Sci. Aging Knowl. Environ., 9 January 2002
Vol. 2002, Issue 1, p. re1
[DOI: 10.1126/sageke.2002.1.re1]


Single-Cell Antisense RNA Amplification and Microarray Analysis as a Tool for Studying Neurological Degeneration and Restoration

Max B. Kelz, Gersham W. Dent, Stavros Therianos, Paolo G. Marciano, Tracy K. McIntosh, Paul D. Coleman, and James H. Eberwine

M. B. Kelz, G. W. Dent, T. K. McIntosh, and J. H. Eberwine are in the Department of Pharmacology; M. B. Kelz is in the Department of Anesthesiology; and P. G. Marciano and T. K. McIntosh are in the Department of Neurosurgery at the University of Pennsylvania Medical School, Philadelphia, PA 19104, USA. S. Therianos and P. D. Coleman are at the Center on Aging and Developmental Biology at the University of Rochester Medical Center, Rochester, NY 14642, USA. E-mail: eberwine{at} (J.H.E.);2002/1/re1

Key Words: antisense RNA • microarray • Alzheimer's • Huntington's disease • stem cells • neurodegenerative

Abstract: Neurodegenerative diseases typically affect subpopulations of neurons. Characterizing these vulnerable cells and identifying the factors that make them susceptible to damage while neighboring cells remain resistant are essential to the understanding of molecular pathogenesis that underlies neurodegenerative diseases. Classically, molecular analysis of the central nervous system involves the identification and isolation of an anatomic region of interest; next, the relevant tissue is pulverized, and the resulting homogenate is analyzed. Although this method provides useful data, its effectiveness diminishes when used in areas of high cellular diversity or in instances in which one cell type is lost as a consequence of selective cell death or quiescence. A technique that affords the ability to assess molecular events in a very precise anatomical site would provide a powerful tool for this research discipline. In this review, we discuss the amplification of messenger RNA from single neural cells and the subsequent use of the RNA to probe DNA microarrays in an effort to create cell-specific molecular profiles. Specifically, recent work in single-cell expression profiling in Alzheimer's and Huntington's diseases is discussed. We also review some new work with neural stem cells and their application to restorative neurobiology. Finally, we discuss the use of cell-specific molecular profiles to better understand the basics of neuronal cell biology.

Introduction Back to Top

In order to characterize mRNAs isolated from small amounts of brain tissue, the RNA must be amplified to concentrations necessary for analysis with currently available techniques. There are two well-accepted nucleic acid amplification procedures that are used to detect the presence of mRNAs in a sample: the polymerase chain reaction (PCR) (1) and the antisense RNA (aRNA) amplification procedure (2).

PCR is a straightforward technique for rapidly amplifying large quantities of complementary DNA (cDNA) from relatively small quantities of poly(A)+ mRNA (2). It consists of an initial reverse transcription reaction followed by a series of amplification reactions that use pairs of oligonucleotide primers and TAQ DNA polymerase. With the choice of appropriate primer pairs, PCR has been used on single cells to determine the presence or absence of a single type of mRNA or of several mRNAs simultaneously. However, PCR leads to the exponential amplification of the mRNAs in the sample. Therefore, any variation in the cDNA synthesis rate for different mRNAs or any aberration in the concentration of an individual mRNA that occurs in any step of the PCR is exponentially amplified in subsequent PCR steps. This skews the relative abundances of the mRNAs compared to the abundances that were initially present in the starting single cell (or tissue extract). In addition, with successive PCR cycles, high-abundance mRNAs outcompete low- and medium-abundance mRNAs for the primer pairs. Consequently, after 30 to 40 amplification cycles, the complexity of the final cDNA population can be lower than that of the initial pool (3).

An alternative to PCR is antisense RNA (aRNA) amplification (3, 4). Like PCR, it may be used to produce large quantities of amplified products from the mRNA contained within a single cell. Because the aRNA method is a linear amplification procedure, not exponential as PCR is, the population of amplification products generated by this protocol better represents the relative abundances of mRNAs present in the initial mRNA pool. The linearity of the aRNA procedure has been examined by several groups (4-7) using both microarray and Northern analysis. Through two rounds of amplification there doesn't appear to be skewing of the abundances of the individual aRNAs present in the aRNA population when compared with the abundances present in the starting RNA sample. Because single cells don't contain enough RNA to do Northern (RNA-DNA) hybridization, these linearity analyses were performed on microgram quantities of RNA isolated from tissues and cells. The aRNA procedure can be used on live cells, tissue homogenates, or fixed tissues such as brain slices (8).

The aRNA procedure is depicted in Fig. 1. First, the poly(A)+ mRNA in the sample is converted into cDNA by in situ transcription (9), which is performed on tissue that is affixed to a coverslip or culture plate. cDNA synthesis is primed with an oligo-dT-T7 primer, in which the oligo-dT acts as a primer on the poly(A) tail of the mRNA. The T7 RNA polymerase promoter region is part of the primer, so that after cDNA synthesis takes place, the promoter can be used to direct the synthesis of RNA from this cDNA-extended oligonucleotide. The product of the cDNA synthesis is an RNA-DNA hybrid. The tissue or cellular region of interest is then microdissected from the coverslip or culture plate and transferred to a microcentrifuge tube, where the subsequent reactions occur. After heat denaturation of the heteroduplex, double-stranded DNA (dsDNA) is created via hairpin loop-primed second-strand synthesis with Escherichia coli DNA polymerase I. The hairpin loops are then degraded with S1 nuclease, yielding dsDNA that contains an active T7 promoter. Using the dsDNA as a template, aRNA is then synthesized by the addition of T7 RNA polymerase, thus preserving the initial relative abundances of mRNAs present in the sample. This procedure can yield >1000-fold amplification of the initial mRNA population in a single cell.

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Fig. 1. Schematic depiction of brain section fixed and mounted on a slide. The section may be stained by immunohistochemistry (to visualize a particular protein in cells) or with Nissl (to identify cells in general) to identify a single cell, several cells, or an entire region of interest. In situ transcription of mRNA into cDNA occurs on the slide using a T7 oligo-dT primer (orange). After creating DNA (red)-RNA (green) hybrids within every cell, the tissue or cellular region of interest is microdissected and transferred to a test tube, where the cDNA-mRNA hybrids are denatured and converted to dsDNA by hairpin loop-primed DNA synthesis with E. coli DNA polymerase I. The first round (1st) of aRNA amplification (~1000-fold) occurs with the addition of T7 RNA polymerase. If [32P]CTP is used in a separate tracer reaction, the first round of amplified products is radioactive (yellow) and may be analyzed on a denaturing RNA gel to confirm the efficiency of the aRNA reaction. The second (2nd) amplification reaction continues with the use of random hexamers to prime the reverse transcription of antisense RNA into cDNA (1st strand synthesis). dsDNA (2nd strand syn.) is again created by the addition of DNA polymerase I. Finally, the second round of RNA amplification (T7 re-amplification) ensues with the addition of the T7 RNA polmerase. If [32P]CTP is used during the second round of RNA synthesis, the resulting radioactive products are abundant enough to be used as probes on macroarrays. For use in microarrays, fluorescent labels (typically Cy3, green; and Cy5, red) are used to monitor hybridization. The aRNA probes can be labeled directly by incorporating fluorescently labeled nucleotides into the aRNA product. Or biotin-labeled nucleotides can be incorporated into the aRNA, and flurorescently labeled avidin could be used to detect the biotin after hybridization of the probe to an array.

For subsequent analyses, a second cycle of amplification is usually required to generate sufficient quantities of ribonucleic acid from a single cell. For the second cycle, the aRNA is reverse transcribed using random hexanucleotide primers and reverse transcriptase (Fig. 1). The products of this reaction are then denatured, and dsDNA is generated with the oligo-dT-T7 primer to initiate second-strand cDNA synthesis. Once again, T7 RNA polymerase may be added to yield an additional 1000-fold amplification of the starting aRNA. After two such cycles, the initial pool of mRNA will have been amplified by up to six orders of magnitude (10), with the quantities of low-, medium-, and high-abundance mRNA species in the same relative concentrations as were present in the cell or tissue sample. Such quantities of aRNA are sufficient for use as probes in macro- or microarray analyses (8) (see below).

The most common problem encountered when first using the aRNA protocol is to have no aRNA at the end of the procedure. This can be caused by one of the following problems in the first cycle of amplification: (i) loss of RNA from the initial sample, (ii) poor reverse transcription, or (iii) too much S1 nuclease digestion. These hazards can be minimized by taking extra caution in handling the initial RNA sample and by doing test reactions for each step of the aRNA procedure as one is going through it. For example, during cDNA synthesis, a small amount of the reaction mixture can be removed and mixed with radioactive deoxynucleotide triphosphates (dNTPS), and, at the end of the reaction, the radiolabled RNA can be analyzed by denaturing gel electrophoresis to determine the amount and size distribution of the radiolabeled RNA (3). Strand-displacement second-strand cDNA synthesis using ribonuclease (RNAse) H and DNA polymerase can also be used as opposed to hairpin loop self-priming and S1 nuclease digestion (3). In the second cycle of amplification, the most common problem is the addition of too many random primers during the conversion of aRNA into cDNA. With too many primers, the cDNA will be very short; this is because reverse transcriptase doesn't have 5'-to-3' exonuclease activity and thus cannot digest any cDNA that is synthesized in front of lagging DNA polymerases that initiate cDNA synthesis 5' to the 3'-most primer. This problem can be remedied by titrating the amount of random primers needed to generate appropriately sized cDNAs, and optimal reaction conditions will yield cDNAs between 1000 and 5000 bases in length.

Although not directly related to the aRNA amplification procedure, the first step for many researchers is deciding what procedure to use for harvesting mRNA from the sample of interest. The first single-cell mRNA harvesting procedure that was developed used a microelectrode to spear a single live neuron; the mRNA was then aspirated into the microelectrode and transferred to a microcentrifuge tube (2, 4). This procedure works well and requires only a microscope, a micromanipulator, a steady hand, and experience. The technique has been used successfully on live cells as well as fixed tissue sections. More recently, laser capture microdissection (LCM) has been used to harvest nucleic acids from tissue sections. Although LCM has been very useful in the isolation of DNA from tissue sections, the isolation of mRNA has been more problematic. Some groups have been highly successful with the methodology (7), whereas others have fared less well. As with all procedures, there are technical hurdles that one can become familiar with only from experience, and the clearing of these hurdles should facilitate the more widespread use of LCM. One of the difficulties with LCM is the use of a plastic cap, which is melted by the laser that permeates the tissue being examined. After melting, the cap is lifted, bringing the tissue and mRNA with it. The mRNA is then removed from the cap by chemical procedures. The use of the cap procedure requires that the tissue not be fixed, and this demand increases the likelihood of contamination. Fortunately, there is a new generation of LCM systems that doesn't use plastic caps, and this innovation promises to make this technology more user friendly. LCM-harvested RNA has been used to generate aRNA probes for screening microarrays (7).

aRNA and Microarray Analysis Back to Top

Recent advances in microarray technology (also known as gene chip or DNA chip analysis) have enabled previously unimaginable parallel processing of biological data. With miniaturized DNA chips, it is currently possible to monitor the expression of thousands of genes simultaneously. Genome-wide mRNA surveillance has already been achieved for yeast and is on the horizon for rodents and humans (11). The fundamental property on which microarrays are based is sequence complementarity of two strands of DNA. More than 30 years ago, Gillespie et al. (12) found that denatured complementary DNA strands bind strongly enough to nitrocellulose to prevent reassociation but are still able to hybridize to complementary RNAs in solution. This discovery is the foundation underlying Northern blots, Southern blots, and dot blots and is the direct precursor to the development of DNA microarray analysis. The microarray procedure is based on hybridization of a probe to multiple defined target sequences (short oligonucleotide sequences or cDNAs) that have preassigned locations on a solid-phase chip (13) (Fig. 2). Macroarrays are conceptually identical but have fewer gene sequences spotted at lower densities, typically on nylon membranes. In either case, the array probe is a heterogeneous mixture of mRNA, cDNA, or aRNA fragments prepared from the tissue of interest. During hybridization, fluorescently labeled probe fragments (or radioactively labeled probe fragments for macroarrays) bind to their appropriate complementary sequence. The intensity of label emission correlates with the number of copies of each individual mRNA (or cDNA or aRNA) present in the probe mixture. If a linear amplification procedure has been used to generate the probe, then the relative emission intensities of the various spots on the array directly reflect the initial mRNA abundances in the target tissue. By design, each target sequence spotted on the array is in vast excess as compared to its complementary sequences in the probe mixture. Therefore, simultaneous hybridization of two probes, each labeled with a different fluorophore, allows for a direct comparison of two samples on a single chip. This property of the technique eliminates confounding factors such as chip-to-chip variation and discrepancies in hybridization conditions inherent in comparing separate experiments. With macroarrays, each radioactively labeled probe must be hybridized to a separate membrane.

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Fig. 2. Use of DNA microarrays. (Top) Two fluorescently labeled probe mixtures, each containing either heterogeneous total RNA (if prepared from a large amount of tissue) or amplified RNA or cDNA (if prepared from small amounts of tissue such as single cells), are co-hybridized to a DNA array (middle row, left). The array contains either cDNAs or short oligonucleotide sequences that have been spotted in vast molar excess to preassigned locations depicted here by gray circles. (Middle row, right box) Magnified view of underlying concept behind hybridization of probes to DNA microarrays. Single-stranded DNA (black half helix) is covalently attached to a glass slide and serves as bait to capture complementary sequences in the fluorescently labeled probes (green or red half helices). Target loci (gray) that have hybridized predominately to Cy3-labeled probe appear green (top left), whereas those that have bound predominately to Cy5-labeled probes appear red (bottom right). When an equal number of Cy3- and Cy5-labeled probes bind to a single sequence of target DNA, the spot appears yellow (bottom panel). The intensity of fluorescence of a spot corresponds to the number of probe molecules bound to the array. Note the light pink color of the top right spot, which is shown to have bound only a single Cy5-labeled sequence. (Bottom) Actual dual channel image of a microarray showing loci that have bound predominately Cy3- (green) or Cy5- (red) labeled probes or those that have bound equal amounts of both labels (yellow). By recent convention, images collected from two probes co-hybridized to a single microarray may also be shown as two microarrays, with each independent channel (Cy3 or Cy5) pseudocolored according to the signal intensity and displayed side by side (Fig. 7, bottom right).

Although data acquisition for microarray experiments is relatively straightforward, data analysis is fraught with statistical challenges that result from multiple simultaneous comparisons [reviewed in (14-17)]. In the ideal case of nonexistent experimental or biological variation between two experimental mRNA populations, the ratio of fluorescent intensities at each locus on the array would be 1.00 and would fall on a line with a 45° slope and zero intercept if the fluorescent intensities from the two probe populations are plotted against each other. Loci where intensities deviate significantly from the y = x line indicate genes that display genuine differential expression. In practice, determining the threshold for true differential expression can be difficult and still requires a second screening method (such as real-time PCR, in situ hybridization, Northern blotting, or RNAse protection assays) to confirm putative differences. One major limitation of microarrays is that they yield no direct information about changes in protein expression. Alterations in the expression of specific mRNAs detected by the arrays are often assumed to correlate with corresponding changes in protein expression; yet this is clearly not true for many mRNA-protein ratios because of the cellular regulatory process known as translational control (18). Despite this shortcoming, microarrays have provided a wealth of information that has been used to correlate changes in gene expression with specific phenotypic changes, such as alterations associated with neurophysiology and neurodegenerative disease progression (10, 19-22). In the next section, we describe how the aRNA approach can be used to address such basic scientific and disease-related questions.

Single-Cell Analysis in the Study of Neurodegenerative Diseases Back to Top

Alzheimer's Disease.

Alzheimer's disease (AD) is an inexorable, progressive, neurodegenerative disorder (see "Detangling Alzheimer's Disease") that is clinically characterized by multiple cognitive deficits, including loss of both recent and distant memories; the inability to produce or comprehend spoken or written language; loss of the ability to sequence, coordinate, and execute purposeful movement; loss of the ability to recognize sensory information; and wide impairment of executive functions (see Honig case study). Once the first clinical signs have appeared, often in the form of subtle deterioration of memory function or social behavioral changes, a continuous cognitive decline is inescapable. Death generally occurs within an average of 8 years after the patient enters the final stage of the disease, which is characterized by a slow, deliberate gait (referred to as hypokinetic hypertonic syndrome). Currently, only a postmortem neurohistological procedure authenticates the clinical presupposed diagnosis.

One key neuropathological characteristic of AD is the presence of neurofibrillary tangles (NFTs). These structures are formed by abnormal aggregation of the protein tau in some neuronal cell types within the central nervous system (CNS) (23-25). In addition, extracellular precipitations of {beta}-amyloid protein are generally concomitant but are not always spatially or temporally associated with NFTs. Although the recent generation of several transgenic models of AD has provided the scientific community with key information concerning tau and {beta}-amyloid dysfunction, our knowledge of the molecular basis of AD remains imprecise. Tau and {beta}-amyloid are certainly the "effector" proteins that lead eventually to neuronal degeneration; in other words, they might be the molecules that operate on neurons to produce the observed cellular phenotypes. But the "selector" genes--genes that encode proteins that regulate expression of these effector proteins--have not yet been clearly identified. Candidates currently under investigation include cell cycle regulatory proteins, components of the inflammation pathway, and synaptic proteins (26-28).

Single neurons are not affected in a homogeneous way during the course of AD (Fig. 3). Indeed, diseased neurons often lie in close proximity to neurons unaffected by the disease. Consequently, an ideal way to identify AD selector genes is to compare the gene expression profiles of cells devoid of tangles or plaques with those of cells that contain these pathological hallmarks; the cells subjected to the comparative analysis should be of the same neuronal type and should be isolated from the same region of the same brain. The use of single-cell mRNA amplification techniques such as the aRNA protocol is central to this line of investigation. Coupled with immunocytochemistry designed to detect markers of AD, the aRNA approach allows the comparison of mRNA transcripts that are differentially expressed in "healthy" and "diseased" neurons at the single-cell level. Fig. 4 illustrates the outcome of such a molecular strategy. After immunocytochemistry was performed to detect the PHF1 protein, a marker for pathological tau protein processing, single cells that were positive or negative for this marker were harvested individually. After the aRNA amplification process was performed on these individual cells and the resulting cDNA probes were radioactively labeled, macroarray analysis revealed 25 candidate genes that potentially are dysregulated in AD. Among the differences in gene expression observed between healthy and AD neurons were a reduction in cyclin D1 mRNA and an increase in {alpha}1-Act mRNA. Cyclin D1 is involved in cell cycle regulation and can be induced in postmitotic neurons during apoptosis. {alpha}1-Act is an acute-phase protein that is induced during inflammation. These changes, as well as the others that were observed, are consistent with various hypotheses concerning cell death in AD.

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Fig. 3. Cell type heterogeneity and AD. Bielchowski staining of the CA1 hippocampal region is shown, revealing "healthy" neurons (black arrows) in the vicinity of "diseased" degenerating neurons (white arrows), {beta}-amyloid plaques (P), endothelial cells (E), and glial cells (G). The heterogeneity of cell types and the different physiological states of similar cell types might diminish the usefulness of brain homogenates for the study of AD at the molecular level. Scale bar, 20 µm.


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Fig. 4. Canonical regression analysis from single cells. This graph exemplifies canonical analysis of single immunolabeled cells from age-matched control cases, pure Lewy body disease (LBD) cases (see Posner case study), and pure AD cases (see Honig case study). Canonical variable 1 (Can 1) represents a weighted combination of genes that best separate the different groups. Canonical variable 2 (Can 2) represents the weighted combination of genes that best reduces the residual variants after Can 1 has reduced the variance. Each individual number represents a single cell (n = 54). Numbers 1 and 4 represent individual cells from an autopsy-confirmed case of pure LBD. Specifically, number 1 represents cells that are immunonegative for PHF1 antibody, and number 4 cells that are positive for PHF1 antibody. Numbers 2 and 3 represents single cells from two different brains with pure AD disease that are negative for PHF1 immunocytochemistry. Numbers 5 and 6 are PHF1-positive individual cells harvested from the same AD-positive brains. The canonical regression analysis allowed us to separate the samples into four populations: ADPHF1-negative (cells from an AD case that were negative for the marker, therefore potentially "healthy" neurons); AD PHF1-positive (cells from the same AD case that were positive for the marker, therefore potentially "diseased" neurons); LBD PHF1-negative (cells from a pure LBD case that are potentially "healthy"); and finally LBDPHF1-positive ("diseased" neurons in the same LBD case).

One crucial feature of array analysis is the quantitation of hybridization differences when comparing different samples. The differences in mRNA abundance in the experiment described above were determined by quantitating the 32P emission from the hybridized cDNAs immobilized on the arrays. Using a statistical tool called canonical regression analysis, which is an extension of multiple regression analysis from one criterion variable to a set of criterion variables, the single harvested cells could be grouped into four separate populations (Fig. 4). Indeed, control "healthy" neurons (PHF-negative) could be separated from "diseased" (PHF-positive) neurons on the basis of their transcript fingerprints. This approach contains several advantages over more classical analyses of homogenized tissues. First, specific neuronal cell types [for example, glutamatergic versus {gamma}-aminobutyric acid (GABA)-ergic neurons] can be analyzed separately; second, transcripts from nonneuronal cell types (such as glia and endothelial cells) do not interfere with the analysis; and third, one can avoid the general problems encountered when comparing tissue samples from age-matched controls and AD patients, such as errors that arise because the samples have distinct life histories or postmortem delays, or because the samples were at different agonal stages (for example, different degrees of ischemia). The single-cell aRNA amplification technique, coupled with immunocytochemistry, offers a level of analysis that allows a precise spatiotemporal study of AD, which cannot be obtained with more classical approaches. The use of in situ hybridization and single-cell reverse transcription-PCR approaches should be standardized and used for validation of the microarray results.

Huntington's Disease.

A second neurodegenerative disease whose pathologies are well suited for study with the aRNA single-cell analysis technique is Huntington's disease (HD). HD is a genetic disorder that is clinically characterized by motor and psychiatric disturbances and is pathologically defined by progressive neuronal degeneration. In 1993, the HD mutation was identified as an expansion of trinucleotide (CAG) repeats within the coding region of a gene located on chromosome 4 (29, 30). This gene encodes the huntingtin protein, a ubiquitously expressed protein whose normal function remains unknown. The CAG repeats are translated into a stretch of polyglutamine residues (29). In normal individuals, the CAG repeat length is fewer than 35, whereas adult HD patients can have 36 to 75 repeats (31, 32). The proposed mechanism by which polyglutamine expansion results in HD pathology is through the cleavage, aggregation, and accumulation of huntingtin NH2-terminal fragments (33, 34).

Although normal huntingtin protein is widely expressed in all somatic tissue, HD pathology is restricted to specific parts of the brain (33, 34). Neuropathological studies of HD patients have demonstrated that degeneration occurs initially in the neostriatum and cortex (35). Neurodegenerative abnormalities can be detected early in the course of HD and before the onset of dementia. The neostriatum exhibits dramatic structural and cellular alterations in HD and is the only brain region where neuronal loss is associated with reactive astrocytosis [glial cell response to injury; for review, see (36)]. The most striking neurochemical alteration in HD is the preferential loss of medium spiny GABAergic neurons (36). These are the principal input and output neurons and account for 80% of the neostriatum. Researchers who study HD are currently striving to answer the following questions: How does the expression profile of cells from HD brains differ from normal age-matched controls? Does the expression profile of presymptomatic gene carriers differ from the profile of cells isolated during the advanced stages of HD (37)? Is the genetic profile of selectively vulnerable brains regions (such as the neostriatum) different from regions that exhibit minimal pathology (such as the hippocampus)? Because the CNS has a heterogeneous cell population and because HD pathology displays distinct topographic and cell-specific alterations, single-cell analysis, combined with microarray technology, provides a novel way to map the expression profile of specific populations of brain cells in HD.

The ability to examine the expression profile from a small quantity of tissue, such as a single cell, requires the faithful amplification of the endogenous mRNA population. The aRNA technique allows for the linear amplification of mRNAs from postmortem tissue. An important step in single-cell expression profiling is to determine the neuronal population to be examined. One approach to cell selection is to profile cells that express known "death-effector" proteins, such as a class of proteases known as caspases. When activated, these proteases play a critical role in apoptotic neuronal cell death (38). In HD transgenic mice and HD cells in culture, caspases cleave the huntingtin protein and thereby act as a signal to propagate apoptotic cell death (38). Fig. 5 shows the process for isolating individual caspase-3-producing neurons from the neostriatum of HD transgenic mice.

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Fig. 5. Immunohistochemical staining of caspase-3-positive cells in the neostriatum of grade 2 HD tissue. (A) Low-magnification image (x4) of the neostriatum. The box shows the area of the neostriatum that contains the single caspase-positive cell from which mRNA was harvested. This boxed region is shown at high magnification (x40) in the other three panels. (B through D) A caspase-3-labeled cell before (B), during (C), and after (D) the cell was aspirated. Tissue was provided by the Hereditary Disease Foundation through the McLean Hospital Brainbank.

Our first step in the single-cell analysis of HD brains will be to generate mRNA profiles of neurons that express caspase-3. It is possible that the products of genes differentially expressed in these dying cells play a role in the etiology and maintenance of the molecular pathology of HD. In addition, adjacent normal cells will be analyzed by the same procedures. Thus, the molecular characterization of normal cells from late-stage HD brains might provide information on why certain cells are resistant to HD-induced cell death and offer insight into how to better design protective agents for striatal and cortical neurons. This example hints at the promise of single-cell mRNA analysis in the characterization of a disease process.

Single-Cell Analysis of Neural Progenitor Cells Back to Top

Stem cells are a group of multipotent cells capable of differentiating into a variety of cell types. Their recent application to a number of disease states, from cardiovascular (39, 40) to endocrine (41, 42) to hematologic (43), is revolutionizing perceptions about the possibility of repairing diseased tissues. Until recently, damage to the CNS was considered irreversible. However, with the discovery of neural cell progenitors, a group of stem cells capable of forming the principal elements of the central and peripheral nervous systems (neurons, astrocytes, and oligodendrocytes), came the hope that the damage caused by neurodegenerative diseases can be reversed.

Although the use of fetal tissue is highly controversial on ethical grounds, early studies in mammals (including humans) on restorative cell replacement therapy for neurodegenerative diseases used stem cells from fetal neural tissue. More than 25 years ago, researchers transplanted dopamine-rich, rat fetal striatal tissue into a rat model of Parkinson's disease and symptoms were reduced (44, 45). Subsequent studies in Parkinsonian monkeys confirmed the promise of symptomatic improvement when fetal neural tissue was transplanted into these animals (46). Fetal striatal stem cell transplants have also restored motor deficits in rat and monkey models of HD [reviewed in (47)]. In a clinical trial where human fetal stem cells were transplanted into patients with HD, the unfortunate death of one patient allowed an important observation to be made. Postmortem analysis demonstrated that fetal neurons survived for 18 months after transplantation, acquired a striatal phenotype, made functional connections, and were free of mutated huntingtin pathology (48). However, despite their considerable promise, routine use of human fetal stem cell tissue has several ethical and biological drawbacks, including the limited quantity of suitable graft material, graft tissue heterogeneity, and infectious risks posed by the graft. For these reasons, many have argued in favor of the engraftment of neural stem cell or progenitor cell lines.

The availability of nearly unlimited quantities of neural stem cells (NSCs) derived from culture, along with the prolonged in vivo viability of cryopreserved NSCs, has made them an ideal source of transplant material for regenerative therapies for many CNS disorders (49). Indeed, two different cell lines--C17-2, a neural stem cell line derived from a 4-day-old mouse cerebellum (50), and Ntera2 (NT-2), a neural progenitor cell line derived from a human teratocarcinoma (51)--have been shown to survive in vivo for months to years after transplantation (52, 53). Although C17-2 cells are truly pleuripotent (that is, they give rise to all fundamental neural lineages), NT-2 cells are more restricted in their fate. After the application of retinoic acid, NT-2 cells are capable of differentiating into mature human neurons (hNTs) (54). Both cell lines have been used in vitro and in numerous transplant studies over the past several years with some noteworthy results (55-57).

In an effort to better understand the basic biology of stem cell differentiation and as an initial step toward determining the repertoire of neural stem cell responses to environmental cues, we quantitated the differences in mRNA expression between the human neural progenitor cell line NT-2 and hNTs that were differentiated from NT-2 cells by treatment with retinoic acid. We used the aRNA amplification technique (Fig. 1) to generate large amounts of aRNA to be used as probes in DNA microarray assays. The aRNA populations were made from undifferentiated NT-2 cells in culture (Fig. 6A), retinoic acid-differentiated hNT cells in culture (Fig. 6B), and hNT cells 1 week after transplantation into the rat cortex (Fig. 6, C and D). To take full advantage of the microarray analysis, aRNA probes were labeled with either the Cy3 or Cy5 fluorescent markers, and two populations of probes [for example, probes from the NT-2 (Cy3) and hNT (Cy5) cells] were hybridized simultaneously to the same glass slide microarray, which contained more than 8000 human genes]. Fig. 7 demonstrates the hybridization of low-, medium-, and high-abundance mRNAs on a log/log plot, where the line y = x (shown in red) depicts no difference in expression between NT-2 and hNT cells. Points that lie outside the green lines represent genes that exhibit greater than twofold differences in expression. At the far right of the figure are 32 examples (out of >8000 genes tested) of specific hybridization of probes to single genes; each channel (Cy3 or Cy5) was separated and pseudocolored according to the intensity of hybridization to allow for side-by-side comparison of signal strength. Note the induction of genes depicted by blue boxes and the lack of induction elsewhere. Also note in the black box the lack of signal, which corresponds to low background noise on the array in a region where no DNA was spotted.

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Fig. 6. Neural progenitor cells in vitro and in vivo. (A) Unstained NT-2 cells in culture at x40 magnification as revealed by light microscopy. Note the confluence and large round appearance of these undifferentiated neural progenitor cells isolated originally from the embryonal carcinoma cells of a human teratocarcinoma. (B) After a 5-week treatment with retinoic acid [10 µM twice a week as per published protocols (61)], NT-2 cells differentiate into more mature, postmitotic neurons called hNT cells, shown here extending long processes (white arrows). hNT cells are unstained and at x40 magnification. (C) x4 magnification of hNT cells 1 week after transplantation into the cortex of adult male Sprague-Dawley rats. The transplantation procedure, processing of rat brains, and immunohistochemical staining were done as published (62), with the following exception: A monoclonal HO14 antibody that recognizes a human-specific neurofilament epitope expressed solely by hNT cells (63) was kindly provided by J. Trojanowski and used at a dilution of 1:3. After immunohistochemical staining, sections were stained with Nissl to visualize cell morphology. (D) x40 magnification of hNT cells that had been transplanted into the cortex of adult male Sprague-Dawley rats. The cells were stained with HO14 (brown). These cells were later microdissected individually for use in the aRNA amplification procedure (not shown). The black arrows in the figures point to cell bodies. Scale bar, 50 µm in x40 pictures, 500 µm in x4 picture.


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Fig. 7. Neural progenitor microarray. (Top) Microdissection of hNT and NT-2 cells from culture done with an inverted Olympus IX 70 (Olympus America, Melville, NY) microscope and a Narishige ONO-21 micromanipulator (East Meadow, NY). For the hNT neurons in the top two photomicrographs, the field of cultured neurons can be seen before (left) and during (right) cell harvesting. The cluster of cells that was just below the micropipette in the right panel was aspirated into the micropipette. The two lower photomicrographs show the cultured NT-2 neurons before (left) and after (right) cell harvesting. These were not single-cell harvestings; instead, groups of cells were harvested. Scale bar, 50 µm. (Bottom) Differential mRNA expression in NT-2 versus hNT, graphed on a log, log scale. Blue points represent expression levels of individual genes in undifferentiated NT-2 versus differentiated hNT cells. The line y = x, with a 45° slope and 0 intercept, is shown in red and marks equal expression of genes in both cell types. All blue points falling between the green lines are genes that have less than twofold differential expression. Those outside the green lines are induced or repressed by differentiation with retinoic acid. The panel on the bottom right shows several examples of hybridization of aRNA probes to the cDNA microarray. Circular spots were separated by channel (NT-2 on channel Cy3, and hNT on channel Cy5) and pseudocolored according to the intensity of signal strength. Blue boxes show examples of mRNAs that are more abundant in NT-2 cells than in hNT neurons (more red than blue, giving rise to the yellow color). The black box shows that a plasmid cDNA spotted onto the glass slide yields a low level of background noise.

Over the years, the differentiation of NT-2 cells into mature hNT neurons has been studied in detail both one gene at a time and more recently on macroarrays. It is known that mRNA coding for proteins known to be involved in the docking and fusion of synaptic vesicles with the plasma membrane, such as SNAP-25, Rab guanosine triphosphatase (Rab GTPase), synaptobrevin, synapsin, and synaptophysin are induced in hNT neurons during differentiation (58). In addition, resting calcium homeostasis and neuronal excitability are radically altered during differentiation to hNT cells and are accompanied by the induction of both N- and L-type calcium channels (59). mRNAs encoding a group of transcription factors, second messengers, growth factors, cytokines, and other neurotransmitter receptors are up-regulated by retinoic acid-induced differentiation, whereas genes encoding cellular proliferation-related proteins are down-regulated in hNT cells (60). We hope to confirm and extend these findings by using the well-characterized changes in expression of these genes as positive controls in DNA microarray analyses. Moreover, we should be able to determine the effect of the host brain on transplanted hNT neurons. Such studies might well lead to logical methods for promoting or inhibiting the differentiation process of neurons in vivo. Further, the cataloging of mRNAs whose expression is altered in stem cells during their terminal differentiation might provide insights into how to modulate endogenous cells in an effort to replace disease-damaged tissues.

Conclusions Back to Top

Steady progress has been made in discovering gene networks that are altered in neurodegenerative disorders such as AD and HD. However, much more remains to be discovered about the primary molecular and cellular adaptations that transform healthy neurons into diseased ones. In addition, further characterization of the fundamental biology of neural stem cell growth, maturation, and directed differentiation is required before regenerative strategies become a practical therapy for neurodegenerative diseases. We believe that the use of aRNA amplification, combined with microarray analysis to create expression profiles of single cells, is a logical and prerequisite step to understanding the mechanisms of neurodegenerative disease pathogenesis. Fully characterized degenerative processes will likely inspire and inform novel cell replacement approaches, which can then be devised and tested.

Acknowledgements Back to Top

We thank the Hereditary Disease Foundation for providing the brain tissue used in the HD study. This work was supported in part by the following grant awards: P50-NS08803 from NIH to T.M., AG14441 from NIH and the Pioneer Award from the Alzheimer's Association to P.C., and AG09900 from NIH to J.E. J.E. is a founding scientist, consultant for, and has an equity position in Layton Bioscience, which is developing the NT-2 neurons for clinical studies.

January 9, 2002

21 November 2001; revised 6 December 2001; accepted 10 December 2001

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Citation: M. Kelz, G. Dent, S. Therianos, P. Marciano, T. McIntosh, P. Coleman, J. Eberwine, Single-Cell Antisense RNA Amplification and Microarray Analysis as a Tool for Studying Neurological Degeneration and Restoration. Science's SAGE KE (9 January 2002),;2002/1/re1

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