Laser microdissection and gene expression profiling in the human postmortem brain

Handbook of Clinical Neurology, Vol. 150 (3rd series) Brain Banking I. Huitinga and M.J. Webster, Editors https://doi.org/10.1016/B978-0-444-63639-3.00018-9 Copyright © 2018 Elsevier B.V. All rights reserved

Chapter 18

Laser microdissection and gene expression profiling in the human postmortem brain 1

KAI-CHRISTIAN SONNTAG1,3 AND TSUNG-UNG W. WOO2,3* Laboratory for Translational Research on Neurodegeneration, Belmont, MA, United States 2

Laboratory of Cellular Neuropathology, McLean Hospital, Belmont, MA, United States 3

Department of Psychiatry, Harvard Medical School, Boston, MA, United States

Abstract Laser microdissection in combination with gene expression profiling using postmortem human brain tissue provides a powerful approach to interrogating cell type-specific pathologies within neural circuits that are known to be dysfunctional in neuropsychiatric disorders. The success of these experiments critically depends on a number of factors, such as the cellular purity of the sample, the quality of the RNA, the methodologies of data normalization and computational data analysis, and how data are interpreted. Data obtained from these experiments should be validated at the protein level. Furthermore, from the perspective of disease mechanism discovery, it would be ideal to investigate whether manipulation of the expression of genes identified as differentially expressed can rescue or ameliorate the neurobiologic or behavioral phenotypes associated with the specific disease. Thus, the ultimate value of this approach rests upon the fact that the generation of novel disease-related pathophysiologic hypotheses may lead to deeper understanding of disease mechanisms and possible development of effective targeted treatments.

The normal functioning of the human brain critically depends upon the molecular homeostasis of its cells and the integrity of the orchestrated activation of networks both on the molecular and the neural circuit levels in a spatially and temporally synchronized manner (Soltesz, 2005; Buzsaki, 2006). A salient feature of neural circuits is the brain region-specific stoichiometry of neuronal cell types. Furthermore, recent evidence suggests that glia, including astrocytes, oligodendrocytes, and microglia, may also actively participate in sculpting neuronal and neural circuitry functions (Fields et al., 2014). Therefore, dysfunction of neurons and glia involving specific neural circuits and brain regions contributes to various neuropsychiatric disorders, from psychotic disorders, such as schizophrenia and bipolar disorder, to neurodegenerative conditions, such as Parkinson’s disease and Alzheimer’s disease. Many of these neuropsychiatric disorders are known to arise from cell type-specific

pathophysiologic disturbances. Hence, molecular profiling of specific cell types involved in the dysfunction of disease-related neural circuitry will have important implications not only for understanding the pathophysiology of these diseases, but also for the potential development of treatments that aim at normalizing or recalibrating cellular and circuitry dysfunction. In this chapter, we review the application of laser microdissection in cell type-specific gene expression profiling in postmortem human brain as a powerful approach to investigating the molecular pathophysiologic basis of neural cells and circuitry disturbances in neuropsychiatric and neurodegenerative disorders.

LASER MICRODISSECTION PLATFORMS There are currently three platforms available for laser microdissection. The Leica system utilizes a high-energy

*Correspondence to: Tsung-Ung W. Woo, M.D., Ph.D., McLean Hospital, Mailman Research Center, Belmont MA 02478, United States. Tel: +1-617-855-2823, E-mail: [email protected]

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ultraviolet (UV) laser beam to cut around the tissue or cells of interest, which then fall into a collection tube situated immediately below. The Palm MicroBeam system, manufactured by Zeiss, is similar to the Leica system in that a laser beam is used to cut out the region or cells of interest. However, instead of collecting the specimen into a collection tube via gravity, as in the Leica system, the Palm system utilizes a patented technology called laser microdissection and pressure catapulting (LMPC) for the removal of tissue/cells by a laser pulse, which allows the propulsion of the specimen against gravity into a collection vessel. It is claimed that this laser pulse does not transfer energy and hence will not cause damage to the specimen. LMPC is a possible advantage as, in our experience, the collection of specimens using the Leica system can be significantly affected by the humidity of the environment in that dissected specimens may not easily fall into the collecting tube due to surface tension. Finally, the Arcturus system utilizes a proprietary technology that was originally developed within the National Institutes of Health and is categorically different from the Leica or the Zeiss instrument. It involves the use of a cap, which is placed over the area or cell of interest. A low-energy infrared laser pulse is then applied, activating the film on the cap to adhere to the target tissue or cells, hence, removing the specimens as the cap is lifted. Specimens on the cap can then be transferred to a microcentrifuge tube for extraction of molecular materials. While this process is affected by humidity, with a humidity of 6–23% being considered as an optimal range (Ordway et al., 2009), the efficiency of specimen removal is negatively affected as humidity increases (unpublished observation). The Arcturus-based approach is specifically termed laser capture microdissection or LCM, which distinguishes it from the high-energy UV laser-based “laser cutting” approach utilized by both the Zeiss and the Leica systems, that are usually referred to as laser microdissection (LMD). In addition, the Arcturus system offers a laser cutting module that can be purchased and added on separately, making it arguably the most versatile platform for a variety of tissue and cellular applications. The high energy carried out by an UV laser utilized in the laser cutting instruments has, in theory, the potential of damaging macromolecules located along and in the vicinity of its path and hence may compromise the integrity of downstream molecular profiling analyses. The diameter of the UV laser beam is 1–2 mm; hence, this potential problem may be negligible if a relatively large area of tissue is being dissected for downstream application, but this could potentially be problematic if small areas, such as single cells, are being targeted.

RNA INTEGRITY Assessment of RNA integrity The integrity of RNA is critical for the success of downstream gene expression-profiling analysis. There is some evidence suggesting that RNA in the brain may be more prone to degradation (Koppelkamm et al., 2011). The RNA integrity number (RIN) has been commonly used as a measure of the integrity of RNA and is frequently assumed to also be an indicator of the integrity of mRNA, which comprises a rather small proportion (
5%) of the entire constellation of RNA species in mammalian cells. RIN is computed from RNA electrophoresis and electropherogram profiles determined by the Agilent bioanalyzer platform (Imbeaud et al., 2005; Schroeder et al., 2006). The RIN ranges from 1 to 10, indicating low or high RNA integrity, respectively. It has been suggested that a RIN of 6 or 7 is considered to be a “quality threshold” in postmortem human brains (Ferrer et al., 2008). Aside from the Agilent platform, the RNA quality indicator or index is the assessment offered by Bio-Rad based on the Experion electrophoresis system, which also utilizes a numbering system of 1–10. Systematic investigation across RNA samples from various tissue types suggests that the RIN and RNA quality indicator algorithms appear to be highly comparable (Riedmaier et al., 2010).

Factors influencing RIN DEMOGRAPHIC FACTORS In the literature, reported RINs from postmortem brains vary substantially across brain collections (Table 18.1). In addition, the RIN has been reported to correlate with a number of demographic variables, such as postmortem interval, age at death, pH, and duration and severity of agonal state, but these findings are often inconsistent across studies. For instance, many studies have shown that postmortem interval is not a predictor of RIN Table 18.1 Variability of RNA integrity number across brain collections Average  SD

Range

Citation

3.6  4.0 3.6  1.5 2.8

1.0–8.5 2.0–6.9 1.9–6.2

5.5  1.9 5.9  2.9 to 8.1  1.2 8.0  2.6 to 8.9  0.7 7.0  0.4

2.5–8.2 n/a 0.0–9.8 1.6–7.3

Trabzuni et al. (2011) Coulson et al. (2008) Koppelkamm et al. (2011) Weis et al. (2007) Stan et al. (2006) Birdsill et al. (2011) Sheedy et al. (2012)

n/a, not available.

LASER MICRODISSECTION AND GENE EXPRESSION PROFILING (Stan et al., 2006; Coulson et al., 2008; Koppelkamm et al., 2011; Trabzuni et al., 2011), but a weak correlation between postmortem interval and RIN has also been observed (Barton et al., 1993; Harrison et al., 1995; Lipska et al., 2006; Webster, 2006; Chevyreva et al., 2008; Birdsill et al., 2011). Similarly, with some exceptions (Durrenberger et al., 2010; Trabzuni et al., 2011), the majority of the studies have found that RIN does not correlate with age (Lipska et al., 2006; Chevyreva et al., 2008; Popova et al., 2008; Koppelkamm et al., 2011; Sherwood et al., 2011). Conversely, most (Lipska et al., 2006; Webster, 2006; Stan et al., 2006; Chevyreva et al., 2008; Durrenberger et al., 2010; Sherwood et al., 2011; Trabzuni et al., 2011), although not all (Coulson et al., 2008), studies have found that pH is positively correlated with RIN, such that pH appears to be a good predictor of RIN. Finally, agonal state has also been found to have some effects on RNA quality (Tomita et al., 2004), and this effect may be mediated by pH as prolonged and more severe agonal state appears to be associated with lower tissue pH, and vice versa (Durrenberger et al., 2010).

BIOLOGIC AND TECHNICAL FACTORS There have been reports suggesting that RIN may vary across brain regions (Lipska et al., 2006; Weis et al., 2007; Chevyreva et al., 2008; Trabzuni et al., 2011), brain bank, or source of brain donation (Stan et al., 2006; Trabzuni et al., 2011). Additionally, diagnosis and manner/cause of death such as drug overdose or carbon monoxide poisoning may affect the RIN (Webster, 2006; Trabzuni et al., 2011). Finally, RIN may also be influenced by unrefrigerated interval, freeze/thaw cycle, tissue integration/degradation, and RNA extraction methods (Stan et al., 2006; Weis et al., 2007; Sheedy et al., 2012; Kolijn and van Leenders, 2016).

RIN is not a reliable predictor of RNA integrity Because of the controversy in the field about RIN as a valid measure of brain tissue quality, in a recently published study we systematically investigated whether the RIN predicts RNA integrity, and thus, tissue quality of postmortem human brains (Sonntag et al., 2016). For this, we analyzed the electrophoresis data and electropherograms of total RNA and mRNA converted cDNA samples from 174 postmortem human brains. In addition, we assessed the successful amplification of cDNA across the entire 5’ to 3’ region of the cytochrome C-1 (CYC1) transcript: CYC1 is a gene integrally associated with the electron transport chain and has been determined as a suitable housekeeping transcript in studies on postmortem brain material from patients with Alzheimer’s

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disease (Penna et al., 2011) and glioma (Grube et al., 2015). Our data showed substantial inconsistencies between RINs and corresponding RNA electropherogram profiles (Fig. 18.1A), as well as a lack of correlations between RINs and RNA concentration, signal area, and rRNA (28S/18S) ratios. All of these observations argue against the interpretation of the RIN as a singular measure of the quality of RNA extracted from postmortem human brains. When we measured mRNA integrity, our data showed that RINs in the lower ranges appeared to be a poor predictor of cDNA quality (Fig. 18.1B). However, quantitative real-time polymerase chain reaction (qRT-PCR) results demonstrated that samples with both low and high RINs had linear amplification across the entire length of the cDNA (Fig. 18.1C), with significant cycle threshold (CT) correlations for the 3’ and 5’ region (Fig. 18.1D). In addition, CT values and RINs were significantly negatively correlated (Fig. 18.1E), indicating that the RIN is an indicator of the amount of intact mRNA, regardless of the degree of linear degradation along the 3’–5’sequence. Altogether these data indicate that the RIN is not a measure of the integrity of mRNA but may reflect its quantity in the samples. What complicates this issue more is the fact that RIN measures on RNA extracted from the same tissue samples can vary quite significantly between laboratories (Table 18.2). This variability may be explained, at least in part, by technical differences, such as differences in the methods used to extract RNA (Kolijn and van Leenders, 2016); however, variability can be observed even within the same laboratory when RNA extraction and RIN measurements are done at different times (Table 18.2). Based on all of these observations, a strong argument can be made for not overly relying on the RIN to determine the quality and usability of postmortem human brain tissue.

GENE EXPRESSION PROFILING OF LASER MICRODISSECTED HOMOGENEOUS CELL POPULATIONS IN POSTMORTEM HUMAN BRAINS We have performed mRNA and micro (mi)RNA expression profiling of homogeneous cell populations obtained by LCM, including pyramidal neurons (Pietersen et al., 2014a), parvalbumin-containing inhibitory neurons (Pietersen et al., 2014b), and oligodendrocytes (Mauney et al., 2015) in the cerebral cortex, g-aminobutyric acid neurons in the hippocampus (Benes et al., 2007, 2008, 2009) and dopamine neurons in the substantia nigra (Simunovic et al., 2009, 2010; Briggs et al., 2015), using postmortem brain specimens from healthy human subjects and those with schizophrenia or Parkinson’s disease. The methodology of

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RNA

RNA

06885; RIN 2.4

cDNA

17270; RIN 2.9

06885; RIN 2.4

08067; RIN 3.8

02315; RIN 4.4

02315; RIN 4.4

14430; RIN 5.0

02646; RIN 6.5

02646; RIN 6.5

03111; RIN 7.7

231; RIN 9.8

231; RIN 9.8

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1:10 1:100 1:1000 1:10000

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Fig. 18.1. See legend on opposite page.

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Table 18.2 Comparisons of RNA integrity number (RIN)measurements across laboratories RIN Case #

Diagnosis

Age

PMI

pH

Laboratory 1

Laboratory 2

Laboratory 3

Laboratory 4

Laboratory 5a

AN09434 AN05548 AN18172 AN15492 AN14305 AN14430

SZ C C SZ PD FTD

44 63 92 86 89 80

13.42 23.58 29.63 13.72 24.75 23.1

7.09 6.49 6.29 6.3 6.91 6.39

7.8 7.5 2.6 7.1 5.7 5.7

7.3 6.7 2.7 7 5.3 4.2

7 4.5 2.8 4.9 n/a n/a

7.7 7 3.4 3.3 7.7 2.6

7.7 7 3 3.3 n/a n/a

9.3 8.8 2.4/2.6 7.8 8.3 5.8

a

Some samples were measured twice at different times in laboratory 5. C, control; FTD, frontal temporal dementia; PD, Parkinson disease; PMI, postmortem interval; n/a, not available; SZ, schizophrenia.

combining LCM with microarray-based profiling has been described extensively in these papers and elsewhere (Pietersen et al., 2009, 2011). In addition to our studies, there have been a number of other reports describing cell type-specific gene expression profile alterations in other neuropsychiatric diseases, such as Alzheimer’s disease (Ginsberg et al., 2012; Elkahloun et al., 2016), amyotrophic lateral sclerosis (Highley et al., 2014), major depression (Kerman et al., 2012), multiple sclerosis (Mycko et al., 2012), and also studies aiming at characterizing the cell type-specific profiles of the healthy human brain (Torres-Munoz et al., 2004; Harris et al., 2008). More recently, there have been successes in utilizing whole-genome transcriptome sequencing, RNA-Seq, in elucidating transcriptomic profiles of laser-captured cells obtained from postmortem brains from schizophrenia subjects (Kohen et al., 2014; Athanas et al., 2015).

Biologic validity of human postmortem gene expression-profiling data The validity of postmortem gene expression-profiling findings depends on a number of variables, including

the cellular purity of the sample, the quality of the RNA, the methodologies of data normalization and computational data analysis, and how data are interpreted.

CELLULAR HOMOGENEITY A major advantage of laser microdissection or, more specifically, LCM, is the ability to isolate homogeneous populations of cells. However, it remains necessary to be cautious about sample “contamination” by other cell types (e.g., perineuronal oligodendrocytes, other types of glia, migrating immune cells from the periphery). Thus, it would be important to demonstrate in the data set the absence of markers that are specific for the potential contaminant cell type(s). If contamination is an issue, it may be possible to address this bioinformatically, such as by subtracting gene expression profile associated with the contaminant cell type from the data set, although this would be very labor-intensive and time consuming if it is necessary to perform a separate experiment in order to define the contaminant cell type expression profile.

Fig. 18.1. Analysis of RNA from human postmortem brain material. Electropherogram and electrophoresis measurements by the Agilent 2100 bioanalyzer platform from representative total RNA (A) or cDNA (B) with RNA integrity numbers (RINs) between 2.4 and 7.7. In the RNA measurements, a substantial number of RINs appeared to be inconsistent with the shape of the traces, and in most cases the differences were within a reading range of 1.0. In the cDNA measurements, there was a typical bell-shaped curve in the control samples reflecting the expected transcription of small- and large-sized mRNAs. In contrast, all cDNAs from the brain samples had a left shift of the curves, indicating an increase in the smaller and a decrease in the larger transcripts, consistent with mRNA degradation. Numbers indicate brain sample identification or control fibroblast (#231). (C–E) Quantitative real-time polymerase chain reaction qRT-(PCR) data on the 3’, center, and 5’ region of the CYC1 transcript. (C) Cycle threshold (CT) values for 10 samples (eight brains and two fibroblast controls) with RINs between 2.4 and 9.8 show linear amplification of PCR products in four dilutions along the entire cDNA in all samples. The linear regression curves depict average CT values for all samples in each dilution. (D) Linear regression curve showing distribution of CT values for the 3’ and 5’ region demonstrate positive correlations in all dilutions. (E) RINs negatively correlate with CT values in all samples and dilutions across the entire cDNA. The r and p values of all regression curves in panels C, D, and E are significant, ranging from 0.84 to 0.99 (r) and 0.00001 to 0.014 ( p). (Data and figures derived from Sonntag KC, Tejada G, Subbujaru S, et al. (2016) Limited predictability of postmortem human brain tissue quality by RNA integrity numbers. J Neurochem 138: 53–59.)

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RNA QUALITY Many studies, including our own, have shown that goodquality RNA can be derived from postmortem human brain tissue and can be successfully assessed using biochemical and molecular assays, such as microarrays, qRT-PCR, or RNA-Seq (Gilbert et al., 1981; SajdelSulkowska et al., 1988; Benes et al., 2009; Simunovic et al., 2009, 2010; Pietersen et al., 2011, 2014a, b; Trabzuni et al., 2011; Athanas et al., 2015; Konopaske et al., 2015; Ruzicka et al., 2015). The RIN is one of the most commonly used measures of RNA quality. However, as discussed above, our experimental findings suggest that the RIN does not predict mRNA or cDNA integrity but rather is an indicator of mRNA or cDNA quantity. In addition, RINs derived from laser-captured cell populations tend to be lower than those derived from whole tissue dissected materials (Wang et al., 2010). Regardless, because of the small amount of RNA isolated from laser-captured cells, which is typically in the picogram to nanogram range, it may not always be possible to have sufficient amount of sample for RIN measurements. Aside from the RIN, visual inspection of the electropherograms will offer a fairly good assessment of the overall integrity of various mRNA species, although this assessment is not necessarily quantifiable. Spectrophotometric analysis can give the investigator a good idea of the purity of the cDNA. Finally, in microarray experiments, percent present call is a good indicator of the quality of hybridization and hence may reflect overall mRNA quality (Benes, 2006).

GENE EXPRESSION PROFILING AND COMPUTATIONAL DATA ANALYSIS

There are several platforms to assess the mRNA or miRNA expression profiles, including microarrays, qRT-PCR, and RNA-Seq, and all of them have been successfully applied to RNA obtained from laser-captured postmortem brain cells. A critical aspect in evaluating differentially expressed genes or small molecules is proper data normalization. Most of the available computational programs have integrated normalization algorithms; however, additional analyses may be necessary. In particular, in qRT-PCR, the choice of suitable housekeeping genes appears to be an important factor (Penna et al., 2011; Tunbridge et al., 2011; Grube et al., 2015). In addition, novel technologies, such as RNA-Seq (Risso et al., 2014; Ding et al., 2015; Li et al., 2015), have different ways of data normalization when compared to conventional methods, such as microarray platforms (Quackenbush, 2002; Calza and Pawitan, 2010). As for assessing the expression of small molecules, such as miRNAs, several methodologies have been used,

including normalization to equally expressed molecules across samples, nuclear RNAs (e.g., sno-RNAs), or global normalization, from which the latter appears to have become the most accepted standard (Schwarzenbach et al., 2015; Wang et al., 2015). In general, normalization should be done using different methodologies and the most robust data points should be used for computational data analyses. Other critical aspects include the determination of detection thresholds, i.e., at what values genes or small molecules are being considered expressed, and to what extent expression differences between sample populations are physiologically relevant or meaningful. These are important considerations in the interpretation of data and have gained more attention in recent years due to the development of technologies with increased detection sensitivities, such as RNA-Seq, digital PCR, or highly sensitive mass spectrometry. Another critical parameter when analyzing human samples is high data variability, as individual expression profiles can substantially vary both within a control and across experimental groups. Therefore, a sufficiently large number of samples is desirable to generate statistically significant data points. However, meaningful findings can also be derived from studies with low “N,” e.g. when pathway analysis is performed on a group of relevant genes in such pathways, instead of focusing on individual (conserved) gene level changes, which may better reflect the underlying biologic processes (discussed by Simunovic et al., 2010).

DATA VALIDATION AND INTERPRETATION qRT-PCR is the most commonly used approach to validate microarray findings. Typically, this is done by validating a combination of randomly selected genes and genes within specific gene networks or pathways found to be differentially regulated by bioinformatic analysis. To validate microarray data, gene expression changes determined by microarray and qRT-PCR must, of course, be in the same direction. Depending on the number of samples available, it may not always be possible to obtain statistically significant qRT-PCR data. A more sensitive approach is to statistically interrogate any significant correlation between gene fold changes determined by microarray and qRT-PCR, respectively (Morey et al., 2006). In addition to qRT-PCR validation, it would be important to determine if the observed gene expression changes also occur at the protein level. As an example, in a recently published study, we found that the expression of the mRNA for UBE3B, which encodes the ubiquitin ligase E3, was significantly decreased in pyramidal neurons in subjects with schizophrenia (Pietersen et al., 2014a). We have since found that the

LASER MICRODISSECTION AND GENE EXPRESSION PROFILING densities of all UBE3B-immunoreactive neurons, including the UBE3B-immunoreactive pyramidal cells, also appear to be decreased in these subjects (Fig. 18.2A, B). Furthermore, we investigated the gene expression changes of pyramidal neurons in the human prefrontal cortex during normal periadolescent development in order to gain insight into the possible molecular mechanisms involved in synaptic remodeling of pyramidal neuronal circuitry. Our data suggest that genes associated with the ubiquitination system, which has been implicated in the biology of synaptic plasticity (Mabb and Ehlers, 2010), may play a major role. Of interest, among these genes, UBE3B appears to undergo periadolescent increase. Consistent with this finding, UBE3B immunoreactivity also appears to be upregulated during periadolescent development (Fig. 18.2C and D). Altogether these findings point to a novel hypothesis that this specific ubiquitin ligase may play a role in the developmental pathogenesis of schizophrenia onset by possibly altering the synaptic remodeling process. This hypothesis can than be followed up experimentally by investigating the effects of pyramidal neuron-specific knockdown of UBE3B on the neurobiologic and/or behavioral phenotypes characteristic of schizophrenia in in vitro and/or in vivo systems.

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The ultimate validation of microarray findings from the perspective of disease mechanism discovery is the fact that manipulation of the expression of genes and signaling pathways identified as differentially expressed in diseases can rescue or ameliorate the neurobiologic or behavioral phenotypes associated with these diseases. An example of such data validation comes from our work on the combined mRNA and miRNA profiles of laser-captured substantia nigra dopamine neurons in Parkinson’s disease (Simunovic et al., 2009, 2010; Sonntag, 2010; Sonntag et al., 2012; Kim et al., 2014; Briggs et al., 2015). Computational analysis of these profiles delineated a comprehensive regulatory mRNA/ miRNA network and the identification of several miRNA target gene relationships, which may have functional implications in disease pathogenesis (Kim et al., 2014, 2016; Briggs et al., 2015). To demonstrate this experimentally, we analyzed one miRNA, miR-126, which was upregulated in Parkinson’s disease and targets factors in insulin/insulin growth factor-1 signaling cascades. In a series of experiments, we found that elevated levels of this miRNA are neurotoxic and increase the vulnerability of neurons to a variety of nonspecific and disease-specific toxic factors,

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Fig. 18.2. Densities of UBE3B-immunoreactive cells (A) and UBE3B-immunoreactive pyramidal cells (B) are significantly decreased by 22.3 % ( p ¼ 0.026) and 48.0% ( p ¼ 0.022), respectively, in schizophrenia (1483  437/mm2 and 594  328/ mm2, respectively) compared to normal control (1909  546/mm2 and 1136  364/mm2, respectively) subjects. (C) Photomicrographs showing the developmental increase in UBE3B expression in the normal human prefrontal cortex. Scale bar ¼ 100 mm. (D) The density of UBE3B-immunoreactive cells in the prefrontal cortex progressively increases during postnatal development.

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including staurosporine, Alzheimer’s disease-associated amyloid beta 1–42 oligomers, and 6-hydroxydopamine, which induces oxidative stress in dopamine neurons, thereby mimicking Parkinson’s disease pathophysiology. Mechanistically, miR-126 targets a series of factors in PI3K/AKT/GSK-3b and MAPK/ERK signaling cascades and small increases of this miRNA cause a downregulation of these pathways, impairing the effects of neurotrophic and neuroprotective growth factors, such as insulin growth factor-1, nerve growth factor, brainderived neurotrophic factor, and soluble amyloid precursor protein a. In turn, inhibiting miR-126 enhances the actions of growth factors without disturbing normal neuronal cell function, suggesting that targeting this miRNA may have therapeutic potential for neurologic and agerelated disorders. Thus, starting from profiling the molecular compositions of laser-captured homogeneous cell populations in postmortem human brains, we have identified a novel mechanism in neurons, mediated by miR-126, that regulates the effects of numerous neurotrophic and neuroprotective growth factors and may therefore play a profound role in neuronal cell function and survival, at least in part by regulating GF/PI3K/AKT and MAPK/ERK signaling (http://biomedfrontiers.org/ alzheimer-2015-5-9/).

SUMMARY In this chapter, we have reviewed the currently available laser microdissection platforms. We also discussed factors that may affect the success of the combined laser microdissection and gene expression-profiling experiments, such as the cellular purity of the sample, the quality of the RNA, the methodologies of data normalization and computational data analysis, data interpretation and validation. If successfully executed, this type of studies has the potential of leading to deeper understanding of cell type-specific pathophysiologic mechanisms of neuropsychiatric disorders and to the conceptualization of novel treatments that aim at restoring cellular functions.

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