Strategies for automated fetal cell screening

Strategies for automated fetal cell screening

Fatima A.Merchant1 and Kenneth R.Castleman

Advanced Digital Imaging Research, LLC, League City, TX 77573, USA

1To whom correspondence should be addressed at: Advanced Digital Imaging Research, LLC, 2450 South Shore Blvd, Suite 305,

League City, TX 77573, USA; E-mail: [email protected]

Studies to date have demonstrated fetal sex determination and aneuploidy detection from maternal blood, but aclinical screening technique has not yet emerged. A key limiting factor is the small number of fetal cells, which makesdetection speci®city and reliability critical. Visual inspection of unsorted or sorted fetal cells is laborious, and cellscan be easily missed. Moreover, it is impractical to examine manually all the separated cells. It is highly likely thatautomation may increase the number of cells inspected, resulting in higher detection sensitivities. Flow and imagecytometry are two feasible approaches for automated detection of cells. This review details computerized microscopy(image cytometry) techniques for the automatic detection of fetal cells. Microscopy-based approaches used to identifyfetal origin include: (i) immunocytochemical identi®cation of fetal haemoglobin-speci®c cells (light or ¯uorescencemicroscopy); (ii) identi®cation of sex chromosomes and/or aneuploidy using ¯uorescence in-situ hybridization; and(iii) morphological identi®cation of nucleated red blood cells using light microscopy. The relevant instrumentation,including motorized stages and ®lters, cameras and digitizer boards are discussed, and software algorithms,including image enhancement, autofocusing, object detection and relocation, and features for operator review anddata analysis, are outlined.

Keywords: aneuploidy detection/automated microscopy/fetal cell detection/image analysis/rare event detection

TABLE OF CONTENTS

Introduction

Image cytometry-based strategies

FISH labelling of fetal cells

Transmitted-light imaging of fetal cells

Conclusions

Summary

Acknowledgements

References

Introduction

The ultimate goal of much current research in medical

cytogenetics is to make low-cost, low-risk prenatal genetic

screening widely available (Steele et al., 1996; Bianchi et al.,

2001). Scientists have documented the presence of fetal cells in

maternal blood, and have envisioned using them to enable non-

invasive prenatal screening. Using fetal cells isolated from

maternal peripheral blood samples eliminates the procedure-

related risks associated with amniocentesis and chorionic villus

sampling (Firth et al., 1991). The minute proportion of fetal cells

found in maternal blood can now be enriched to one per few

thousand using either magnetic activated cell sorting (MACS)

(Ganshirt-Ahlert et al., 1992, 1993) or ¯uorescence activated cell

sorting (FACS) (Bianchi et al., 1993), or a combination of the

two. Aneuploidies can then be detected with chromosome-speci®c

DNA probes using either ¯uorescence in-situ hybridization

(FISH) (Elias et al., 1992; Ganshirt-Ahlert et al., 1993) or other

techniques such as PCR (Geifman-Holtzman et al., 1996).

Current status

Despite some promising results and several investigations in this

®eld, non-invasive prenatal screening using fetal cells for routine

genetic analysis is far from being realized in the clinical setting. A

key limiting factor is the small number of fetal cells in the

maternal circulation, and this makes cell detection labour-

intensive and also limits accuracy. To be useful for genetic

analysis, fetal cells must not only be separated and distinguished

from maternal cells, but they must be unequivocally identi®ed as

cells of fetal origin (Bianchi et al., 2001). Microscopy-based

approaches that have been used to identify fetal origin have

included techniques such as: (i) immunocytochemical identi®ca-

tion of fetal haemoglobin (HbF) -speci®c cells (light or

¯uorescence microscopy) (Oosterwijk et al., 1996, 1998a,b;

Tanke et al., 1996; Bajaj et al., 2000); (ii) identi®cation of X and

Y chromosomes (male fetal cells) using FISH (Elias et al., 1992;

Ganshirt-Ahlert et al., 1993); and (iii) morphological identi®ca-

tion of May±Giemsa-stained nucleated red blood cells (nRBCs)

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using light microscopy (Takabayashi et al., 1995). These

techniques, however, tend to be very labour-intensive because

hundreds of maternal cells have to be examined to locate one or

more fetal cell(s). Furthermore, it is critical to locate fetal cells

reliably because of the limited number of cells available for

diagnosis. This creates the need for automated systems that allow

repeatable, unbiased and practical detection of the small

proportions of fetal cells in enriched maternal blood samples.

To date, fetal cells have been isolated using a variety of

techniques such as density gradient centrifugation, ¯ow cytometry

or magnetic cell sorting. Not only have the sorting approaches

varied widely, but the gestation period is also different in most of

the studies published (Bianchi et al., 2001). Thus, it has been

dif®cult to use data from these studies to generalize about the

absolute numbers of fetal cells recovered. Further, it is quite likely

that the number of fetal cells varies from time to time in the same

individual, as well as among individuals. There is therefore a

pressing need for systematic sequential studies designed to

evaluate enrichment procedures, time of entry of fetal cells into

maternal blood, and the absolute concentrations of these cells at

different periods of gestation.

The need for automation

Computer automation has the potential to make the procedure

practical on a large scale because: (i) it reduces cost by relieving

humans of tedious duties; (ii) it uses quantitative methods to

determine statistically signi®cant results on the relatively rare

fetal cells; and (iii) it can support signi®cantly expanded testing

volumes without concomitant requirements for additional per-

sonnel and ¯oor space. Moreover, automation may also serve as

enabling technology to enhance current research in prenatal

screening. For instance, automated screening may be used to

evaluate enrichment procedures in terms of both yield (the

absolute number of fetal cells isolated from a maternal sample)

and purity (the ratio of fetal cells to the total number of cells

isolated). This is because virtually all the cells in a sorted sample

can be examined using automation, and an increase in the number

of cells inspected may result in higher detection sensitivities.

Flow versus imaging

Flow and image cytometry are two feasible approaches for the

automated detection of cells (Iverson et al., 1981; Price et al.,

1991; Wachtel et al., 1991; Tanke et al., 1996; Oosterwijk et al.,

1998a). Both of these methods rely on morphological descriptors

and/or highly speci®c markers for cell recognition. Flow cytometry

is superior when analysing a large number of cells at higher speeds,

but image-based cytometry with a microscope is becoming a

primary technique for screening clinical tissue specimens because

it allows a diverse range of quantitative and non-destructive

measurements to be made on specimens (Ploem et al., 1986; Parry

and Hemstreet, 1988). While ¯ow cytometry has excellent

quantitative precision, the measured parameters are limited to

integrated ¯uorescence intensities and light scatter per cell. Image

cytometry offers other advantages that are not possible with ¯ow

cytometry, including: (i) permanent records of detected cells can

be stored as images in memory; and (ii) the re-examination of

detected cellsÐsuch cells can be easily re-located on slides using

previously determined spatial locations. The limit of detection is

also relatively higher for image cytometry.

Image cytometry-based strategies

The fetal cell detection approaches based on image cytometry

with light microscopy that are discussed in this review form three

major categories: (i) immunophenotyping of fetal cells using a

marker, such as embryonic haemoglobins, which allows easy

microscopic identi®cation; (ii) FISH for sex or aneuploidy

determination; and (iii) morphological descriptors to identify

nucleated cells using characteristic structural features.

Immunocytochemical labelling of fetal cells

Monoclonal antibodies that are directed to characteristic cellular

components have been used routinely to identify cells of interest.

This not only uniquely identi®es the target cell, but also increases

the sensitivity of detection. In the case of fetal cells, antibodies to

the gamma/epsilon/zeta chains of haemoglobin are used to

identify fetal haemoglobin speci®cally (Zheng et al., 1993,

1995, 1999). Immunophenotyping for selective identi®cation of

fetal haemoglobin by microscopy has been used in conjunction

with FISH for the detection of fetal cells (Zheng et al., 1993).

The Applied Imaging system

A semi-automated approach for the detection of fetal erythro-

blasts in maternal blood, using multi-mode microscopy has been

described (Tanke et al., 1996). This system was further developed

under a joint venture with Applied Imaging Corporation (Santa

Clara, CA, USA). The instrumentation and software for this

system were described in 1998 (Ravkin and Temov, 1998), while

others (Oosterwijk et al., 1998c) tested the system's performance

by locating fetal nucleated red blood cells (fnRBCs) in 52% of 42

maternal blood samples. The detection of fetal cells was achieved

using labelled monoclonal antibodies directed to gamma and

epsilon haemoglobin chains, in combination with nuclear staining

using DAPI (4¢,6-diamidino-2-phenyl indole dihydrochloride).

This allows the use of both transmitted light (bright®eld) and

¯uorescence microscopy for the speci®c identi®cation of fetal

cells. The nucleated fetal cells stain positively for both the HbF

and nucleus.

The Applied Imaging system was built around a conventional

microscope with transmission and ¯uorescence optics. It included

a motorized stage and motorized ®lter wheels for both transmis-

sion and ¯uorescence. Image acquisition was achieved using a

Cohu 4910 camera. The software capabilities included scanning,

relocation, autofocusing, feature extraction, and facilities for

operator review and data analysis.

In a description of system performance, the limit of detection

was reported as 1:1.53106 with 91% sensitivity (Oosterwijk et

al., 1998c). These authors used maternal blood samples processed

via a protocol consisting of one enrichment step followed by

immunocytochemical staining for HbF, and FISH for X- and Y-

chromosomal sequences. The system automatically detected cells

positive for both DAPI and HbF, and this was followed by manual

identi®cation of the detected cells to con®rm fetal origin. Out of

44 slides that were screened, 23 were found to be positive for HbF

by automated screening, and 19 were positive by manual

screening. The number of objects selected as HbF/DAPI-positive

by the system ranged from 107 to 9981 (average 1452). Following

operator review, the actual number of HbF/DAPI-positive cells

ranged from 0 to 111 (average 8) cells. Thus, the false-positive

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rate was quite high, though a possible contributing factor to this

was non-speci®c staining. The average time needed for screening

was 4 h and 27 min (range 1 h 20 min to 7 h 20 min). The

screening time per slide was much longer than that required for

manual screening (average 20 min). Only true fetal cells were

then subjected to visual genetic analysis via FISH. However,

FISH data on the detected nRBC population were insuf®cient to

enable reliable statistical analysis.

A novel feature of this system was the capability to acquire

simultaneously the absorption (HbF) and ¯uorescence (DAPI)

images. This had the advantages of speed and easy human

recognition. A disadvantage was that stringent slide preparation

was needed for cell ®xation and DAPI staining. Non-optimal slide

preparation resulted in fuzzy DAPI images that overlapped the

cytoplasm (HbF staining), in which case only sequential imaging

was possible. However, it was unclear from the data reported,

whether simultaneous or sequential data acquisition was used.

Despite the relatively promising results reported earlier, no further

data are available on the current status of this instrument.

Scanning cytometer for detection of rare events

More recently, an automated scanning cytometer for the detection

of rare events was described (Bajaj et al., 2000), which used a

model of fnRBCs in the maternal circulation. Adult cells were

spiked with fetal cells to create ratios of about 1 fnRBC in 107

nucleated cells, and deposited on slides using centrifugal

cytology. The cells were stained with ¯uorescein isothiocyanate

(FITC)-conjugated anti-fetal haemoglobin (gamma chain), and

DAPI. The fnRBCs stained positively for both DAPI (nucleus)

and FITC (HbF). The instrument was built around a conventional

microscope ®tted with a CCD camera, motorized XY-stage, and a

piezoelectric Z-axis objective positioner. A key feature of this

instrument was the application of analogue autofocus circuitry for

achieving fast autofocus (Bravo-Zanoguera et al., 1998). A FITC

image was ®rst obtained, and the DAPI-labelled cells were

recorded in a second scan by relocating to the previously stored

FITC cell co-ordinates. A dual-colour montage of the detected

cells was prepared and manually edited to discard artefacts and

verify fetal origin.

Fetal cell detection was tested using 15 spiked samples.

Approximately 1±14 fnRBCs were detected in 20 million

peripheral blood mononuclear cells. An average sensitivity of

91% was reported, with a correlation coef®cient of 0.9915

between manually and automatically scanned data. The total time

to analyse 203106 cells was approximately 6 h 26 min (four

slides, 5800 ®elds per slide at a 1-Hz ®eld rate). Considerable

effort was devoted to optimizing cell preparation and immuno-

cytochemical staining. Minimal enrichment and centrifugal

cytology techniques were implemented to minimize cell loss.

These results showed that scanning cytometry had potential for

rare-event detection, and it remains to be seen whether its

application to fetal cell detection from maternal blood yields

equally exciting results.

Limitations of immunocytochemical labelling

A major weakness of the immunophenotyping approach is the

lack of any speci®c marker for fetal cells. To date, no antibodyÐ

whether cell surface or intracellularÐis 100% speci®c for fetal

cells (Zheng et al, 1997, 1999). In the 5- to 35-week fetus, the

predominant haemoglobin molecule consists of alpha and gamma

chains and is known as HbF. However, gamma globin, while

reasonably speci®c for fetal cells, is not expressed in all fetal

cells. Moreover, there is some evidence that pregnancy stimulates

the synthesis of small amounts of HbF in the adult (Pembrey et

al., 1973). In the embryonic stage, additional chains (known as

the zeta chains) are found in the haemoglobin molecule; these are

present in fetal cells up to 14 weeks (DeMaria et al., 1996).

Monoclonal antibodies directed against the zeta chain of the fetal

haemoglobin molecule were expected to be highly speci®c to fetal

cells, but no relationship was seen between zeta-positive cells and

gestational age. Instead, dual-labelling studies showed that most

zeta-positive cells were also gamma-positive. New antibodies that

would potentially label all fetal cells and no maternal cells, would

greatly improve the outcome of immunophenotyping-based image

cytometry systems.

Most of the commercially available antibodies recognize

different epitopes and have varying amounts of non-speci®c

background staining. This makes it dif®cult to calibrate

imaging systems to differentiate automatically between speci®c

and non-speci®c backgrounds. Also, it has been shown that

processing cells for staining of globin chains requires reagents

that often contain preservatives capable of interfering with FISH

(Zheng et al., 1993), thus making subsequent genetic analysis

dif®cult. Hence, this approach has a few limitations, the most

signi®cant being the absence of a highly speci®c marker for fetal

cells.

FISH labelling of fetal cells

Another approach utilizes hybridization techniques such as FISH

for the detection of fetal cells. FISH is a quick, inexpensive,

accurate, sensitive and relatively speci®c method, that allows the

detection of the autosomal trisomies 13, 18 and 21, and X and Y

abnormalities, and any other chromosome abnormality for which

a speci®c probe is available. It is presently used as an adjunct to

conventional cytogenetic analysis, with a high detection rate and

low false-positive and false-negative rates. FISH is one of the

methods currently under investigation for the automated detection

of fetal cells. Recently developed automated image analysis

techniques for nuclei detection and FISH dot counting (Carothers,

1994; Castleman et al., 1996; Vrolijk et al., 1996; Netten et al.,

1997; Ortiz de Solorzano et al., 1998), in conjunction with the

rapid evolution in digital imaging technology and computation,

have made feasible the automation of analyses of FISH fetal cell

samples. The separated cells in enriched maternal blood are

examined for gender and genetic aneuploidy using chromosome-

speci®c DNA probes via FISH. If the fetus is male, FISH can be

used directly, with one probe targeting the Y-chromosome, and

different coloured probes for other chromosomes, to detect

aneuploidies. An automated system can examine enough cells

to locate several fetal (Y-positive) cells and then make a

determination about aneuploidy in the fetus. If the fetus is

female, a suf®cient number of cells must be analysed so as to rule

out the possibility of aneuploid fetal cells.

Automated FISH analysis

The automatic detection and scoring of FISH signals in interphase

nuclei was ®rst reported in 1997 (Netten et al., 1997). These


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authors developed an automated ¯uorescence microscope system

that could examine 500 cells in 15 min to determine the number of

dots (labelled chromosomes) in each cell nucleus. The system was

built around a Zeiss Axioskop microscope ®tted with motorized

focus, scanning stage, excitation ®lter wheels and shutter. Images

were acquired by a Photometrics KAF 1400 camera and analysed

on a Macintosh Quadra 840AV computer. This system was

designed to analyse one-colour FISH, i.e. double-labelled nuclei

(counterstain and one hybridization signal), and system perfor-

mance was compared with manual counting. The automated

system dot counting feature was reported to be biased with respect

to manual counting, with a higher variance and an error rate of

11%. A major disadvantage of this system is that it analyses only

one-colour FISH specimens.

Subsequently, a fully automated hybridization dot analyser was

developed that performed dot counting on two-colour FISH

specimens (counterstain and two hybridization signals) (Ortiz de

Solorzana et al., 1998). The system was built around a microscope

with motorized stages and ®lter wheels, and equipped with image

capture and digitization capabilities. The software incorporated

FISH image analysis algorithms reported in the literature

(Castleman et al., 1996), with the addition of one new feature

for systematic error correction. In order to avoid differences

between the `detectable' and the `actual' populations, an

internal control was implemented by using a probe of which the

copy number was known a priori. The strategy for correction

involved examination of the FISH control signals, with nuclei

whose control copy numbers were different than the expected

number being discarded. The use of control signals to discard

cells with spurious signals limits the direct applicability of this

system for fetal cell detection. It is dif®cult to pick a suitable

control signal, and removal of cells may result in missed rare fetal

cells.

More recently, an automated system was developed which was

capable of analysing microscope slides with FISH-stained

interphase nuclei in both two and three dimensions (Kozubek et

al., 1999). This can be used to analyse multi-colour FISH images

using sequential acquisition of signals from individual probes

through appropriate single band-pass excitation and emission

®lters. The system was built around a fully automated

epi¯uorescence microscope (Leica Microsystems Inc.,

Bannockburn, IL, USA), with customized software for scanning,

focusing, digitization and image analysis. Other software

capabilities include the computation of spatial distances between

the hybridized dots, and analysis of repeatedly hybridized

samples. The functionality of the system was demonstrated using

a sample for the detection of the BCR/ABL [the fusion gene

found in 95% chronic myelogeneous leukaemia (CML) patients]

translocation in interphase nuclei, and for the determination of

three-dimensional distances between centromeres in HL-60 cells.

The authors projected that the system should be able to analyse

~105 nuclei per slide at a cell density of 10 nuclei per ®eld, at

speeds of 50±100 nuclei per min and 1 to 5 nuclei per min, in two

and three dimensions respectively. Although these statistics are

suf®cient for a number of clinical tasks such as routine

diagnostics, follow-up of therapy and studies of chromatin

structure, the application of the system for rare-event detection

remains to be tested.

The advanced digital imaging research (ADIR) system for FISH

imaging

A similar system has been built by our group for the identi®cation

of fetal cells using ¯uorescence microscopy of FISH-labelled

maternal blood samples. Assembled around an Axioskop epi-

illumination microscope (Carl Zeiss Microimaging, Inc.,

Thornwood, NY, USA), the instrument is equipped with two

lampsÐa 100 W mercury lamp for ¯uorescence imaging and a 30

W halogen source for conventional light microscopy. The

microscope is ®tted with a three-axis motorized scanning stage

system (Prior Scienti®c Inc., Rockland, MA, USA) and a four-

specimen slide holder. The spatial resolution of the scanning stage

is 0.5 mm in X and Y, and 0.05 mm in the Z direction. A cooled

triple chip colour camera (Dage-MTI, Inc., Michigan City, IN,

USA) capable of on-chip integration up to 8 s and 575-line

resolution, is used in conjunction with a 24-bit frame grabber

(Scion Corporation, Frederick, ML, USA) to allow simultaneous

acquisition of all three colour channels (640348033). Triple-

band and Quad-band ®lter sets (Vysis Inc., Downers Grove, IL,

USA) are used for imaging three- and four-colour FISH samples

respectively.

The software that controls the automated microscope includes

functions for spatial and photometric calibration, automatic focus,

image scanning and digitization, background subtraction and

colour compensation, nuclei segmentation, location and measure-

ment, and FISH dot counting (Castleman et al., 1996). Cells are

located in one colour channel (e.g. blue for DAPI staining), and

dots are counted in user-selected channels and associated with the

cell to which they belong. A count of the number of dots of each

colour in the nucleus provides chromosomal enumeration. For

each slide scanned, screening results are reported as a distribution

of `dots-per-cell' for each colour.

Autofocus

The autofocus algorithm is a two-pass algorithm designed to

determine ®rst whether the ®eld in question is empty or not, and

then to bring the image into sharp focus. The ®rst pass of the

algorithm examines images at three different z-axis positions to

determine whether there is enough variation among the images to

indicate the presence of objects in the ®eld to focus on. Once the

coarse focus step is complete, a different algorithm is used to

bring the image into sharp focus. To ensure that the background

image of the nuclei (blue) and dots (red/green/aqua) are all in

sharp focus, the ®ne focus value is taken to be the sum of the ®ne

focus function output for each of the three- or four-colour

channels. This technique has been found to be more reliable for

bringing all the dots into sharp focus.

The coarse focus routine (three video frames) is followed

by the ®ne focus algorithm to ®nd the optimal focus plane

(~5±8 video frames). Autofocusing is executed at the

beginning of every scan, and may be executed for every scan

position, or at regular intervals as de®ned by the user. In our

experience, the images are `in-focus' over a relatively large area

of the slide, and frequent refocusing is not required. For an

integration time of 0.5 s, an average autofocus time of 28 6 4 s

was observed. The total time for autofocus depends upon image

content (which affects processing time), and the selected

integration time.


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Slide scanning

The automated slide scanning algorithm scans the slide in a

raster pattern. It moves vertically down the user-selected area,

retraces back to the top, moves a predetermined distance across,

and then starts another scan vertically downward. This process

continues until the entire user-de®ned area is scanned. The

step size is adjusted such that there is no overlap between

sequential scan ®elds. The system was designed to implement

slide scanning in two modes depending on the slide preparation.

A `spread' mode scans the entire slide, whereas a `cytospin'

mode2allows scanning of slides prepared using centrifugal

cytology. Both modes allow user-de®ned areas to be scanned.

The average slide-scanning rate recorded for the system is 12

images per min.

Cell detection

The digitized images are initially thresholded in the user-de®ned

cell channel (generally, blue for DAPI counterstain) to obtain

binary images of the cells. The cells are then uniquely identi®ed

using a region-labelling procedure (Merchant et al., 1993). The

number of pixels in each object is used as a measure of cell size.

Shape analysis is used to discard large cell clusters and non-

circular objects. Isolated cells are initially evaluated to determine

gender and/or aneuploidy, and ®nally classi®ed as fetal or

maternal cells. The stage and image co-ordinates of fetal cells

are stored in a table along with each cell's morphological features

(area, shape factor and dot count). The detected cells can be

automatically re-located later using the previously stored stage

and image co-ordinates.

Figure 1. Colour compensation applied to an image of a FISH-labelled lymphocyte. The nuclei is counterstained with DAPI, and dots are labelled with red(chromosome 21) and green (chromosome 13). (A) Original image and individual red (R), green (G), and blue (B) channels. The blue ¯uorophore (nuclear stain) isseen in both the red and green channels. Similarly, green dots are also seen in the red channel and vice versa. (B) Intensity pro®le of a line segment through a redand green dot (white arrows in original image). (C, D) Colour- compensated images. The smearing of colours across the individual channels is effectively removed.Original magni®cation = 3630.


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Colour compensation

Some of the most troublesome problems encountered in the

automatic identi®cation of cells and dots results from the fact that

all three ¯uorophores appear in all three colour channels due to

the unavoidable overlap among ¯uorophore emission spectra and

camera sensitivity spectra. The result is that the red dye shows up

in the green and blue channel images, and the green and blue dyes

are also smeared across all three colour channels. Figure 1

demonstrates this effect on a FISH-labelled lymphocyte. The

nucleus was counterstained with DAPI, and chromosomes 21 and

13 are labelled using SpectrumOrange (loci D21S259, D21S341,

Figure 2. (A) Deblurring FISH-labelled lymphocytes. The left three images are from an optical section stack taken 1 mm apart. The right-hand image is the centreimage deblurred. The in-plane dots are brighter, while the out-of-plane dots have been removed. (B) Fusing FISH-labelled lymphocytes. The left three images arefrom a deblurred optical section stack taken 1 mm apart. The right-hand image is the fusion of the three using the Biorthogonal 2,2 wavelet transform. Note that thefused image has all of the dots in focus. Original magni®cation = 3630.

Figure 3. (A) Fluorescence image of ®ve female (XX) cells and one male (XY) cell. Adult female blood was mixed with male blood (100:1 dilution) and processedvia FISH. Cells are counterstained blue (DAPI); X chromosomes are labelled in red (TRITC); the Y chromosome is labelled in green (FITC). (B) Results ofautomated image analysis. As illustrated here, the software accurately detects single cells, separates touching cells, and detects the red and green dots in individualcells. Original magni®cation = 3630.


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D21S342; 21q22.13-q22.2) and SpectrumGreen (RB1; 13q14)

(Vysis Inc.). An intensity pro®le of the line connecting the arrows

in Figure 1A is presented in Figure 1B. A process has been

developed that effectively isolates the three ¯uorophores by

separating them into three colour channels (RGB) of the digitized

image (Castleman, 1994). The method, which can account for

background level and unequal integration times, is applied to all

images prior to dot counting. Figure 1C and 1D demonstrate the

application of this technique to a peripheral blood cell

(lymphocyte) processed for FISH.

Dot counting

Hybridization dots are located by thresholding in the probe colour

channels. A minimum size criterion is used to eliminate noise

spikes, and shape analysis is used to ¯ag non-compact dots. The

location of dots found is compared with the cell masks to

associate each chromosomal dot with its corresponding cell.

Finally, a statistical model is used to determine unbiased

estimates of the proportion of cells having each number of dots.

The befuddlement theory (Castleman and White, 1995) provides

guidelines for dot counting algorithm development by establish-

ing the point at which further reduction of dot counting errors will

not additionally improve the estimate. This occurs when statistical

sampling error outweighs dot-counting error.

Handling thick specimens

Automated microscope instruments almost uniformly carry out

their analysis on two-dimensional images, and their ability to

handle thick specimens is severely limited. In thick specimen

preparations, FISH dots may not all occur at the same depth in the

focused nuclei. Also, the presence of artefacts at times may result

in a highly defocused image of cells. While automatic focusing is

suf®cient for detecting nuclei and FISH signals, in the case of

thick specimens the FISH dots that fall above or below the focal

plane can be lost. A technique has been developed by the present

authors to enhance the image content available in microscope

images of thick sections by performing optical section deblurring,

followed by image fusion using wavelet transforms.

Originally, the deblurring of optical section images was

performed using a simple method that involved subtracting

adjacent plane images which had been blurred with an appropriate

defocus point spread function (Weinstein and Castleman, 1971).

While this technique could not recover the specimen function

exactly, it did improve optical section images at reasonable

computational expense. The deblurred section images could then

be combined in the wavelet transform domain by taking the

maximum-amplitude coef®cient at each co-ordinate. An inverse

wavelet transform of the resulting coef®cients then reconstructed

the fused image (Lu and Healy, 1994; Lu et al., 1994). It was

found that deblurring prior to wavelet fusion signi®cantly

improved the measured sharpness of the processed images.

Figure 2A presents colour images of FISH-labelled lymphocytes

that were acquired at focus positions 1 mm apart and deblurred.

An example of wavelet image fusion is shown in Figure 2B.

Optical section deblurring followed by image fusion produced an

image in which all of the dots are visible.

System performance

Arti®cial samples of female blood spiked with male blood

(emulating speci®c fetal cell to maternal cell ratios) were

processed using the prototype automated imaging system to

investigate the feasibility of automated detection. The samples

(provided by Dr Laird Jackson, MCP Hahnemann University,

Philadelphia, PA, USA) were prepared for FISH using DAPI as

the counterstain, with X and Y chromosomes labelled with

tetramethylrhodamine B isothiocyanate (TRITC) and FITC

respectively. An example illustrating the operation of the

automated cell detection and dot-counting algorithm is presented

in Figure 3. Figure 3A shows a ¯uorescence image of ®ve female

(XX) cells and one male cell. The specimen was obtained from

female blood mixed with male blood (100:1 dilution), and

processed via FISH. The results of automated image analysis are

illustrated in Figure 3B. The software accurately: (i) detects single

cells; (ii) separates touching cells; and (iii) detects the red and

green dots in the isolated cells.

Automated detection of male cells (XY, one red dot, one green

dot) from surrounding female cells (XX, two red dots) was

performed for four different dilutions of male to female cells,

namely 1:10, 1: 100 and 1:500 and 1:1000. Table I presents a

direct comparison of automated dot counting with manual dot

Table I. Comparison of automated dot counting with manual inspectiona

Parameter evaluated

Manual

inspection

Automated

analysis

Area inspected (mm2) 6 6

Total time elapsed (min) 90 44

Number of ®elds 252 252

Fields/min 3 6

Total cells counted (n) 957 938

Cells with zero dots (n) 41 80

Female cells (XX, 2 red signal) (n) 628 497

Male cells (XY,1 red, 1 green signal) (n) 6 6

>2 X and 0 Y (n) 117 82

>1 X and >1 Y (n) 9 6

Other (clusters, debris, etc.) (n) 156 265

Ratio of XY:XX 1:105 1:83

aA sample of 1:100 dilution of male to female cells was processed using theprototype automated imaging system. The images thus obtained were storedand evaluated manually. The automated dot-counting algorithm detects cellsand counts cells with a correlation coef®cient of 0.75, when compared withmanual inspection.

Table II. Automated cell detectiona

Dilution Data from automated cell detection

Total cells (n) Time (min) XY:XX ratio

1:10 2905 52 1:9

1:100 938 44 1:83

1:500 2071 43 1:534

1:1000 2472 67 1:652

aFour varying dilutions were used (1:10, 1:100, 1:500 and 1:1000) of malecells mixed with female cells, and samples were evaluated using theautomated imaging system. Results indicate that the instrument was able todetect the male cells in all four samples processed.


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counting. A sample of 1:100 dilution of male to female cells was

processed using the prototype automated imaging system. The

images thus obtained were stored and evaluated manually. As

shown in Table I, the automated dot counting algorithm detects

cells and counts cells with a correlation coef®cient of 0.75, when

compared with manual inspection. In order to evaluate the ability

of this instrument to detect rare cells, four dilutions were used

(1:10, 1:100, 1:500 and 1:1000) of male cells mixed with female

cells. The results of that study are presented in Table II. Male

cells could be detected in both the lower and higher dilutions.

Since the ratio of 1:1000 closely mimics the actual proportion of

fetal cells recovered after enrichment in maternal blood (Steele et

al., 1996), the automated system should be able to detect fetal

cells in maternal blood at enrichment ratios in the range of 2±5

cells per 3000 maternal cells. A manual evaluation of 3000 cells

takes approximately 3±4 h. As seen in Table II, the automated

system should be able to accomplish the same task in

approximately half the time. These results suggest feasibility for

development of chromosome-based (FISH) detection of nucleated

fetal cells found in the maternal circulation. Further re®nements

in both instrumentation and slide preparations are currently in

progress.

Limitations of FISH

There are certain drawbacks inherent in using the ¯uorescence

imaging approach for fetal cell detection: (i) the inherently low

light levels place limits on how quickly the system can operate;

(ii) depth-of-®eld is a limitation, with small dots distributed inside

ellipsoidal interphase cells; (iii) auto¯uorescence and photo-

bleaching can affect performance; and (iv) the system is more

expensive because ¯uorescence lamps are costly (life of

approximately 200 h).

Furthermore, in FISH samples the hybridization and detection

ef®ciency, as well as the geometry or chromosomal location

within the nucleus, directly in¯uences the enumeration of

chromosomes. In any population of euploid cells, although

~90% of the cells display two signals, a number of cells appear

to have either one, three or four signals. Thus, a large number of

cells must be analysed in order to obtain statistically signi®cant

data on dot counts.

Transmitted-light imaging of fetal cells

An alternative approach is to use transmitted light imaging to

detect the fetal cells automatically, thereby avoiding the

complexities of ¯uorescence imaging, and providing a faster,

and less expensive technique for fetal cell detection. One

approach describing the use of transmitted-light (bright®eld)

microscopy for the detection of fetal cells has been reported

(Takabayashi et al., 1995). This study demonstrated that nRBCs

may be isolated from maternal blood using a simple Percoll

discontinuous density gradient centrifugation method. The

enriched maternal blood is subjected to a May±Giemsa staining

protocol, and nRBCs may be microscopically identi®ed from the

surrounding maternal cells based on distinct morphological

features speci®c to the nucleated erythrocytes. Sensitive and

speci®c analytical methods such as PCR and FISH may then be

applied to perform genetic testing for gender and aneuploidy.

The ADIR system for bright®eld imaging

A study was performed to evaluate the feasibility of using

morphological descriptors for the detection of fetal cells from

maternal blood. The automated screening process used trans-

mitted light (not ¯uorescence) for cell ®nding. Visual analysis and

FISH dot-counting were then used to perform genetic testing.

Maternal blood enrichment was performed using the Percoll

discontinuous density gradient separation protocol described

previously (Takabayashi et al., 1995). Peripheral blood from

pregnant women at between 10 and 27 weeks gestation was

collected serially prior to invasive prenatal diagnostic procedures

(either chorionic villus sampling or amniocentesis). Following

centrifugation at 4003g for 30 min, the granulocytes with nRBCs

separated at the interface of 1.075 and 1.085 g/ml Percoll

gradients. The enriched maternal blood was then processed using

the May±Giemsa staining protocol, and slides were examined

microscopically using transmitted light.

Following minor modi®cations, the prototype instrument

described earlier (for FISH imaging) was used to perform

automated detection of rare nRBCs in maternal blood. Since in

bright®eld, no integration time is required, an average autofocus

time of 1864 s was recorded, with a slide-scanning rate of 17 6 1

images per min.

Image analysis

A representative image showing a nRBC and the surrounding

maternal cells is presented in Figure 4. The nRBC appears to be

morphologically distinct from the surrounding maternal granulo-

cytes and mononuclear cells. The rare nRBCs are characterized as

small cells with pyknotic nucleus and relatively little cytoplasm

(Takabayashi et al., 1995). The nucleus appears as a dense

circular mass surrounded by clear cytoplasmic material, and is

smaller than that of the surrounding granulocytes. nRBCs are

distinguished from the surrounding maternal granulocytes based

on three unique morphological features (size, shape and density)

computed from images digitized in transmitted light.

An automated classi®cation algorithm was implemented to

detect nRBCs by employing a series of relatively simple pattern

recognition routines. The digitized images were initially thre-

Figure 4. Bright®eld images showing a nucleated red blood cells (nRBC,indicated by arrow), isolated using a Percoll density gradient centrifugationtechnique. The image was obtained using a Zeiss AchroPlan 363 objective,NA 0.95.


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sholded to obtain binary images of cells. The cells were then

uniquely identi®ed using a region-labelling procedure as before.

The density of the nucleus was computed as the average optical

density (AOD), the size of the nucleus was taken as cell area, and

the circularity of nucleus was quanti®ed by the shape factor using

digital image analysis.

System performance

A blinded study was conducted using seven maternal blood

samples. The slides were analysed automatically by our group and

manually at Dr Bischoff's laboratory (Baylor College of

Medicine, Houston, TX). The total number of ®elds analysed

per slide ranged from 7500 to 75 000. Of the total cells scored, the

automated system detected 25% as nRBCs. Upon manual

examination, 3% of the automatically detected cells were found

to be false-positives, and 1.5% of the nRBCs were missed by the

automated system. The locations of the detected nRBCs were

recorded and stored for each individual slide.

When the cells had been detected via light microscopy and

their location recorded, they were hybridized for FISH. The

detected and hybridized nRBCs were then relocated and tested for

gender using ¯uorescence microscopy. The system can accurately

relocate in both the X and Y directions to an accuracy of 61 mm.

Results

A total of seven samples was analysed, but subsequent FISH

analysis was possible on only four samples. The results are

summarized in Table III. The number of nRBCs identi®ed varied

for the different samples, ranging from 5 to 327 cells. The ratio of

nRBCs to maternal cells varied from 1:4000 to 1:40 000; hence,

there was an ~10-fold variation in the number of nRBCs

identi®ed.

Initially, it was believed that all nRBCs isolated from a

maternal sample were fetal in origin. This assumption was made

because of reports (Pembrey et al., 1973) that nRBCs were rarely

seen in peripheral blood samples from pregnant women. The more

recent and more sensitive techniques of enrichment used to detect

fetal cells have now uncovered a previously under-appreciated

population of maternal nRBCs that circulate during pregnancy

(Slunga-Tallberg et al., 1995).

There are several factors that in¯uence the number of nRBCs

present in maternal blood, including gestation age, loss of cells

during enrichment, aneuploidy status of the fetus and pre-

eclampsia. Other factors likely to in¯uence the number of fetal

cells present in the mother include blood group incompatibility

and prior sensitization, and whether or not an invasive procedure

has been previously performed. So far, several estimates have

been reported for the number of nucleated cells in maternal blood.

One group (Hall and Williams, 1992) estimated that the ratio of

fetal nucleated cells to maternal nucleated cells ranged from

1:4.753106 to 1:1.63107. Others (Hamada et al., 1993) estimated

the ratio of fetal nucleated cells to maternal nucleated cells; fetal

cell frequencies in successive trimesters were 0.27, 3.52 and

8.563105 respectively. Subsequent to FISH analysis, it was found

that 50% of the slides (two out of four could not be FISH-

labelled) had fnRBCs, and the fetal gender was accurately

identi®ed in all cells found. The ratio of fnRBCs to that of

maternal nRBCs in those samples was approximately 1:75 and

1:175.

However, out of the seven specimen slides processed for FISH

following automated detection of nRBCs, FISH signals were

detected in only four slides (57%). Also, the quality and quantity

of the FISH signals varied for each of the specimen slides

processed (Table IV). In addition, FISH signals were observed in

only 22 6 4% of the total nRBCs found. Although other

mononuclear cells in surrounding regions of the nRBCs showed

FISH signals, the appearance of FISH signals in the nRBCs

varied. Thus, it was concluded that the FISH protocol was

functioning, and the variability existed only in the nRBCs. Some

Table IV. Percentage FISH signals observed in the detected nRBCs

Slide Total Total nRBCs with Cells with X

nRBCs FISH signals and Y

R0743 77 20 (26) 1

R0943 327 55 (17) 2

R0391 18 4 (22) 0

R0578 33 7 (21) 0

Values in parentheses are percentages.

Table III. Automated detection of NRBCs followed by FISH to detect male fetal cells

Slide Total area Total Total Total Total Amnio (A)/ FISH cells

scanned ®eldsa time cellsb nRBCs CVS (C) w/XY

(cm2) (h) results fnRBCs

R0743 6 75 000 72 1 575 000 77 (A) 46 XY 1

R0943 5.36 67 000 65 1 423 750 327 (C) 46 XY 2

R0391 2.72 34 000 30 722 500 18 (C) 46 XY 0

R0578 0.6 7600 8 202 500 33 (A) 46 XY 0

and 46 XXc

R0567 1.2 15 000 15 275 400 5 (A) 46 XY NA

R0576 1.0 12 000 12 244 800 10 (A) 46 XY NA

R0460 0.5 6500 6 110 500 6 (C) 46 XY NA

a~10±15% of ®elds had less than three cells.b~25 cells/®eld scored.cAmniocentesis analysis for specimen R0578 reported twinsNA = FISH data not available.


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nRBCs showed compact nucleus and FISH signals, while others

showed a disintegrated nucleus, and almost 70% of the nRBCs

showed no FISH signals (even though neighbouring cells showed

FISH). Figure 5 shows images demonstrating the variability of the

FISH signal observed, and which can be explained as follows.

Although quantitative assays based on PCR have shown that as

many as one fetal cell/ml of maternal blood exists (Bianchi et al.,

1997), FISH detects far fewer fetal cells. In fact, in general only

two to four fetal cells are recovered from 20±30 ml of maternal

blood. Until now, most protocols have targeted the fnRBC;

however, recent studies have now shown that most of these cells

are at late stages of differentiation and undergoing apoptosis

(Sekizawa et al., 2000; Hristoskova et al., 2001; Kolialexi et al.,

2001) and, consequently, unsuitable for FISH analysis.

It is speculated that the nRBCs identi®ed on three slides (out of

seven) were unsuitable for FISH due to processing dif®culties (a

long interval between the original Giemsa staining and subse-

quent FISH), but in the remaining four slides the nRBCs were at

various stages of differentiation, including the stage of terminal

erythroid differentiation and enucleation. These dramatic mor-

phological changes reduce the suitability of the cells for FISH

analysis. Thus, insuf®cient FISH data were available to con®rm

how many of the detected nRBCs were of fetal origin.

Consequently, only parameters such as false- negative/positive

rates for fetal cell detection could be determined statistically.

Overall, these results clearly demonstrate that nRBCs can be

found automatically with a 3% false-positive rate, and 98%

sensitivity using transmitted light microscopy. FISH analysis of

Figure 5. Images of cells stained with May±Giemsa, and subsequently hybridized for FISH. (A) A nRBC following FISH; (B) a nRBC following FISH; however,the nucleus appears to be disintegrating in this case; (C) a bright®eld image of a nRBC; (D) a FISH image of the nRBC in (C). Although neighbouring cells showFISH signals, the nRBC shows no FISH staining. These images demonstrate the variability observed when processing nRBCs for FISH. Originalmagni®cation = 3630. X and Y = FISH signals for X and Y chromosomes respectively.


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the nRBCs is, however, largely variable, being dependent on the

state of differentiation of the erythroid cells.

These results, together with those of other studies, have now

shown that these cells may not be the ideal target cell for

enrichment, given that most are in late stages of differentiation

and are undergoing apoptosis (Sekizawa et al., 2000; Hristoskova

et al., 2001; Kolialexi et al., 2001). An alternative approach might

be to evaluate the potential for fetal cell analysis of progenitor

cells from whole blood (Little et al., 1997).

Conclusions

Although fetal cells are known to exist in the maternal blood

circulation, they are few in number and are not detected in every

case. This is primarily because the recovered cell population is

not analysed in its entirety. As is frequently observed with

separation protocols, many thousands of cells are recovered

following enrichment. Following FISH, however, only a small

fraction of these cells can be scored. In addition, due to individual

bias at the time of scoring, FISH results may be inconsistent,

requiring enforcement of strict scoring criteria. However, this is

dif®cult to achieve across different laboratories. Thus, it is clear

that an unbiased analysis of almost all of the recovered cells is

needed, and this can be achieved by using automated image

cytometry.

Three strategies for the automated detection of fetal cells were

discussed. Each approach exploits an independent feature of fetal

cells (immunophenotype, genetic constitution or morphology) for

its detection, and each offers different advantages and limitations.

Unfortunately, none of these approaches has been extensively

tested for the detection of fetal cells in maternal blood samples,

thus making it dif®cult to judge their ef®cacies.

Immunophenotyping

The immunophenotyping approach using antibodies for fetal

haemoglobin has the advantage of scanning specimens at higher

speeds using transmitted light imaging for the detection of HbF-

positive cells. However, a second scan is typically required in this

approach for genetic analysis using FISH. A key disadvantage of

this approach is that a speci®c marker for fetal haemoglobin is

lacking at the present time.

The Applied Imaging system is a multi-mode technique that

was developed to acquire transmitted light and ¯uorescence

images simultaneously, but it encountered problems regarding an

overlap between the DAPI (nuclear) and FITC (HbF/cytoplasm)

stained areas.

The scanning cytometer (Bajaj et al., 2000) provides faster

focusing and segmentation algorithms, but requires manual

interaction for ®lter changes; this makes the system only semi-

automated, which is not practical for routine screening. The

application of this system to clinical samples of peripheral

maternal blood would provide more meaningful data regarding its

potential for fetal cell diagnosis.

FISH

FISH for the detection of fetal cells is relatively straightforward

when the fetus is male, whereas in the case of a female fetus,

detection is only possible if the fetus has a chromosomal

aneuploidy. Several systems for automated FISH dot counting

have been developed, and a considerable amount of published

data exists regarding FISH image analysis, and the statistical

interpretation of FISH data. These studies have also been

successfully incorporated into semi- or fully automated imaging

systems (Ravkin and Temov, 1998; Kozubek et al., 1999).

However, none of the automated systems developed to date has

been extensively tested to determine the clinical feasibility of

automatically detecting fetal cells from maternal blood samples.

The FISH-based system developed by the present authors' group

is in the ®nal development stage, and is scheduled for installation

at MCP Hahnemann University, PA, USA (Dr Laird Jackson's

Laboratory) for clinical testing.

The key limitation is that scanning thousands of cells using

¯uorescence techniques is not practical for routine cytogenetic

analysis. Furthermore, the reliability of results is heavily

dependent on slide quality. Some of the factors responsible for

errors in FISH dot counting include: (i) overlapping nuclei; (ii)

poor microscopic resolution of contiguous signals; (iii) split

signals; (iv) touching cells with overlap; and (v) spatial location

of signals (a dot that is unclear in one plane may become clear on

refocusing in another plane). There are numerous image analysis

and statistical methods that can be applied to address these issues

(Carothers, 1994; Castleman, 1994; Castleman and White, 1995;

Castleman et al., 1996; Netten et al., 1997). However, extensive

implementation of such correction methods tends to increase the

sample processing time, whereas discarding cells with spurious

signals may result in missing the rare fetal cells. Most automated

systems based on the FISH approach try to balance the use of

correction methods versus simply discarding problematic cells to

achieve reasonable slide-scanning rates. Hence, an extensive

evaluation of this approach is required to determine its clinical

feasibility. Employing the approach of isolating every object or

cluster of objects that have a Y-signal, irrespective of its

morphological integrity would increase the odds of ®nding a

fetal cell in the case of a male fetus.

Morphology

Finally, the use of morphological descriptors is highly

promising because it does not need speci®c fetal cell markers,

and also has the advantage of reduced scanning times.

Currently, its major limitation is the target cell type. Despite

the presence of unique morphological descriptors, most nRBCs

are in various stages of differentiation and are thus not robust

for FISH analysis. PCR can be used instead of FISH, but

there is some concern over allele-dropout and PCR ef®ciency

using the nRBCs. In addition, a signi®cant number of

separated nRBCs are maternal, so the only mutations or

genes that can be detected with PCR must be paternal in

origin. Recently, culturing erythroid progenitor cells from

maternal blood has been considered as a potential alternative

method to increase the number of target fetal cells available

for genetic analysis (Little et al., 1997). However, culturing

techniques designed to stimulate fetal cells have so far proved

to be unsuccessful (Manotaya et al., 2002; Zimmermann et al.,

2002). One approach is to use progenitor cells to increase the

number of target fetal cells available for genetic analysis via

FISH. Fetal progenitor cells can then be examined to evaluate

any characteristic morphological features and, based on these


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structural descriptors, transmitted light automated imaging can

be implemented.

Summary

There is a pressing need for automated microscopy for the

detection of fetal cells. Automation is not only required for the

eventual clinical realization of fetal cell diagnosis using maternal

blood, but is also critical for research progress to be made in the

®eld. The current status of automation in fetal cell screening is

that image cytometry-based automated approaches have trans-

cended the investigational and development stage. A systematic

application of these approaches to the actual detection of fetal

cells from maternal blood is impending. This will provide the

statistical data required to determine whether or not imaging

cytometry can provide a practical solution to the problem of non-

invasive fetal cell screening. Moreover, irrespective of the

eventual status of clinical non-invasive fetal cell screening, the

incorporation of automation in current research is critical in order

to achieve any breakthrough technological advances.

Commercialization

Unfortunately, an automated microscopy system especially for

fetal cell screening is not commercially available at the present

time, although we are currently in the process of completing the

development and testing of an automated microscopy system for

transmitted light and ¯uorescence microscopy. Several of the

systems discussed in this review have been developed at academic

institutions and have not (yet) been commercialized. Applied

Imaging Corporation (Santa Clara, CA, USA) reported the

development of a system in 1998, but its current commercializa-

tion status is not known. CompuCyte LSC (CompuCyte Corp.,

Cambridge, MA, USA) sells a laser scanning cytometer

(Kamentsky et al., 1997a,b). Laser scanning cytometry (LSC)

uses lasers to excite ¯uorochromes in cellular specimens and

detects the ¯uorescence in discrete wavelengths with multiple

photomultiplier tubes. LSC, like ¯ow cytometry, yields high-

content stoichiometric data on heterogeneous populations of large

numbers of cells, and offers software analysis tools to obtain

statistical data on the populations. It can be used to count and

measure the ¯uorescence of FISH probe spots, or to navigate

around a segmented nucleus in order to identify cytoplasmic

¯uorescence and distinguish it from nuclear ¯uorescence. The

resolution of LSC is relatively low, and only two-dimensional

studies are possible; furthermore, the use of lasers makes the

equipment expensive. Most importantly, this system has not found

wide use in the area of clinical cytogenetics, and its applicability

to the problem of fetal cell detection is presently unknown. Most

recently, MetaSystems (Altlussheim, Germany) introduced a

high-throughput scanning system which is currently being

evaluated for the detection of fetal cells (Plesch and Loerch,

2001).

Future work

Several key issues related to the detection of fetal cells from

maternal blood have yet to be addressed. First, can the fetal cell

detection rate be improved by scoring most or all cells recovered

following enrichment? Second, can we reduce false-positive rates

by removing observer bias? Third, do fetal progenitor cells persist

from prior pregnancies? Automation may play a signi®cant role in

addressing these issues.

Acknowledgements

The authors thank Dr Laird Jackson from MCP Hahnemann University,Philadelphia, PA, USA for providing FISH specimens, and Dr FaridehBischoff from Baylor College of Medicine, Houston, TX, USA for providingMay±Giemsa-stained specimens. The development of the bright®eld approachfor automated microscopy was supported by NIH SBIR Grant No. 1R43HD37308-01, and the development of the FISH-based automated microscopysystem is supported by NIH SBIR Grant No. 2R44 HD34719-02.

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