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Aging and disease    2019, Vol. 10 Issue (6) : 1146-1158     DOI: 10.14336/AD.2019.0225
Orginal Article |
AmpliSeq Transcriptome of Laser Captured Neurons from Alzheimer Brain: Comparison of Single Cell Versus Neuron Pools
Wenjun Deng1,2,*, Changhong Xing1,3,*, Rob David4, Diego Mastroeni5, MingMing Ning1,2, Eng H Lo1, Paul D Coleman5,*
1Neuroprotection Research Laboratories, Departments of Radiology and Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02192, USA
2Clinical Proteomics Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
3Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
4Thermo-Fisher Scientific, Salem, MA 02114, USA
5ASU-Banner Neurodegenerative Disease Research Center, Biodesign Institute, Arizona State University, Tempe, AZ 85281, USA
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Alzheimer’s disease (AD) is the most common cause of dementia in older adults. However, the pathogenesis of AD remains to be fully understood and clinically effective treatments are lacking. Recent advances in single cell RNA sequencing offers an opportunity to characterize the heterogeneity of cell response and explore the molecular mechanism of complex diseases at a single cell level. Here, we present the application of the Ion AmpliSeq transcriptome approach to profile gene expression in single laser captured neurons as well as pooled 10 and 100 neurons from hippocampal CA1 of AD brains versus matching normal aged brains. Our results demonstrated the high sensitivity and high genome coverage of the AmpliSeq transcriptome in single cell sequencing. In addition to capturing the known changes related to AD, our data confirmed the diversity of neuronal profiles in AD brain, which allow the potential identification of single cell response that might be hidden in population analyses. Notably, we also revealed the extensive inhibition of olfactory signaling and confirmed the reduction of neurotransmitter receptors in AD hippocampus. We conclude that although single neuron data show more variance than data from 10 or 100 pooled neurons, single neuron data can be informative. These findings support the utility of the Ion AmpliSeq method for obtaining and analyzing gene expression data from single defined laser captured neurons.

Keywords AmpliSeq transcriptome      single neuron      Alzheimer's disease     
Corresponding Authors: Deng Wenjun,Xing Changhong,Coleman Paul D   
Just Accepted Date: 12 April 2019   Issue Date: 16 November 2019
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Deng Wenjun
Xing Changhong
David Rob
Mastroeni Diego
Ning MingMing
Lo Eng H
Coleman Paul D
Cite this article:   
Deng Wenjun,Xing Changhong,David Rob, et al. AmpliSeq Transcriptome of Laser Captured Neurons from Alzheimer Brain: Comparison of Single Cell Versus Neuron Pools[J]. Aging and disease, 2019, 10(6): 1146-1158.
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SamplesMapped readsOn targetTargets detectedGenes Identified
Single neuron_15,416,54290.65%61.51%15,094
Single neuron_25,871,99581.56%64.82%15,586
Single neuron_35,748,51267.48%64.16%15,582
10 neurons_16,153,10983.52%66.98%16,375
10 neurons_25,793,32683.08%65.61%16,075
10 neurons_35,596,52884.35%65.69%16,662
100 neurons4,239,54489.57%64.90%16,764
Table 1  Summary of AD neuron transcriptome.
Figure 1.  Evaluation of AmpliSeq transcriptome sequencing results. (A) Comparison of gene identifications between Ion AmpliSeq technology and other RNA sequencing platform. (B) The distribution of gene abundance in single neuron and pooled 10 and 100 neurons (left) as well as in single neurons with different loading amount (right). (C) Pearson correlation of gene expression between technical replicates. (D) Comparison of gene expression quantified by AmpliSeq with the hippocampus neuron transcriptome measured in other species using different technologies.
Figure 2.  Gene expression diversity in different neuron sets. Pearson correlation of gene expression between replicate experiments (A) and across different neuron sets (B). (C) Principal component analysis of gene expression profiles in different neuron sets. (D) Standard deviation of gene expression within replicates binned according to gene expression levels (mean±95% CI).
PlatformNeuron originNeuron setsReplicatesIdentified genes
ThermoFisher Ion AmpliSeq transcriptomeHuman
hippocampus CA1
Single cell315,09415,58615,421283
10 cells316,07516,66216,371294
100 cells1N/AN/A16,764N/A

Illumina MiSeq
Krishnaswami et al. 2016
Human brainSingle cell63,3856,2674,5671,209

Illumina HiSeq 2000
Nichterwitz et al. 2016
motor neuron
Single cell48,4559,2088,795310
5 cells98,68712,23710,7721,040
10 cells510,60512,50011,482921

Illumina HiSeq 2500
Lacar et al. 2016
dentate granule cell
Single cell384,5538,7696,8431,135
Table 2  Comparison with other single neuron sequencing studies.
Figure 3.  The expression diversity of neuronal markers in different neuron sets. (A) The relative expression of neuron markers across the different neuron sets. (B) Examples of neuron marker expression in single neurons and pooled 10 and 100 neurons.
Figure 4.  AmpliSeq neuron transcriptome in AD versus control. (A) Principal component analysis of gene expression between AD and control neurons. (B) The expression levels of well-characterized AD genes in AD and control neurons. (C) Correlation of gene expression changes across different neuron sets. (D) The Venn diagram of the genes with expression change in AD brain across different neuron sets.
Figure 5.  AD-related functional alterations in different neuron sets. (A) Biological functions enriched with the genes with >2-fold expression changes in AD brain. (B) The heatmap of genes involved in apoptosis. (C) The expression levels of well-characterized pro-apoptotic genes in AD and control neurons.
Figure 6.  Olfactory transduction was significantly inhibited in AD neuron. (A) The heatmap of genes involved in olfactory signal transduction. (B) The expression distribution of olfactory receptor family in the genome of control and AD neuron. (C) The heatmap of significantly changed olfactory receptors. (D) The expression levels of the olfactory receptors with the highest abundance as well as β-arrestin 2 in AD and control neurons.
Figure 7.  Neurotransmitter receptors were reduced in AD neurons. (A) The functional classification of the genes involved in G-protein coupled receptor signaling pathway. (B) The expression levels of the receptors of dopamine, GABA and 5-hydroxytryptamine neurotransmission system.
[1] Hardy J, Selkoe DJ (2002). The amyloid hypothesis of Alzheimer's disease: progress and problems on the road to therapeutics. Science, 297:353-356.
[2] Guillozet AL, Weintraub S, Mash DC, Mesulam MM (2003). Neurofibrillary tangles, amyloid, and memory in aging and mild cognitive impairment. Arch Neurol, 60:729-736.
[3] Kim J, Basak JM, Holtzman DM (2009). The role of apolipoprotein E in Alzheimer's disease. Neuron, 63:287-303.
[4] Ballatore C, Lee VM, Trojanowski JQ (2007). Tau-mediated neurodegeneration in Alzheimer's disease and related disorders. Nat Rev Neurosci, 8:663-672.
[5] Castillo-Carranza DL, Nilson AN, Van Skike CE, Jahrling JB, Patel K, Garach P, et al. (2017). Cerebral Microvascular Accumulation of Tau Oligomers in Alzheimer's Disease and Related Tauopathies. Aging Dis, 8:257-266.
[6] Mastroeni D, Sekar S, Nolz J, Delvaux E, Lunnon K, Mill J, et al. (2017). ANK1 is up-regulated in laser captured microglia in Alzheimer's brain; the importance of addressing cellular heterogeneity. PLoS One, 12:e0177814.
[7] Lam B, Masellis M, Freedman M, Stuss DT, Black SE (2013). Clinical, imaging, and pathological heterogeneity of the Alzheimer's disease syndrome. Alzheimers Res Ther, 5:1.
[8] Badhwar A, Brown R, Stanimirovic DB, Haqqani AS, Hamel E (2017). Proteomic differences in brain vessels of Alzheimer's disease mice: Normalization by PPARgamma agonist pioglitazone. J Cereb Blood Flow Metab, 37:1120-1136.
[9] Xu Y, Liu X, Shen J, Tian W, Fang R, Li B, et al. (2018). The Whole Exome Sequencing Clarifies the Genotype- Phenotype Correlations in Patients with Early-Onset Dementia. Aging Dis, 9:696-705.
[10] Rosi S (2018). The role of peripherally derived monocytes in the aging injured brain. Cond Med, 1:350-354.
[11] Leak RK (2018). Conditioning Against the Pathology of Parkinson's disease. Cond Med, 1:143-162.
[12] Keren-Shaul H, Spinrad A, Weiner A, Matcovitch-Natan O, Dvir-Szternfeld R, Ulland TK, et al. (2017). A Unique Microglia Type Associated with Restricting Development of Alzheimer's Disease. Cell, 169:1276-1290 e1217.
[13] Artegiani B, Lyubimova A, Muraro M, van Es JH, van Oudenaarden A, Clevers H (2017). A Single-Cell RNA Sequencing Study Reveals Cellular and Molecular Dynamics of the Hippocampal Neurogenic Niche. Cell Rep, 21:3271-3284.
[14] Ofengeim D, Giagtzoglou N, Huh D, Zou C, Yuan J (2017). Single-Cell RNA Sequencing: Unraveling the Brain One Cell at a Time. Trends Mol Med, 23:563-576.
[15] Eberwine J, Yeh H, Miyashiro K, Cao Y, Nair S, Finnell R, et al. (1992). Analysis of gene expression in single live neurons. Proc Natl Acad Sci U S A, 89:3010-3014.
[16] Chow N, Cox C, Callahan LM, Weimer JM, Guo L, Coleman PD (1998). Expression profiles of multiple genes in single neurons of Alzheimer's disease. Proc Natl Acad Sci U S A, 95:9620-9625.
[17] Li W, Turner A, Aggarwal P, Matter A, Storvick E, Arnett DK, et al. (2015). Comprehensive evaluation of AmpliSeq transcriptome, a novel targeted whole transcriptome RNA sequencing methodology for global gene expression analysis. BMC Genomics, 16:1069.
[18] Papp AC, Azad AK, Pietrzak M, Williams A, Handelman SK, Igo RP Jr., et al. (2018). AmpliSeq transcriptome analysis of human alveolar and monocyte-derived macrophages over time in response to Mycobacterium tuberculosis infection. PLoS One, 13:e0198221.
[19] Birdsill AC, Walker DG, Lue L, Sue LI, Beach TG (2011). Postmortem interval effect on RNA and gene expression in human brain tissue. Cell Tissue Bank, 12:311-318.
[20] Walker DG, Whetzel AM, Serrano G, Sue LI, Lue LF, Beach TG (2016). Characterization of RNA isolated from eighteen different human tissues: results from a rapid human autopsy program. Cell Tissue Bank, 17:361-375.
[21] Huang da W, Sherman BT, Lempicki RA (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 4:44-57.
[22] Krishnaswami SR, Grindberg RV, Novotny M, Venepally P, Lacar B, Bhutani K, et al. (2016). Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons. Nat Protoc, 11:499-524.
[23] Nichterwitz S, Chen G, Aguila Benitez J, Yilmaz M, Storvall H, Cao M, et al. (2016). Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling. Nat Commun, 7:12139.
[24] Lacar B, Linker SB, Jaeger BN, Krishnaswami SR, Barron JJ, Kelder MJE, et al. (2016). Nuclear RNA-seq of single neurons reveals molecular signatures of activation. Nat Commun, 7:11022.
[25] Ianov L, De Both M, Chawla MK, Rani A, Kennedy AJ, Piras I, et al. (2017). Hippocampal Transcriptomic Profiles: Subfield Vulnerability to Age and Cognitive Impairment. Front Aging Neurosci, 9:383.
[26] Cembrowski MS, Wang L, Sugino K, Shields BC, Spruston N (2016). Hipposeq: a comprehensive RNA-seq database of gene expression in hippocampal principal neurons. Elife, 5:e14997.
[27] Readhead B, Haure-Mirande JV, Funk CC, Richards MA, Shannon P, Haroutunian V, et al. (2018). Multiscale Analysis of Independent Alzheimer's Cohorts Finds Disruption of Molecular, Genetic, and Clinical Networks by Human Herpesvirus. Neuron, 99:64-82 e67.
[28] Zeisel A, Munoz-Manchado AB, Codeluppi S, Lonnerberg P, La Manno G, Jureus A, et al. (2015). Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science, 347:1138-1142.
[29] Lake BB, Ai R, Kaeser GE, Salathia NS, Yung YC, Liu R, et al. (2016). Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science, 352:1586-1590.
[30] Liang WS, Dunckley T, Beach TG, Grover A, Mastroeni D, Ramsey K, et al. (2008). Altered neuronal gene expression in brain regions differentially affected by Alzheimer's disease: a reference data set. Physiol Genomics, 33:240-256.
[31] Ray M, Zhang W (2010). Analysis of Alzheimer's disease severity across brain regions by topological analysis of gene co-expression networks. BMC Syst Biol, 4:136.
[32] van Rooij JGJ, Meeter LHH, Melhem S, Nijholt DAT, Wong TH, Netherlands Brain B, et al. (2019). Hippocampal transcriptome profiling combined with protein-protein interaction analysis elucidates Alzheimer's disease pathways and genes. Neurobiol Aging, 74:225-233.
[33] Mosconi L (2013). Glucose metabolism in normal aging and Alzheimer's disease: Methodological and physiological considerations for PET studies. Clin Transl Imaging, 1.
[34] Liang WS, Reiman EM, Valla J, Dunckley T, Beach TG, Grover A, et al. (2008). Alzheimer's disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. Proc Natl Acad Sci U S A, 105:4441-4446.
[35] Meles SK, Pagani M, Arnaldi D, De Carli F, Dessi B, Morbelli S, et al. (2017). The Alzheimer's disease metabolic brain pattern in mild cognitive impairment. J Cereb Blood Flow Metab, 37:3643-3648.
[36] Herholz K, Haense C, Gerhard A, Jones M, Anton-Rodriguez J, Segobin S, et al. (2018). Metabolic regional and network changes in Alzheimer's disease subtypes. J Cereb Blood Flow Metab, 38:1796-1806.
[37] DeKosky ST, Scheff SW (1990). Synapse loss in frontal cortex biopsies in Alzheimer's disease: correlation with cognitive severity. Ann Neurol, 27:457-464.
[38] Selkoe DJ (2002). Alzheimer's disease is a synaptic failure. Science, 298:789-791.
[39] Coleman PD, Yao PJ (2003). Synaptic slaughter in Alzheimer's disease. Neurobiol Aging, 24:1023-1027.
[40] Xu Y, Yan J, Zhou P, Li J, Gao H, Xia Y, et al. (2012). Neurotransmitter receptors and cognitive dysfunction in Alzheimer's disease and Parkinson's disease. Prog Neurobiol, 97:1-13.
[41] Vassilaki M, Christianson TJ, Mielke MM, Geda YE, Kremers WK, Machulda MM, et al. (2017). Neuroimaging biomarkers and impaired olfaction in cognitively normal individuals. Ann Neurol, 81:871-882.
[42] Yu Q, Guo P, Li D, Zuo L, Lian T, Yu S, et al. (2018). Olfactory Dysfunction and Its Relationship with Clinical Symptoms of Alzheimer Disease. Aging Dis, 9:1084-1095.
[43] White LE (1965). Olfactory bulb projections of the rat. The Anatomical Record, 152:465-480.
[44] Cragg BG (1960). Responses of the hippocampus to stimulation of the olfactory bulb and of various afferent nerves in five mammals. Exp Neurol, 2:547-572.
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