System and method for localization of large numbers of fluorescent markers in biological samples

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.

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., >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.

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 mirror314inFIGS. 3,4A and4B.

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. 1shows a schematic diagram100of a protein of interest (PoI)102, with a green fluorescent protein (GFP)104attached by a linker peptide110. A typical GFP has a diameter106of 3 nm and a length108of 4 nm—details of the “beta barrel” structure of the GFP104are omitted here. In general, the PoI102will be larger than the GFP104, as shown. One key consideration in the use of expressible tags is that the tag does not interfere with the proper functioning of the PoI102within the cell, whether that function is metabolic, transport, structural, etc. Thus, a short peptide linker110is often used to attach the fairly rigid GFP104to the PoI102, enabling the GFP104to swing around on an arc112of radius114, as shown. Note that this radius114determines the maximum precision at which the GFP104can be located since GFP104is free to occupy any position on circle112. Thus, it is likely that locating the GFP104to 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 speciesAequorea victoriaare 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. 2is a schematic diagram of a 32×32 X-Y scan raster200, illustrating pixels containing GFPs208and pixels without GFPs206. The fast scan axis202is along the X-direction, while the slow scan axis204is 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 pixels206represent those not containing a GFP, while black pixels208contain 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. 3is a schematic diagram of a first embodiment300of the present invention comprising detector optics above the sample306. Sample306may 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. Sample306may 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. Sample306sits on a sample stage at a sample position that defines a sample plane.

A charged particle column302, such as an electron beam column or a focused ion beam column, generates a beam304of charged particles that is focused by column302onto the surface of a sample306at a location308. 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 deflector310, which may comprise magnetic coils, electrostatic multipoles, or a combination of both magnetic coils and electrostatic multipoles, is configured to move the beam304around on the surface of the sample306, typically in an X-Y raster pattern for imaging, as inFIG. 2.

In this first embodiment300of the invention, the sample306is assumed to be thick enough to prevent penetration of the beam304through the sample306, thus all imaging signals (both light and charged particles) are collected above the sample surface as shown. A paraboloidal mirror314is positioned between the deflector310and the sample306. A hole312in mirror314allows passage of beam304downwards to the sample306. Below the mirror314is a flat shield plate316, typically biased to the same voltage as the sample306. In order to achieve maximum collection efficiency for light, mirror314is configured to subtend the largest possible solid angle, preferably greater than π steradians, at location308. Then the maximum possible amount of light emitted from the fluorescent markers (FMs) at location308will be collected and transmitted through the beam splitter326, then through color filter372, and finally to detector360—this maximizes the achievable signal-to-noise ratio in the optical signal.

The fluorescent markers (FMs) are excited by light324from laser322, which is emitted upwards, reflected first off beam splitter326and then reflected and focused by the paraboloidal mirror314onto the surface of sample306at location308. Note that it is desirable to have the largest possible transmission of emitted light from the FMs through beam splitter326in order to increase the amount of light reaching detector360. If the transmission of beam splitter326is 50%, then half the signal light327from location308will get to the detector360, and half of the excitation light324from laser322will reach location308. Thus, to focus 3 mW from laser322onto location308would require a 6 mW laser output324(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 splitter326, for example to 80%. Then 80% of the light from location308(again, ignoring reflective losses) will pass through the beam splitter326, while only 20% of the light324from laser322will reach location308—thus, to focus 3 mW at location308, a 15 mW laser output power324would be required (12 mW would pass through beam splitter326, to be absorbed in a beam dump (not shown) above splitter326). In this embodiment, the light from laser322is reflected by mirror314onto the top surface of sample306, that is, the light does not first pass through sample306before being reflected by mirror314and the light from the source illuminates the sample initially from above the sample. Similarly, light emitted by fluorescent markers within sample306are emitted through the top surface of sample306, collected by mirror314above sample306, and reflected to light detector 360 without passing completely through sample306and being collected on the opposite side of the mirror, as in U.S. Pat. No. 7,317,515.

The second function of paraboloidal mirror314is to provide a conductor that can be electrically biased to provide an electric field that prevents secondary electrons from impacting mirror314by reflecting secondary electrons (SEs)332emitted from location308due to the impact of the primary charged particle beam304. This is shown in more detail inFIGS. 4A and 4B. SEs332are 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 light328does, because the SEs are reflected by the electrostatic field created by the voltage applied to the mirror314or other conductor, and this field extends throughout the entire volume of the paraboloidal mirror314(seeFIG. 4Bfor an isometric view of mirror314). The SEs332are deflected toward a detector320and collected by the detector320as shown to the side of the sample306. Thus, both the light and secondary electrons are collected from location308with high efficiencies since there is no conflict between the collection solid angles for light and SEs. The size of hole312is 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 mirror314is approximately at location308on the surface of sample306—thus light emitted from the vicinity of location308will be focused into roughly parallel light beams328, directed towards the right ofFIG. 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 detector320.

A system controller362is electrically connected to column302through cable370, to X-Y deflector310through cable368, to mirror314through cable366, to shield plate316through cable376, to sample306through cable378, to SE detector320through cable380, and to laser322through cable374. The system controller362coordinates the scanning of beam304by the X-Y deflector310with 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 enclosure334contains the exit of column302, X-Y deflector310, mirror314, shield plate316, and sample306, as shown. Typically, it is much easier to locate as much of the light optical instrumentation outside the vacuum as possible, thus a viewport356allows the light327from laser322(reflected off beam splitter326) to pass into enclosure334, while the light emitted from FMs at location308is allowed to pass out from enclosure334, through beam splitter326, then through color filter372and into detector360. Color filter372serves to reduce the amount of laser excitation light324which can pass into detector360. Since the excitation light always has a shorter wavelength than the emitted light from the FMs, it is possible to tune the passband of filter372to 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 detector360. Electrical feedthrough354allows the passage of cables366,368and370into and out of enclosure334, while feedthrough352allows the passage of cables376,378and380into and out of enclosure334.

Detector Optics for High Efficiency Collection of Light and Secondary Electrons

FIG. 4Ais a side cross-sectional view400generated using the SIMION ray-tracing program showing both light and secondary electron trajectories for the detector optics inFIG. 3. The primary beam304can be seen passing downwards through hole312in the paraboloidal mirror314. Impact of beam304with sample306at location308induces the emission of secondary electrons332into a cosine (Lambert Law) distribution. Shield plate316and sample306generally have the same voltage applied by system controller362(seeFIG. 3). Several hundred volts negative bias is applied to the conductive mirror surface314to repel the (0 to 50 eV) secondary electrons332as shown. This reflection differs from that of the light reflecting specularly off mirror314, thus the SEs are collected on detector320to the side of sample306. The collection solid angle at location308is very high, preferably greater than it steradians, in this configuration, giving a good signal-to-noise SE image. Light emitted from the fluorescent markers (FMs) in the sample306is also emitted into a cosine distribution, a large fraction of which is directed towards mirror314, as shown. Since location308on sample306is the focal point of paraboloidal mirror314, light328reflecting off mirror314is generally parallel passing to the right of theFIG. 4A. It will be understood that the benefits of the mirror314can 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 mirror314include 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. 4Bis a cutaway isometric view of the detector optics inFIG. 4A, also generated using SIMION. In addition, the beam splitter326is shown at the lower right. The elliptical pattern of SE332impacts at detector320can be seen, thus the area of detector320need 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. 5is a schematic diagram of a second embodiment500of the present invention comprising detector optics below a sample506. A charged particle column502, such as an electron beam column or a focused ion beam column, generates a beam504of charged particles which is focused by column502onto the surface of the sample506at a location508. Beam504typically includes electrons having energies between about 50 keV and 300 keV. An X-Y beam deflector510, which may comprise magnetic coils, electrostatic multipoles, or a combination of both magnetic coils and electrostatic multipoles, is configured to move the beam504around on the surface of the sample506, typically in an X-Y raster pattern for imaging. In this second embodiment500of the invention, the sample506is assumed to be thin enough to permit penetration of the beam504through the sample506, thus all imaging signals (both light and charged particles) are collected below the sample surface as shown. A paraboloidal mirror580is positioned below the sample506. A hole582in mirror580allows the travel of transmitted charged particle beam584downwards after passage through sample506. Beam584may typically comprise unscattered particles from the primary beam504, elastically-scattered particles, inelastically-scattered particles, secondary electrons and/or ions, and particles which have scattered both elastically and inelastically in the sample506. After passing through hole582, beam584enters detector586which 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, mirror580is configured to subtend the largest possible solid angle (typically >π steradians) at location508. Thus, the maximum possible amount of light emitted from the fluorescent markers (FMs) at location508will be collected and transmitted through beam splitter596, then through color filter598, and finally to light detector590—this maximizes the achievable signal-to-noise ratio in the optical signal. The FMs are excited by light594from laser522, which is emitted upwards, reflected first off beam splitter596and then reflected and focused by paraboloidal mirror580through sample506at location508. Note that it is desirable to have the largest possible transmission of light through beam splitter596in order to increase the amount of light reaching detector590—the same percentage transmission considerations apply here as forFIG. 3, above. It is important that the size of hole582be kept to a minimum to reduce loss in light reflection, while remaining large enough to accommodate the elastically-scattered electrons within beam584. The focal point of the paraboloid580is at approximately location508on sample506—thus light emitted from the vicinity of location508will be focused into roughly parallel light beams587, directed towards the right of the figure.

A system controller562is electrically connected to column502through cable570, to X-Y deflector510through cable568, to sample506through cable566, to laser522through cable574, to detector590through cable564, and to detector586through cable588. System controller562coordinates the scanning of beam504by X-Y deflector510with 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 enclosure534contains the exit of column502, X-Y deflector510, sample506, mirror580, and detector586, as shown. Viewport556allows the light587from laser522(reflected off beam splitter596) to pass into enclosure534, while the light emitted from FMs at location508is allowed to pass out of enclosure534, through beam splitter596, then through color filter598, and into detector590. Color filter598serves to reduce the amount of laser excitation light594which can pass into detector590, as for the first embodiment inFIG. 3. The same reflectivity considerations apply here for beam splitter596as for beam splitter326inFIG. 3. Electrical feedthrough554allows the passage of cables566,568and570into and out of enclosure534, while feedthrough552allows the passage of cable588into and out of enclosure534.

Third Embodiment

Detector Optics Both Above and Below the Sample

FIG. 6is a schematic diagram of a third embodiment600of the present invention comprising detector optics both above and below the sample606. A charged particle column602, such as an electron beam column or a focused ion beam column, generates a beam604of charged particles which is focused by column602onto the surface of a sample606at a location608. An X-Y beam deflector610, which may comprise magnetic coils, electrostatic multipoles, or a combination of both magnetic coils and electrostatic multipoles, is configured to move the beam604around on the surface of the sample606, typically in an X-Y raster pattern for imaging. In this third embodiment600of the invention, the sample606is assumed to be thin enough to permit penetration of the beam604through the sample606. To achieve larger collection efficiencies for light, two paraboloidal mirrors614and680are positioned above and below the sample606, respectively. A hole612in mirror614allows passage of beam604to the sample606. A hole682in mirror680allows passage of transmitted charged particle beam684downwards after passage through sample606. Beam684may typically comprise unscattered particles from primary beam604, elastically-scattered particles, inelastically-scattered particles, secondary electrons and/or ions, and particles which have scattered both elastically and inelastically in sample606. After passing through hole682, beam684enters detector686which 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 SEs632emitted from location608due to the impact of primary beam604into detector620are the same as inFIGS. 3,4A and4B.

In order to achieve maximum collection efficiency for light, both mirrors614and680are configured to subtend the largest possible solid angles (typically >π steradians for each of mirrors614and680, giving a total >2π steradians) at location608. The maximum possible amount of upwards-emitted light emitted from the fluorescent markers (FMs) at location608will be collected and transmitted through the beam splitter626, then through color filter672, and into detector660—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 location608will be collected and transmitted through color filter698and then to detector690. The FMs are excited by light624from laser622, which is emitted upwards, reflected first off beam splitter626and then reflected and focused by paraboloidal mirror614onto sample606at location608. Note that it is desirable to have the largest possible transmission of light through beam splitter626in order to increase the amount of light reaching detector660—the same percentage transmission considerations apply here as forFIGS. 3 and 5, above. It is important that the size of holes612and682be kept to a minimum to reduce loss in light reflection. The focal points of paraboloids614and680are at approximately location608on sample606—thus light emitted from the vicinity of location608will be focused into roughly parallel light beams628and687, respectively, directed towards the right of the figure.

A system controller662is electrically connected to column602through cable670, to X-Y deflector610through cable668, to mirror614through cable666, to detectors660and690through cable664, to shield plate616through cable676, to sample606through cable678, to detector686through cable688, and to laser622through cable674. Detectors690and660are shown interconnected through cable699, however, an alternative configuration would have separate cables to system controller662. System controller662coordinates the scanning of beam604by the X-Y deflector610with 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 enclosure634contains the exit of column602, X-Y deflector610, mirror614, shield plate616, sample506, mirror680, and detector686, as shown. It is much easier to locate as much of the light optical instrumentation outside the vacuum as possible, thus a viewport656allows the light624from laser622(reflected off beam splitter626) to pass into enclosure634, while the upwards-emitted light from FMs at location608is allowed to pass out of enclosure634, through beam splitter626, then through color filter672and into detector660. The downwards-emitted light from FMs at location608passes out of enclosure634through viewport656, through color filter698, and then into detector690. Color filters672and698serve to reduce the amount of laser excitation light624which can pass into detectors660and690, respectively, as for the first embodiment inFIGS. 3 and 5. Electrical feedthrough654allows the passage of cables666,668and670into and out of enclosure334, while feedthrough652allows the passage of cables676,678,688, and680into and out of enclosure634. Note that in this dual paraboloidal mirror configuration, light from laser622which passes through sample606unabsorbed will reflect off mirror680towards detector690—thus color filter698must be configured to withstand a potentially high level of laser illumination, higher than would be the case inFIGS. 3 and 5.

Imaging of Smaller Numbers of Fluorescent Markers in the Scan Field

FIG. 7is a graph700of the signal704(number of photons collected per pixel) as a function of the time702during 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. Curve706represents 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 curve706descends 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, curve706has some irregularities while following an overall descent from 0 s to 60 s. The laser power of 3 mW is distributed over a 28 μm2area 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. 8is a graph800showing a close-up of the beginning of the graph700inFIG. 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). Graphs700and800were made with the assumptions listed in Table I. Curve806represents 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 drop812at the upper left. Above curve806is the +3σ curve808(long dashes), representing expected signal fluctuations three standard deviations above the mean signal level806—a relatively unlikely event. Similarly, below curve806is the −3σ curve810(short dashes), representing expected signal fluctuations three standard deviations below the mean signal level806—also a relatively unlikely event. The key thing to note here is that at jump812, representing the loss (due to damage) of one GFP, curve810at the left of jump812is well above curve808at the right of jump812—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.

FIG. 9is a graph900of the signal904(number of photons collected per pixel) as a function of the time902during 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 inFIG. 7. Curve906represents 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 curve906descends 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, curve906is approximately a straight line. The laser power of 3 mW is distributed over a 28 μm2area 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. 10is a graph1000showing a close-up of the beginning of the graph900inFIG. 9, showing damage to the first eight GFPs out of the total of 10000. As was the case for graph700inFIG. 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). Graphs900and1000represent a hundred times more GFPs in the area illuminated by the laser than was the case in FIGS.7and8—thus the total light collected (see the three alternative detector geometries inFIGS. 3,5, and6) 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 graphs800and1000.

Graphs900and1000were made with the assumptions listed in Table II. Curve1006represents 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 drop1012at the upper left. Above curve1006is the +3σ curve1008(long dashes), representing expected signal fluctuations three standard deviations above the mean signal level1006—a relatively unlikely event. Similarly, below curve1006is the −3σ curve1010(short dashes), representing expected signal fluctuations three standard deviations below the mean signal level1006—also a relatively unlikely event. The key thing to note here is that at the jump1012, representing the loss (due to damage) of one GFP, curve1010at the left of jump1012is now below curve1008at the right of jump1012—this situation differs qualitatively from that shown inFIG. 8where there was no overlap. Although ±3σ 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 detector360inFIG. 3) will be more difficult in this case.

In this section, we will examine further the localization of fluorescent markers (FMs) such as green fluorescent proteins (GFPs), as first discussed inFIGS. 7 and 8, above, for the case of 100 GFPs in the laser illumination area.FIG. 11is a graph1100showing a raw signal1106with statistical noise and a smoothed signal1108as a function of the time1102during a raster scan, showing damage to the first three GFPs out of the total of 100. Both curves1106and1108are plotted against a vertical axis1104representing 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 forFIG. 8, curves808and810were close to the mean number of photons curve806, 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 at1110,1112, and1114. An image processing method comprising a smoothing step, followed by a differentiation step, is illustrated inFIGS. 11-13. InFIG. 11, curve1108is a smoothed version of the raw data curve1106—the downward steps at each of the three GFP damage events1110,1112, and1114can clearly be seen. The smoothing function (kernel)1206is shown inFIG. 12.

FIG. 12is a graph1200showing a Gaussian smoothing function1206centered at1208and the derivative function1210centered at1212, plotted against the time1202from the center (i.e., the particular pixel data being smoothed) in units of pixels (229 μs dwell time in this example)—the vertical axis1204is the values of the two functions (unitless). The sum of the 13 weights (solid squares) in curve1206is 1.000, with a maximum value at the center of approximately 0.11. Although in this embodiment, a Gaussian smoothing function1206is 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 curve1206, the resultant smoothed data, such as curve1108inFIG. 11, is then autocorrelated with a second, “derivative function” curve1210, which is the derivative of curve1206in this example. Although in this embodiment, curve1210is 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 curve1206is a parameter to be optimized, as discussed inFIG. 16, below, and is 10.0 pixels in this example. A simplification of this process would be to first convolve curves1206and1210, which is allowed since both convolution and autocorrelation are associative, and then convolve this resultant curve with the raw image data. Curves1206and1210are kept separate here to clarify the process.

FIG. 13is a graph1300showing the raw signal1106and the smoothed signal1108(both fromFIG. 11), and the smoothed derivative1306as a function of the time1302during a raster scan, showing damage to the first three GFPs out of the total of 100. The vertical axis1304at the left is for curves1106and1108in units of photons per pixel from all undamaged GFPs, while the vertical axis1305at the right is for the derivative1306, also in units of the numbers of photons per pixel from all undamaged GFPs. The derivative curve1306has three deep downward-going peaks: a first at1310corresponding to GFP damage event1110, a second at1312corresponding to GFP damage event1112, and a third at1314corresponding to GFP damage event1114—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 signal1106, as shown. The threshold line1320defines the maximum height for peaks in the derivative curve1306which 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 curve1206inFIG. 12), and the threshold value1320. Choices for these two parameters are discussed inFIG. 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 inFIGS. 9 and 10, above, for the case of a hundred times as many GFPs (i.e., now 10000) in the laser illumination area.FIG. 14for the 10000 GFP case corresponds toFIG. 11for the 100 GFP case—graph1400shows a raw signal1406with statistical noise and a smoothed signal1408as a function of the time1402during a raster scan, showing damage to the first three GFPs out of the total of 10000. Both graphs are plotted against a vertical axis1404representing 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 forFIG. 10, curves1008and1010were much farther from the mean number of photons curve1006, 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 inFIGS. 11-13was used here. InFIG. 14, curve1408is a smoothed version of the raw data curve1406, calculated using a smoothing curve1206having a FWHM of 10.0 pixels—the exact locations of the downward steps at each of the three GFP damage events1410,1412,1414are difficult to see in the raw data curve1406. The smoothing function (kernel)1206is shown inFIG. 12, generating the smoothed curve1408, in which the GFP damage events are more apparent.

FIG. 15is a graph1500showing the raw signal1406and the smoothed signal1408(both fromFIG. 14), and the smoothed derivative1506as a function of the time1502during a raster scan, showing damage to the first three GFPs out of the total of 10000. The vertical axis1504at the left is for curves1406and1408in units of photons per pixel, while the vertical axis at the right1505is for the derivative, also in units of the numbers of photons per pixel. The derivative curve1506has three deep downward-going peaks: a first at1510corresponding to GFP damage event1410, a second at1512corresponding to GFP damage event1412, and a third at1514corresponding to GFP damage event1414—note the excellent agreement, in spite of the relatively noisy raw signal data1406in this example, compared with curve1106inFIG. 11. The threshold line1520defines the maximum height for peaks in the derivative curve1506which are counted as GFP damage events (compare with threshold1320inFIG. 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 curve1206inFIG. 12), and the threshold value1520. Choices for these two parameters are discussed inFIG. 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 signal1406, 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. 16is a histogram1600showing 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 inFIGS. 11,13-15), histogram1600shows that for a FWHM of 10.0 pixels and a threshold of −3625, that 94% of the time1608, 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 cases1606, one out of the three GFPs is missed, while in another 3% of the cases1610, an extraneous GFP is recorded (3 actual+1 extraneous=4 total).

FIG. 17is a graph1700of the optimization results for the image processing method, illustrating the GFPs found1706as a function of the FWHM (in pixels)1702. The left axis1704corresponds to curve1706in percent. Curve1708illustrates the percentage of false negatives (i.e., the missed GFPs) using axis1704magnified by 10×. The sum of curves1706and1708always equals 100%. Curve1710illustrates the percentage of false positives (i.e., extraneous GFP locations not corresponding to real GFPs), also using axis1704magnified by 10×. The right axis1720corresponds to the curve1712of the optimized threshold level for the derivative (i.e., the values for line1320inFIG. 13and line1520inFIG. 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 curve1706while reducing and equalizing the percentages of false negatives and false positives. The results are shown in Table III, below. The threshold curve1712continues 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%) ⅔=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%) ¾=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 inFIG. 17and 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. 18is a flow chart1800for the method of the present invention for localizing expressible tags such as GFPs within a biological sample. In block1802, 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'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 block1804, 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 block1806in 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 blocks1802-1806, in block1808, 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 inFIGS. 3,5and6are 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 block1806, and the charged particle system has been properly configured in block1808, the sample can be inserted into the charged particle beam system in block1810and positioned under the charged particle beam. The efficient dual imaging capability enabled by the detector optics illustrated inFIGS. 4A and 4Bmay enable this process to be performed with low levels of damage to the specimen (because imaging doses can be minimized). Now, in block1812, 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 block1814while the light signal from the excited GFPs is collected and stored in an image storage device, such as a frame grabber. Block1818represents 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, block1816will use previously-defined image processing parameters. In block1816, 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. 19is a flow chart1900for the method of the present invention for localizing functionalized tags such as quantum dots within a sample. In block1902, 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 block1904, 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 block1906in a manner familiar to those skilled in the art. In parallel with blocks1902-1906, in block1908, 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 inFIGS. 3,5and6are 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 block1906, and the charged particle system has been properly configured in block1908, the sample can be inserted into the charged particle beam system in block1910and positioned under the charged particle beam. The efficient dual imaging capability enabled by the detector optics illustrated inFIGS. 4A and 4Bmay enable this process to be performed with low levels of damage to the specimen (because imaging doses can be minimized). Now, in block1912, 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 block1914while the light signal from the excited Q-dots is collected and stored in an image storage device, such as a frame grabber. Block1918represents 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, block1916will use previously-defined image processing parameters. In block1916, 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. 20is a schematic diagram2000of a combined secondary electron and fluorescent marker image2002. 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. InFIG. 20, the SE image corresponds to the various lines2008, circles2006, ovals, and shaded areas2004, 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 block2122ofFIG. 21. The benefits of the high collection efficiency combined SE and light detection enabled by the detector optics illustrated inFIGS. 4A and 4Bare 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 detector586inFIG. 5, or detector686inFIG. 6.

Exemplary Image Processing Method

FIG. 21is a flow chart2100for 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 as362,562, and662inFIGS. 3,5, and6, respectively. Alternatively, an image processor may be comprised in a separate off-line processing computer (not shown). In block2102, the image processor convolves the raw image data (such as curve1106inFIG. 11, or curve1406inFIG. 14) with a pre-determined smoothing function from block2104(such as curve1206inFIG. 12) to generate smoothed image data which is stored in a second image memory. In block2106, the smoothed image data (such as curve1108inFIG. 11, or curve1408inFIG. 14) from block2102is autocorrelated with a pre-determined derivative function from block2108(such as curve1210inFIG. 12) to generate derivative data which is stored in a third image memory.

Next, in block2110, 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 peaks1310,1312, and1314inFIG. 13, or peaks1510,1512, and1514inFIG. 15. A loop comprising decision block2112and blocks2114,2122, and2124is 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 block2114, such as level1320inFIG. 13, or level1520inFIG. 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 block2114, most of the local minima which do not correspond to actual FMs will be eliminated by decision block2112(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. Path2120from decision block2112corresponds 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 block2122, and the loop then proceeds to block2124. All local minima having values above the maximum threshold level follow path2118to block2124and 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 block2124, the loop increments to the next local minimum in the DMLF until all stored local minima have been analyzed in decision block2112. 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 inFIGS. 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 IIIImage Processing Routine Optimization Results. For each set ofFWHM and threshold values, 300 simulation runs (each with exactlythree initial GFP damage events) were run to get good statisticson the performance of the image processing routine.% of Times Each No.GFPsFalseFalseFWHMof GFPs DetectedFoundNeg.Pos.(pixels)Threshold2.03.04.05.0(%)(%)(%)6.0−80009.5078.2512.250.0096.833.173.067.0−660012.5075.0012.50.0095.834.173.138.0−50000.0085.0015.000.00100.000.003.759.0−43003.1793.673.170.0098.941.060.7910.0−36253.0094.003.000.0099.001.000.7511.0−31003.2593.503.250.0098.921.080.8112.0−25000.0094.006.000.00100.000.001.5013.0−21000.0093.007.000.00100.000.001.75

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.