PoIs encoded for by the GoIs. 
     Flowchart of Method for Localizing Functionalized Tags Such as Quantum Dots 
       FIG. 19  is a flow chart  1900  for the method of the present invention for localizing functionalized tags such as quantum dots within a sample. In block  1902 , the sample is prepared for attachment of functionalized quantum dots or other types of functionalized fluorescent markers or dyes to the intracellular components of interest to the researcher. In block  1904 , the sample is exposed to a solution of functionalized fluorescent markers (FMs), such as quantum dots (Q-dots). The sample is then prepared for charged particle microscopy in block  1906  in a manner familiar to those skilled in the art. In parallel with blocks  1902 - 1906 , in block  1908 , a charged particle beam system is configured for both the laser illumination of the sample [with the required wavelength(s) based on the choice of Q-dot(s)], as well as the efficient collection of emitted fluorescence from the excited Q-dots. The three embodiments of the invention illustrated in  FIGS. 3, 5 and 6  are exemplary of systems having this required capability, however other systems also having this capability are also possible for implementation of the present invention. 
     Once the sample has been prepared in block  1906 , and the charged particle system has been properly configured in block  1908 , the sample can be inserted into the charged particle beam system in block  1910  and positioned under the charged particle beam. The efficient dual imaging capability enabled by the detector optics illustrated in  FIGS. 4A and 4B  may enable this process to be performed with low levels of damage to the specimen (because imaging doses can be minimized). Now, in block  1912 , the sample is illuminated by a laser beam tuned to optimally excite the Q-dots within the sample. Preferably almost immediately, rastering of the charged particle beam (comprising either electrons or ions) is started in block  1914  while the light signal from the excited Q-dots is collected and stored in an image storage device, such as a frame grabber. Block  1918  represents the operator selecting image processing parameters, such as the FWHM for smoothing and the threshold, as discussed above. This step is optional, and if skipped, block  1916  will use previously-defined image processing parameters. In block  1916 , the raw noisy signal data from the sample are processed to determine the locations of Q-dots in the sample, and thus the locations of the PoIs compatible with the Q-dot functionalization. 
     Combined Secondary Electron and FM Damage Event Imaging 
       FIG. 20  is a schematic diagram  2000  of a combined secondary electron and fluorescent marker image  2002 . During the scanning in a pattern of the charged particle beam across the sample surface by the beam deflector, two images may be simultaneously acquired: a secondary electron (SE) image and a light optical image arising from emitted fluorescent light from the sample containing expressible fluorescent markers (FMs), such as GFPs, or functionalized fluorescent markers, such as Q-dots. The charged particle beam irradiates an area generally somewhat smaller than the area of the light or other radiation beam that causes the FMs to fluoresce—it is preferred that the illumination area not be substantially larger than the area irradiated by the charged particle beam so that the decreases in light collected for each FM damage event may be maximized relative to the overall light background from all the undamaged FMs. The secondary electron image is composed of image pixels, the brightness of each corresponds to the signal from the SE detector while the charged particle beam is on the corresponding point on the sample, the signal from the SE typically being related to the number of SEs detected. Such an image is referred to as a “charged particle beam image” and can be generated by a primary beam of electrons or ions, using detected secondary electrons, backscattered electrons, secondary ions, or other types of signal. In  FIG. 20 , the SE image corresponds to the various lines  2008 , circles  2006 , ovals, and shaded areas  2004 , corresponding to various intracellular components of the cell being imaged, e.g., nuclei, cell membranes, smooth and rough endoplasmic reticula, mitochodria, vesicles, etc. Superimposed on the SE image are indicators of the locations of the multiplicity of FMs, indicated by small black circles in the figure. As the charged particle beam is scanned across the sample, the position of the charged particle beam is registered at the instant that a reduction or extinguishment of fluorescence of a FM is detected. The extinguishment is determined by the image processing method in  FIG. 21 —these data are stored in the FM Location File generated by block  2122  of  FIG. 21 . The benefits of the high collection efficiency combined SE and light detection enabled by the detector optics illustrated in  FIGS. 4A and 4B  are apparent here—high SE collection efficiency improves the image quality of the various intracellular structures, while the efficient collection of light from the sample enables a high percentage of the FMs in the sample to be precisely located, with the location being stored and superimposed onto the SE image. Since the SE and light data both arise from the same raster scan, superposition of the FM locations on the SE image can be very precise. Alternatively, the locations of FMs within the sample can be superimposed on typical TEM images (elastic, inelastic, energy-filtered inelastic, etc.) created using signals from detector  586  in  FIG. 5 , or detector  686  in  FIG. 6 . 
     Exemplary Image Processing Method 
       FIG. 21  is a flow chart  2100  for an image processing method for localizing fluorescent markers (FMs) within a sample. This method assumes that a full raster scan of the sample by the charged particle beam has been completed—during this scan, a set of raw image data for the set of pixels comprising the raster has been acquired and stored in a first image memory. Each pixel datum is a number proportional to the emitted fluorescent light intensity from all the undamaged fluorescent markers (FMs), such as GFPs or Q-dots, in the sample averaged over the pixel dwell time. An image processor may be comprised in the system controller such as  362 ,  562 , and  662  in  FIGS. 3, 5, and 6 , respectively. Alternatively, an image processor may be comprised in a separate off-line processing computer (not shown). In block  2102 , the image processor convolves the raw image data (such as curve  1106  in  FIG. 11 , or curve  1406  in  FIG. 14 ) with a pre-determined smoothing function from block  2104  (such as curve  1206  in  FIG. 12 ) to generate smoothed image data which is stored in a second image memory. In block  2106 , the smoothed image data (such as curve  1108  in  FIG. 11 , or curve  1408  in  FIG. 14 ) from block  2102  is autocorrelated with a pre-determined derivative function from block  2108  (such as curve  1210  in  FIG. 12 ) to generate derivative data which is stored in a third image memory. 
     Next, in block  2110 , the image processor scans the derivative data for all local minima—both the values and locations of all local minima are stored in a Derivative Minimum Location File (DMLF). Examples of local minima include peaks  1310 ,  1312 , and  1314  in  FIG. 13 , or peaks  1510 ,  1512 , and  1514  in  FIG. 15 . A loop comprising decision block  2112  and blocks  2114 ,  2122 , and  2124  is then executed for each of the local minima in the DMLF. The value of each local minimum is compared with a predetermined maximum threshold level from block  2114 , such as level  1320  in  FIG. 13 , or level  1520  in  FIG. 15 . In general, many of the local minima will correspond to random noise fluctuations in the data, and not to true locations of FMs in the sample—with the proper selection of the maximum threshold level in block  2114 , most of the local minima which do not correspond to actual FMs will be eliminated by decision block  2112  (thereby reducing the number of false positives). Also, it is preferred that most of the local minima which do correspond to actual FMs will fall below the maximum threshold level (thereby reducing the number of false negatives). The success of the image processing method in localizing a large fraction of actual FMs, while excluding a large fraction of minima not corresponding to actual FMs relies on the fact that when an actual FM is damaged, there is a permanent reduction in the light from the sample, while for random noise the light from the sample goes up and down, but remains the same on average. Thus, by smoothing the data and using a derivative function, the up and down fluctuations due to noise will be smoothed out, while step reductions in light from the sample will still be detectable. Path  2120  from decision block  2112  corresponds to all local minima falling below the maximum threshold level—the locations of these local minima are saved in the FM Location File (FMLF) in block  2122 , and the loop then proceeds to block  2124 . All local minima having values above the maximum threshold level follow path  2118  to block  2124  and are not stored in the FMLF since these data are, by definition, assumed not to correspond to actual FM locations (this is the purpose of the threshold). In block  2124 , the loop increments to the next local minimum in the DMLF until all stored local minima have been analyzed in decision block  2112 . At the conclusion of the image processing method, the FMLF will preferably contain the locations of the majority of the FMs within the sample, and a minimum number of extraneous (non FM) locations—thus the levels of false negatives (missed FMs) and false positives (erroneous extra FMs) will both be minimized, as discussed in  FIGS. 16 and 17 . 
     The above discussion has used the term “green fluorescent protein”, or “GFP”, to represent any type of expressible biological fluorescent marker, or tag, all being within the scope of the invention. The term “Q-dot” has been used to represent any type of functionalized fluorescent marker as commonly used in the art, all being within the scope of the invention. Although three embodiments of charged particle systems for implementing the present invention are presented, it is understood that other system configurations are also possible within the scope if the invention. The term secondary electron may include not only low energy secondary electrons, but also Auger electrons and backscattered electrons. 
     
       
         
               
             
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
             
           
               
                 TABLE III 
               
             
             
               
                   
               
               
                 Image Processing Routine Optimization Results. For each set of FWHM 
               
               
                 and threshold values, 300 simulation runs (each with exactly three initial 
               
               
                 GFP damage events) were run to get good statistics on the performance of 
               
               
                 the image processing routine. 
               
             
          
           
               
                   
                   
                 % of Times Each No. of 
                 GFPs 
                 False 
                 False 
               
               
                 FWHM 
                   
                 GFPs Detected 
                 Found 
                 Neg. 
                 Pos. 
               
             
          
           
               
                 (pixels) 
                 Threshold 
                 2.0 
                 3.0 
                 4.0 
                 5.0 
                 (%) 
                 (%) 
                 (%) 
               
               
                   
               
             
          
           
               
                 6.0 
                 −8000 
                 9.50 
                 78.25 
                 12.25 
                 0.00 
                 96.83 
                 3.17 
                 3.06 
               
               
                 7.0 
                 −6600 
                 12.50 
                 75.00 
                 12.5 
                 0.00 
                 95.83 
                 4.17 
                 3.13 
               
               
                 8.0 
                 −5000 
                 0.00 
                 85.00 
                 15.00 
                 0.00 
                 100.00 
                 0.00 
                 3.75 
               
               
                 9.0 
                 −4300 
                 3.17 
                 93.67 
                 3.17 
                 0.00 
                 98.94 
                 1.06 
                 0.79 
               
               
                 10.0 
                 −3625 
                 3.00 
                 94.00 
                 3.00 
                 0.00 
                 99.00 
                 1.00 
                 0.75 
               
               
                 11.0 
                 −3100 
                 3.25 
                 93.50 
                 3.25 
                 0.00 
                 98.92 
                 1.08 
                 0.81 
               
               
                 12.0 
                 −2500 
                 0.00 
                 94.00 
                 6.00 
                 0.00 
                 100.00 
                 0.00 
                 1.50 
               
               
                 13.0 
                 −2100 
                 0.00 
                 93.00 
                 7.00 
                 0.00 
                 100.00 
                 0.00 
                 1.75 
               
               
                   
               
             
          
         
       
     
     Computer programs can be applied to input data to perform the functions described herein and thereby transform the input data to generate output data. The output information is applied to one or more output devices such as a display monitor. In preferred embodiments of the present invention, the transformed data represents physical and tangible objects, including producing a particular visual depiction of the physical and tangible objects on a display. 
     Preferred embodiments of the present invention also make use of a particle beam apparatus, such as a FIB or SEM, in order to image a sample using a beam of particles. Such particles used to image a sample inherently interact with the sample resulting in some degree of physical transformation. Further, throughout the present specification, discussions utilizing terms such as “calculating,” “determining,” “measuring,” “generating,” “detecting,” “forming,” or the like, also refer to the action and processes of a computer system, or similar electronic device, that manipulates and transforms data represented as physical quantities within the computer system into other data similarly represented as physical quantities within the computer system or other information storage, transmission or display devices. 
     Although embodiments of the present invention and their advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made to the embodiments described herein without departing from the spirit and scope of the invention as defined by the appended claims. The invention includes several novel and inventive aspects which may be used together or separately in different embodiments. Moreover, the scope of the present application is not intended to be limited to the particular embodiments of the process, machine, manufacture, composition of matter, means, methods and steps described in the specification. For example, the novel image processing method can be used with other types of systems, including prior art systems and yet-to-be developed systems. As one of ordinary skill in the art will readily appreciate from the disclosure of the present invention, processes, machines, manufacture, compositions of matter, means, methods, or steps, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding embodiments described herein may be utilized according to the present invention. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or steps.

Technology Classification (CPC): 7