Maintenance of age in human neurons generated by microRNA-based neuronal conversion of fibroblasts

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Version of Record published: October 17, 2016 (This version)
Accepted Manuscript published: September 20, 2016 (Go to version)
Accepted: September 15, 2016
Received: June 9, 2016

1. Of interest
Dynamic compartmentalization of the pro-invasive transcription factor NHR-67 reveals a role for Groucho in regulating a proliferative-invasive cellular switch in C. elegans

Taylor N Medwig-Kinney, Brian A Kinney ... David Q Matus

Research Article Dec 1, 2023
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Abstract

Aging is a major risk factor in many forms of late-onset neurodegenerative disorders. The ability to recapitulate age-related characteristics of human neurons in culture will offer unprecedented opportunities to study the biological processes underlying neuronal aging. Here, we show that using a recently demonstrated microRNA-based cellular reprogramming approach, human fibroblasts from postnatal to near centenarian donors can be efficiently converted into neurons that maintain multiple age-associated signatures. Application of an epigenetic biomarker of aging (referred to as epigenetic clock) to DNA methylation data revealed that the epigenetic ages of fibroblasts were highly correlated with corresponding age estimates of reprogrammed neurons. Transcriptome and microRNA profiles reveal genes differentially expressed between young and old neurons. Further analyses of oxidative stress, DNA damage and telomere length exhibit the retention of age-associated cellular properties in converted neurons from corresponding fibroblasts. Our results collectively demonstrate the maintenance of age after neuronal conversion.

https://doi.org/10.7554/eLife.18648.001

eLife digest

As we age, so do our cells. When cells are used in the laboratory to study the biology of diseases, it is important that the age of the cells reflects the age at which the disease develops. This is particularly important for illnesses with symptoms that develop during old age, and where younger cells may appear to be relatively unaffected.

Aging is a major risk factor in many brain disorders that affect elderly individuals. These late-onset disorders can be difficult to study because it is rarely possible to collect diseased cells from patients. Recent experimental advances, however, now mean that unrelated cell types – typically cells called fibroblasts, taken from a patient’s skin – can be converted directly into brain cells instead. These new brain cells will have the same genetic makeup as the patients they came from, but whether these converted cells would reflect the patient’s age too remained to be determined.

By measuring a range of biological properties of the converted cells, Huh et al. now show that converted cells do indeed keep track of their age when they are changed from fibroblasts to brain cells. The age of the cells was tested by looking at age-linked markers attached to their DNA known as an “epigenetic clock”. In addition, Huh et al. measured the age of the cells by examining the expression of genes altered with aging. Other factors examined included the amount of damaged DNA and the size of DNA regions called telomeres, which become shorter with age. Together, all of these indicated that the converted brain cells retain the age of the fibroblasts that they were made from.

So far this work has only been done using fibroblasts collected from healthy people. The same tests now need to be done using cells from people with late-onset illnesses like Huntington’s disease and Alzheimer’s disease. If the converted brain cells show signs of illness, it may provide new ways to study these illnesses using cells from specific patients, which may eventually lead to the development of new treatments.

https://doi.org/10.7554/eLife.18648.002

Introduction

Increasing evidence suggests that in addition to genetic susceptibility, age-related neurodegeneration may be caused in part by cellular aging processes that result in accumulation of damaged DNA and proteins in neurons (Mattson and Magnus, 2006). However, because of the inaccessibility to neurons from elderly individuals, studying these age-related cellular processes in human neurons remains a difficult task. Cellular reprogramming approaches have explored generating populations of human neurons by inducing pluripotent stem cells (iPSC) from human fibroblasts and subsequent differentiation into neurons (Takahashi and Yamanaka, 2006; Takahashi et al., 2007; Hanna et al., 2008; Hu et al., 2010). Importantly, this induction of pluripotency in adult fibroblasts reverts cellular age to an embryonic stage (Lapasset et al., 2011; Patterson et al., 2012) which remains even after differentiation into neurons (Patterson et al., 2012; Miller et al., 2013). While this is useful for modeling early developmental phenotypes (Lafaille et al., 2012; Lee et al., 2009), iPSC-derived cells have been reported to be unsuitable in recapitulating phenotypes selectively observed in aged cells (Mattson and Magnus, 2006; Vera and Studer, 2015). Recently, experimental manipulations to accelerate aging in iPSC-derived cells have been explored, for instance, by overexpressing progerin, a mutant form of lamin A observed in progeria syndrome, to force the detection of age-related pathophysiology of neurodegenerative disease (Arbab et al., 2014; Cornacchia and Studer, 2015).

Alternatively, we previously described a reprogramming paradigm using neuronal microRNAs (miRNAs), miR-9/9* and miR-124 (miR-9/9*-124), that exert reprogramming activities to directly convert human fibroblasts to specific mature neuronal subtypes (Richner et al., 2015; Victor et al., 2014; Yoo et al., 2011). Because this neuronal conversion is direct and bypasses pluripotent/multipotent stem cell stages, we reasoned that miR-mediated directly reprogrammed neurons would retain the age signature of the original donor. To assess the cellular age, we used the epigenetic clock method, which is a highly accurate biomarker of age based on DNA methylation (Horvath, 2013). Further, we evaluated age-associated signatures based on gene expression levels, miRNAs, and cellular readouts considered to be hallmarks of aging (López-Otín et al., 2013). Our thorough investigation into multiple age-associated signatures collectively demonstrate the maintenance of cellular age of the original donor during neuronal conversion and strongly suggest that directly converted human neurons can be advantageous for studying age-related neuronal disorders.

Results and discussion

MiRNA-mediated neuronal conversion across the age spectrum

Establishing robust reprogramming efficiency is essential prior to assessing age-related phenotypes in reprogrammed neurons. We therefore elected to test our recently developed conversion approach that utilizes miR-9/9*-124 and transcription factors to robustly generate a highly enriched population of striatal medium spiny neurons (MSNs) from the fibroblasts of donors of varying ages (Richner et al., 2015; Victor et al., 2014). Fibroblast samples from donors ranging from 3 days to 96 years of age were collected and expanded to match population doubling level (PDL) to eliminate any confounding variability introduced by sequential passaging (Campisi and d'Adda di Fagagna, 2007; Pazolli and Stewart, 2008), then subsequently transduced with miR-9/9*-124 with CTIP2, DLX1/2, and MYT1L (CDM) following our established protocol (Richner et al., 2015; Victor et al., 2014) (Figure 1A). Reprogrammed cells were then stained for neuronal markers, MAP2, TUBB3, NeuN and MSN markers, DARPP32 and GABA (Figure 1B–C). MAP2 and TUBB3-positive reprogrammed neurons exhibiting extensive neurite outgrowth represented approximately 70–80% of the cell population (Figure 1D), demonstrating the consistency of reprogramming efficiency in all fibroblast samples. Neurons reprogrammed from young and old fibroblasts exhibited fast inward currents and action potentials in monocultures without necessitating coculturing with glial cells or primary neurons (n = 11) (Figure 1E). In addition, the consistent upregulation of neuronal genes, including MAP2, NCAM, and voltage-gated sodium channels, and downregulation of fibroblast-associated genes were observed in reprogrammed neurons from both young and old cells (Figure 1—figure supplement 1). These results suggest the consistent applicability of miRNA-based neuronal reprogramming in fibroblasts of all ages.

Figure 1 with 1 supplement see all

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MicroRNA-mediated direct neuronal conversion applied to fibroblasts across the age spectrum.

(A) Schematic diagram of neuronal conversion of human fibroblast samples from individuals ranging from three days to 96 years of age. Primary fibroblasts were transduced with microRNA-9/9*-124 and the transcription factor cocktail CTIP2, DLX1/2, MYT1L (CDM) and analyzed for neuronal properties after 30 days. (B) Expression of pan-neuronal markers MAP2 (top) and TUBB3 (bottom) after neuronal conversion of human fibroblasts ranging in age. Scale bar = 50 µm. (C) Expression of pan-neuronal marker NeuN and medium spiny neuron-specific markers GABA and DARPP32 in reprogrammed neurons from fibroblasts aged 91-, 72-, 92-years respectively. (D) Immunostaining analysis of percentage of reprogrammed neurons positive for neuronal markers TUBB3 and MAP2 over DAPI signals (n = 200–300 per cell line). (E) Representative whole-cell current clamp recording of converted neurons from young (29 years old, left) and old (94 year old donor, right) donors. Converted human neurons in monoculture displayed multiple action potentials in response to step current injections at four weeks post-transduction. All reprogrammed neurons from old fibroblasts recorded (n = 11) fired APs in response to current injections (top). Representative traces of fast-inactivating inward currents recorded in voltage-clamp mode. Voltage steps ranged from +10 to +70 mV (bottom).

https://doi.org/10.7554/eLife.18648.003

Maintenance of epigenetic age during neuronal conversion

Aging largely influences the epigenetic landscape of cells (Oberdoerffer and Sinclair, 2007) with a number of genomic loci becoming differentially methylated with age (Horvath et al., 2012; Christensen et al., 2009). The epigenetic clock, which analyzes the methylation status of 353 specific CpG loci, has been shown to be a highly accurate age estimator that applies to all human organs, tissues, and cell types (Horvath, 2013). The epigenetic clock leads to an age estimate (in units of years) which is referred to as epigenetic age or DNA methylation (DNAm) age. We analyzed DNA methylation levels of neurons converted from 16 fibroblast samples aged three days to 96 years alongside 37 fibroblast samples aged three days to 94 years (Figure 2A). The actual chronological donor age was highly correlated with the estimated DNAm age of fibroblasts (correlation = 0.75) (Figure 2B) and of reprogrammed neurons (correlation = 0.82) (Figure 2B). Importantly, when the DNAm age of each reprogrammed neuron was compared to the DNAm age of the corresponding fibroblast, there was a near-perfect correlation (correlation = 0.91), which suggests that the epigenetic clock is unperturbed during miRNA-based neuronal reprogramming (Figure 2C) and supports the notion of age maintenance during direct neuronal conversion. By contrast, it is known that iPSC generation resets the epigenetic clock to an embryonic state since iPSCs have a DNAm age that is negative or close to zero (Horvath, 2013).

Figure 2

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Conservation of the epigenetic clock of reprogrammed neurons from human fibroblasts.

(A) Schematic diagram representing the hypothesis that the epigenetic clock of fibroblasts from different age groups is conserved in reprogrammed neurons after miR-mediated neuronal conversion. (B) Top: Predicted ages based on the methylation status (DNAm age) of fibroblasts plotted against the actual ages of fibroblasts (correlation = 0.75, p=9.1e-08). Middle: Predicted DNAm ages of reprogrammed neurons against actual ages of starting fibroblast, correlation = 0.82, p=1e-04. Bottom: Combined plot of DNAm ages of fibroblasts and DNAm ages of reprogrammed neurons against actual ages (correlation = 0.77, p=1.6e-11). (C) DNAm age of reprogrammed neurons plotted against the DNAm ages of the corresponding, starting fibroblast ages, correlation = 0.91 p=2.5e-06.

https://doi.org/10.7554/eLife.18648.005

Figure 2—source data 1 Output for sample information and DNAm ages for fibroblasts and reprogrammed neurons compared to original age.: https://doi.org/10.7554/eLife.18648.006
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‘Aging’ transcriptome and microRNAs in reprogrammed neurons

Given the broad variability in gene expression with age in multiple cell types (Berchtold et al., 2008; Lu et al., 2004; Fraser et al., 2005; Glass et al., 2013) and a recent demonstration of maintenance of age-associated transcriptomic changes in neurons directly converted by transcription factors (Mertens et al., 2015), we sought to determine whether age-associated transcriptomic changes could be detected after miR-9/9*-124-CDM-based neuronal conversion. The transcriptome and microRNA profiles of reprogrammed neurons from both young and old fibroblasts were analyzed alongside corresponding fibroblasts. Principal component analysis (PCA) of transcriptome revealed cell type-specific clustering of reprogrammed neurons versus fibroblasts, while age-associated segregation is observed in both fibroblasts and reprogrammed neurons (Figure 3A). A cohort of upregulated and downregulated genes with aging was commonly observed in both fibroblasts and converted neurons (Figure 3B), consistent with a previous report (Mertens et al., 2015). Gene ontology (GO) analysis of differentially expressed genes with age in reprogrammed neurons is enriched for terms associated with age-related biological processes (Figure 3—figure supplement 1), including vesicle-mediated transport (Wilmot et al., 2008), nervous system development (Lu et al., 2004) NF-kappaB transcription factor activity (Tilstra et al., 2011), regulation of apoptosis and inflammatory response (de Magalhães et al., 2009), and for genes previously identified to be associated with age in the human brain (Lu et al., 2004).

Figure 3 with 2 supplements see all

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Age-associated changes in transcriptome and microRNA profiles in reprogrammed neurons.

(A) Principle component analysis (PCA) of transcriptome profiling of reprogrammed neurons from young fibroblasts aged three days, five months one year (green) and from old fibroblasts aged 90, 92, and 92 years (blue) alongside corresponding young fibroblasts (black) and old fibroblasts (red) (FDR < 0.05). (B) Differentially expressed genes (DEGs) with age in fibroblasts (x axis) and in reprogrammed neurons (y axis). Age-associated DEGs in reprogrammed neurons shown in green, DEGs in fibroblasts shown in red, and commonly shared DEGs with age in both fibroblasts and reprogrammed neurons shown in blue. (C) PCA of miRNA profile in reprogrammed neurons from young fibroblasts aged three days, five months one year (green) and from old fibroblasts aged 90, 92, and 92 years (bue) alongside the corresponding young fibroblasts (black) and old fibroblasts (red) (FDR < 0.05). (D) MicroRNAs that are differentially regulated with age in both fibroblasts (red) and reprogrammed neurons (blue). Four microRNAs, miR-10a, miR-497, miR-10b, and miR-195, are upregulated with age, while 10 microRNAs are shown to be downregulated with age, p<0.05. (E) Validation of expression of miRNA expression upregulated with aging (miR-10a, miR-497, miR-195) in reprogrammed neurons from old fibroblasts over reprogrammed neurons from young fibroblasts (left). Validation of expression changes of miR-10a, miR-497, miR-195 in human striatum slices (middle) and human cortex slices (right) from old individuals compared to young individuals.

https://doi.org/10.7554/eLife.18648.007

Figure 3—source data 1 Raw data for qPCR for microRNA expression analysis.: https://doi.org/10.7554/eLife.18648.008
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Figure 3—source data 2 Full GO terms for age-regulated genes in reprogrammed neurons and for predicted targets of miR-10a-5p and miR-497-5p.: https://doi.org/10.7554/eLife.18648.009
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MicroRNA profiling similarly revealed distinct sample segregation both with age (young versus old) and cell type (fibroblasts versus reprogrammed neurons) (Figure 3C). Interestingly, we detected fourteen microRNAs commonly regulated with age in both fibroblasts and reprogrammed neurons, including miR-10a, miR-497, and miR-195, whose expression increased with age (Figure 3D). Because microRNAs have been implicated as global regulators of aging-associated cellular processes (Liu et al., 2012; Harries, 2014) through repression of existing target transcripts (He and Hannon, 2004; Pasquinelli, 2012; Lewis et al., 2005), we reasoned that these age-upregulated microRNAs may target and repress classes of genes found to be downregulated in old reprogrammed neurons. Indeed, GO analysis of predicted targets amongst age-downregulated transcripts in reprogrammed neurons (Figure 3B) for miR-10a-5p and miR-497-5p revealed terms associated with age such as metabolism and cellular death and survival (Figure 3—figure supplement 2). Our results suggest the potential role of miR-10a and miR-497 in regulating genes involved in cell death and survival, metabolic pathways, and DNA repair. While the exact role of these microRNAs in aging is unknown, miR-10a and miR-497 have been previously implicated in aging-associated cellular processes including inflammation, senescence, metabolism and telomerase activity (Kondo et al., 2016; Qin et al., 2012; Fang et al., 2010). Importantly, the increased expression of miR-10a, miR-497 and miR-195 detected in reprogrammed neurons was also validated by qPCR to be concordantly upregulated in human striatum and cortex samples from old individuals in comparison to young individuals (Figure 3E). These results further support the validity of reprogrammed neurons for detecting age-associated changes in microRNA network, mirroring changes observed in human brain.

Cellular biomarkers reveal maintenance of age in reprogrammed neurons

Directly converted neurons were additionally assayed for cellular hallmarks of aging, including oxidative stress, DNA damage and telomere erosion (López-Otín et al., 2013). Oxidative stress has been reported to increase with age, in part due to the accumulation of reactive oxygen species (ROS) (Keating, 2008; Prigione et al., 2010; Suhr et al., 2010; Cui et al., 2012). FACS analysis of ROS levels using fluorescent marker MitoSOX (Miller et al., 2013) revealed that old reprogrammed neurons have increased ROS levels compared to young reprogrammed neurons, mirroring the observed age-associated differences of ROS levels in fibroblasts (Figure 4A). Moreover, reprogrammed neurons were analyzed by Comet Assay, a single-cell gel electrophoresis technique that assesses DNA damage accumulation (Singh et al., 1990). Old reprogrammed neurons were found to have longer comet tail lengths, an age-associated property also observed in fibroblasts, that reflects more extensive DNA damage accumulation compared to young cells (Figure 4B). Additionally, neuronal conversion maintained the length of telomeres from starting fibroblast which is virtually unchanged (Figure 4C), in contrast to the progressive increase in length commonly observed with iPSC reprogramming, where telomeres reach a plateau of around 12–14 kilobases after a few cellular passages (Agarwal et al., 2010; Batista et al., 2011; Marion et al., 2009). Together, these cellular assays support the maintenance of aging marks in old reprogrammed neurons after direct conversion.

Figure 4

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Analysis of cellular biomarkers of age reveals conservation of neuron-specific aging in reprogrammed neurons.

(A) ROS levels visualized by MitoSOX and analyzed by FACs. Representative dot plot of reprogrammed neuron from 1-year-old fibroblast (left) and from 91-year-old fibroblast (right), y axis = FL2 channel (MitoSOX), x axis = FSC. Quantification of percent of cells positive for MitoSOX reveals a significant difference in fibroblasts with age in addition to reprogrammed neurons with age. ***p-value=0.0008; *p-value=0.019. (B) Representative images of comets indicating DNA damage from five month old fibroblasts (top left) versus old fibroblasts (top right) alongside reprogrammed neurons from 5-month-old fibroblasts (bottom left) and 92-year-old fibroblasts (bottom right). Quantification of tail lengths in fibroblasts and reprogrammed neurons with age *p-value=0.013, **p-value=0.004. (C) Telomere length analyses of reprogrammed neurons are maintained from corresponding starting fibroblasts from one year old, 56 year old, and 86 year old donors.

https://doi.org/10.7554/eLife.18648.012

Figure 4—source data 1 Raw data for mitoSOX and comet assay.: https://doi.org/10.7554/eLife.18648.013
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Conclusion

Our in-depth analyses of multiple age signatures provide evidence that neuronal conversion from human somatic cells sampled at different ages generates neurons that emulate the donors’ ages. In addition to the demonstration of age-associated transcriptomic changes reported previously (Mertens et al., 2015), our results provide novel insights into multiple key signatures associated with age— epigenetic, microRNA and cellular— that are consistently maintained in directly converted neurons. Because aging is a complex process affecting many hallmarks of a cell (López-Otín et al., 2013), our assessment of a broad spectrum of age-related markers suggests that directly converted neurons may serve as an alternative model of neuronal aging to iPSC-derived neurons, whose erasure of multiple aging-associated signatures precludes it from adequately modeling, especially, late-onset diseases. Whereas, future studies may investigate whether additional aging marks are similarly conserved in reprogrammed neurons to model different facets of aging. While miR-9/9*-124-based reprogramming can directly convert fibroblasts with similar efficiency to neurons, we note that the older fibroblasts have lower replicative potential in a culture. However, this does not impede in the conversion efficiency of old fibroblasts. MiRNA-mediated generation of aged neurons paves the road to direct conversion into specific neuronal subtypes to investigate the contribution of neuronal aging to late-onset neurodegenerative disorders.

Materials and methods

Cell culture, cell lines and population doubling level (PDL) matching

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The following fibroblast cell lines ranging in age from three day old to 96 year old were obtained from the NIA Aging Cell Repository at the Coriell Institute for Medical Research, Coriell ID, RRID#: AG08498, RRID:CVCL_1Y51; AG07095, RRID:CVCL_0N66; AG11732, RRID:CVCL_2E35; AG04060, RRID:CVCL_2A45; AG04148, RRID:CVCL_2A55; AG04349, RRID:CVCL_2A62; AG04379, RRID:CVCL_2A72; AG04056, RRID:CVCL_2A43; AG04356, RRID:CVCL_2A69; AG04057, RRID:CVCL_2A44; AG04055, RRID:CVCL_2A42; AG13349, RRID:CVCL_2G05; AG13129, RRID:CVCL_2F55; AG12788, RRID:CVCL_L632; AG07725, RRID:CVCL_2C46; AG04064, RRID:CVCL_L624; AG04059, RRID:CVCL_L623; AG09602, RRID:CVCL_L607; AG16409, RRID:CVCL_V978; AG06234, RRID:CVCL_2B66; AG04062, RRID:CVCL_2A47; AG08433, RRID:CVCL_L625; AG16409, RRID:CVCL_V978; GM00302, RRID:CVCL_7277; AG01518, RRID:CVCL_F696; AG06234, RRID:CVCL_2B66. We routinely check all our cell cultures and confirm it to be free of mycoplasma contamination. Authentication was completed by LINE and PCR-based techniques. The International Cell Line Authentication Committee (ICLAC) lists none of these primary cells are commonly misidentified cell lines. Fibroblast cell lines were cultured and expanded in DMEM media (high glucose, Invitrogen) supplemented with 10% or 15% fetal bovine serum (Gibco), sodium pyruvate, non-essential amino acids, GlutaMAX (Invitrogen), Pen/Strep solution, and Beta-mercaptoethanol. Fibroblast cell lines were expanded to a population doubling level (PDL) of ~19–21. Formula to calculate PDL = 3.32*log (cells harvested/cells seeded) + previous PDL. Cells were kept frozen at −150°C in the above culture medium with additional 40% FBS and 10% DMSO.

MicroRNA-mediated neuronal reprogramming

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Human fibroblasts ranging in age from 3 days to 96-year old were transduced with a lentiviral preparation of the Doxycline-inducible synthetic cluster of miR-9/9* and miR-124 (miR-9/9*-124), alongside transcription factors CTIP2, DLX1, DLX2, and MYT1L as previously described (Richner et al., 2015; Victor et al., 2014). Briefly, transduced fibroblasts were maintained in fibroblast media for two days with doxycycline prior to selection with Puromycin (3 μg/ml) and Blasticidin (5 μg/ml) at day three, then were plated onto poly-ornithine, fibronectin and laminin-coated coverslips at day five. Cells were subsequently maintained in Neuronal Media (ScienCell, Carlsbad, CA) supplemented with valproic acid (1 mM), dibutyryl cAMP (200 μM), BDNF (10 ng/ml), NT-3 (10 ng/ml), and RA (1 μM) for 30–35 days before analysis.

Immunocytochemistry

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Reprogrammed neurons were fixed with 4% paraformaldehyde (Electron Microscopy Sciences, Hatfield, PA) for 20 min at room temperature (RT), then permeabilized with 0.2% Triton X-100 for 10 min at room temperature. Cells were blocked with 1% goat serum, incubated with primary antibodies at 4°C overnight, then incubated with secondary antibodies for 1 hr at RT. Primary antibodies used for immunocytochemistry included chicken anti-MAP2 (Abcam Cat# ab5392 RRID:AB_21381531; 1:10,000 dilution), mouse anti-β-III tubulin (Covance Research Products Inc Cat# MMS-435P RRID:AB_2313773; 1:5000), rabbit anti-β-III tubulin (Covance Research Products Inc Cat# PRB-435P-100 RRID:AB_291637; 1:2000), chicken anti-NeuN (Aves Labs Cat# NUN RRID:AB_2313556; 1:500), rabbit anti-GABA (Sigma-Aldrich Cat# A2052 RRID:AB_477652; 1:2000), mouse anti-GABA (Sigma-Aldrich Cat# A0310 RRID:AB_476667, 1:500), and rabbit anti-DARPP32 (Santa Cruz Biotechnology Cat# sc-11365 RRID:AB_639000; 1:400). The secondary antibodies included goat anti-rabbit, mouse, or chicken IgG conjugated with Alexa-488, −594, or −647 (Thermo Fisher Scientific, Waltham, MA). Images were captured using a Leica SP5X white light laser confocal system with Leica Application Suite (LAS) Advanced Fluorescence 2.7.3.9723.

Electrophysiology

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Whole-cell patch-clamp recordings were performed at four weeks after transduction with miR-9/9*-124-CDM. Intrinsic neuronal properties were studied using the following solutions (in mM): Extracellular: 140 NaCl, 3 KCl, 10 Glucose, 10 HEPES, 2 CaCl₂ and 1 MgCl₂ (pH adjusted to 7.25 with NaOH). Intracellular: 130 K-Gluconate, 4 NaCl, 2 MgCl₂, 1 EGTA, 10 HEPES, 2 Na₂-ATP, 0.3 Na₃-GTP, 5 Creatine phosphate (pH adjusted to 7.5 with KOH). Membrane potentials were typically kept at −60 mV to −70 mV. In voltage-clamp mode, currents were recorded with voltage steps ranging from +10 mV to +80 mV. In current-clamp mode, action potentials were elicited by injection of step currents that modulated resting membrane potential from −20 mV to +80 mV. Local application of TTX (Sigma-Aldrich#T8024) was achieved using a multibarrel perfusion system with a port placed within 0.5 mm of the patched cell.

DNA extraction

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Reprogrammed neurons were harvested after 30 days of ectopic expression of miR-9/9*-124-CDM. DNA was extracted using phenol/chloroform/isoamyl alcohol followed by ethanol precipitation with a final concentration of 0.75M NaOAc and 2 μg of glycogen. DNA concentration was quantified using a standard curve with the Quant-iT dsDNA Assay Kit, broad range (Thermo Fisher Scientific, Waltham, MA) according to manufacturer’s instruction, while the DNA quality was determined by the ratio of absorbance of 260 nm and 280 nm at approximately 1.7–2.0.

Illumina DNA methylation array

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The bisulfite conversion was performed for fibroblasts and reprogrammed neurons using the Zymo Research EZ-96 DNA Methylation-Gold Kit (catalog #D5008). DNA methylation data were generated on the HumanMethylation450k Bead Chip (Illumina, San Diego, CA) according to the manufacturer's protocols. Scanning was performed via Illumina’s iScan system in conjunction with the Illumina Autoloader 2 robotic arm. DNA methylation levels (β values) were established by calculating the ratio of intensities between methylated (signal A) and un-methylated (signal B) sites. The β value was calculated from the intensity of the methylated (M corresponding to signal A) and un-methylated (U corresponding to signal B) sites, as the ratio of fluorescent signals β = Max(M,0)/[Max(M,0)+Max(U,0)+100]. β values range from 0 (completely un-methylated) to 1 (completely methylated). The data were normalized using the 'Noob' normalization method (Triche et al., 2013).

Epigenetic clock analysis

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The epigenetic clock method is an accurate measurement of chronological age in human tissues (Horvath, 2013). Epigenetic age was estimated using the published software tools (Horvath, 2013). An online age calculator can be found at the webpage, https://dnamage.genetics.ucla.edu. The epigenetic clock has been shown to capture aspects of biological age: the epigenetic age is predictive of all-cause mortality even after adjusting for a variety of known risk factors (Marioni et al., 2015; Christiansen et al., 2016; Horvath et al., 2015a). The utility of the epigenetic clock method has been demonstrated in applications surrounding cognitive function (Levine et al., 2015), obesity (Horvath et al., 2014), Down syndrome (Horvath et al., 2015b), HIV infection (Horvath and Levine, 2015), and Parkinson's disease (Horvath and Ritz, 2015).

Transcriptome and microRNA microarray

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Total RNA was extracted from reprogrammed neurons from young fibroblasts aged three days, five months, and one year and from old fibroblast aged 90, 92, and 92 years alongside corresponding starting fibroblast samples using TRIzol (Thermo Fisher Scientific, Waltham, MA) according to the manufacturer’s instruction and extracted using chloroform and ethanol precipitation. RNA quality was determined by the ratio of absorbance at 260 nm and 280 nm to be approximately 2.0. Samples for RNA microarray were then standardly prepped and labeled with Illumina TotalPrep kits (Thermo Fisher Scientific, Waltham, MA) for Agilent Human 4x44Kv1, while samples for microRNA microarray were prepared using Genisphere Flashtag labeling kits designed for Affymetrix miRNA 4.0 microarray. Standard hybridization and imagine scanning procedure were performed according to the manufacturer's protocol at Genome Technology Access Center at Washington University School of Medicine, St. Louis. The intensity of probes was imported into R environment and normalized by using package 'oligo'. Differentially expressed mRNA transcripts were identified by using package 'limma' with cut-off at adjusted p-value<0.01 and over logfc >1 fold expression change. For miRNA, the intensity of human-specific probes was isolated by using in-house python script, and were imported into R environment. Quantile normalization was performed by using 'preprocessCore' package, and differentially expressed miRNAs were identified by using package 'limma' with cut-off at adjusted p-value<0.01 and over one-fold expression change.

Quantitative PCR validation

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cDNA was generated from 4 ng of RNA using specific primer probes from TaqMan MicroRNA Assays (Thermo Fisher Scientific, Waltham, MA) and subsequently analyzed on a StepOnePlus Real-Time PCR System (AB Applied Biosystems, Foster City, CA). Expression data were normalized to RNU44 and analyzed using the 2^−ΔΔCT relative quantification method. QPCR validation of miRNA expression was conducted in reprogrammed neurons from old fibroblasts aged 89, 90, 91, 92, 92, 94 compared to reprogrammed neurons from young fibroblasts aged three days, five months, one, two, 12 years of age. QPCR experiments were conducted with human striatum and human cortex slices acquired from young individuals aged 9, 11, and 19 years compared to those from older individuals aged 83, 85, and 87 years.

MitoSOX

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MitoSOX Red Mitochondrial superoxide indicator (Thermo Fisher Scientific, Waltham, MA) was diluted to 15 μM and incubated with cells for 15 min at 37°C. Cells were washed three times with PBS, dissociated with 0.25% Trypsin, then stained with DAPI. If FACs was not conducted on the same day, cells were fixed with 4% paraformaldehyde for 20 min at room temperature. Samples were compared to untreated (unstained) fibroblast and reprogrammed neurons. Cell sorting was performed on a FACSCalibur and LsrFortessa (BD Biosciences), while quantification of the percent of the population of MitoSOX positive cells was performed using FlowJo X 10.0.7r2. Each plot on the graph represents an individual experiment with multiple reprogrammed neurons. Unpaired t-test analysis of 3 sets of experiments of reprogrammed neurons from young fibroblasts compared to reprogrammed neurons from old fibroblasts. Young samples included reprogrammed neurons from fibroblasts aged three days, five month, one year and two years, while old samples were from donors aged 86, 90, 91, 92A, and 92B years. Analyzed fibroblast samples include 1, 2, 91, 72, 74, and 94-year-old samples. P-values were calculated with the student t-test.

Comet assay

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Cells were prepared and analyzed using the CometAssay Kit (Trevigen) according to manufacturer’s instruction. Cells were harvested after 30 days of neuronal reprogramming using 0.25% Trypsin, then whole cells were embedded in molten LMAgarose onto slides prior to overnight incubation in lysis buffer. Slides were then run in gel electrophoresis at 20 volts for 30 min, then stained with SYBR Green and visualized by epifluorescence microscopy. Tails lengths were measured by drawing a region of interest and p-values were calculated with student t-test for reprogrammed neurons from old fibroblasts aged 91, 92A and 92B compared to reprogrammed neurons from young fibroblasts aged three days, five months, and one year old, while analyzed fibroblasts were aged five months, one year, 12, 72, 86, and 92 years of age.

Telomere analysis

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Genomic DNA was collected from reprogrammed neurons from fibroblasts from one year, 56, and 86 year old donors and the corresponding fibroblasts. The isolated genomic DNA was then digested with RsaI and HinfI and fractionated as described previously (Tomlinson et al., 2008). Membranes were prepared by Southern transfer and hybridized to a radioactively end-labelled (TTAGGG) 4 oligonucleotide probe as described previously (Batista et al., 2011).

Data availability

The following data sets were generated

(2016) Datasets from: Matintenance of age in human neurons generated by microRNA-based neuronal conversion of fibroblasts, DNA methylation and annotation, transcriptome and microRNAs
Available at Dryad Digital Repository under a CC0 Public Domain Dedication.

http://dx.doi.org/10.5061/dryad.t6096

References

1. Agarwal S
2. Loh YH
3. McLoughlin EM
4. Huang J
5. Park IH
6. Miller JD
7. Huo H
8. Okuka M
9. Dos Reis RM
10. Loewer S
11. Ng HH
12. Keefe DL
13. Goldman FD
14. Klingelhutz AJ
15. Liu L
16. Daley GQ
(2010) Telomere elongation in induced pluripotent stem cells from dyskeratosis congenita patients
Nature 464:292–296.
https://doi.org/10.1038/nature08792
- Google Scholar
(2014) Modeling motor neuron disease: the matter of time
Trends in Neurosciences 37:642–652.
https://doi.org/10.1016/j.tins.2014.07.008
- Google Scholar
1. Batista LF
2. Pech MF
3. Zhong FL
4. Nguyen HN
5. Xie KT
6. Zaug AJ
7. Crary SM
8. Choi J
9. Sebastiano V
10. Cherry A
11. Giri N
12. Wernig M
13. Alter BP
14. Cech TR
15. Savage SA
16. Reijo Pera RA
17. Artandi SE
(2011) Telomere shortening and loss of self-renewal in dyskeratosis congenita induced pluripotent stem cells
Nature 474:399–402.
https://doi.org/10.1038/nature10084
- Google Scholar
1. Berchtold NC
2. Cribbs DH
3. Coleman PD
4. Rogers J
5. Head E
6. Kim R
7. Beach T
8. Miller C
9. Troncoso J
10. Trojanowski JQ
11. Zielke HR
12. Cotman CW
(2008) Gene expression changes in the course of normal brain aging are sexually dimorphic
PNAS 105:15605–15610.
https://doi.org/10.1073/pnas.0806883105
- Google Scholar
1. Campisi J
2. d'Adda di Fagagna F
(2007) Cellular senescence: when bad things happen to good cells
Nature Reviews Molecular Cell Biology 8:729–740.
https://doi.org/10.1038/nrm2233
- Google Scholar
1. Christensen BC
2. Houseman EA
3. Marsit CJ
4. Zheng S
5. Wrensch MR
6. Wiemels JL
7. Nelson HH
8. Karagas MR
9. Padbury JF
10. Bueno R
11. Sugarbaker DJ
12. Yeh RF
13. Wiencke JK
14. Kelsey KT
(2009) Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context
PLoS Genetics 5:e1000602.
https://doi.org/10.1371/journal.pgen.1000602
- Google Scholar
1. Christiansen L
2. Lenart A
3. Tan Q
4. Vaupel JW
5. Aviv A
6. McGue M
7. Christensen K
(2016) DNA methylation age is associated with mortality in a longitudinal danish twin study
Aging Cell 15:149–154.
https://doi.org/10.1111/acel.12421
- Google Scholar
1. Cornacchia D
2. Studer L
(2015) Back and forth in time: Directing age in iPSC-derived lineages
Brain Research S0006-8993(15)00857-4.
https://doi.org/10.1016/j.brainres.2015.11.013
- Google Scholar
1. Cui H
2. Kong Y
3. Zhang H
(2012) Oxidative stress, mitochondrial dysfunction, and aging
Journal of Signal Transduction 2012:646354.
https://doi.org/10.1155/2012/646354
- Google Scholar
(2009) Meta-analysis of age-related gene expression profiles identifies common signatures of aging
Bioinformatics 25:875–881.
https://doi.org/10.1093/bioinformatics/btp073
- Google Scholar
1. Fang Y
2. Shi C
3. Manduchi E
4. Civelek M
5. Davies PF
(2010) MicroRNA-10a regulation of proinflammatory phenotype in athero-susceptible endothelium in vivo and in vitro
PNAS 107:13450–13455.
https://doi.org/10.1073/pnas.1002120107
- Google Scholar
(2005) Aging and gene expression in the primate brain
PLoS Biology 3:e274.
https://doi.org/10.1371/journal.pbio.0030274
- Google Scholar
1. Glass D
2. Viñuela A
3. Davies MN
4. Ramasamy A
5. Parts L
6. Knowles D
7. Brown AA
8. Hedman AK
9. Small KS
10. Buil A
11. Grundberg E
12. Nica AC
13. Di Meglio P
14. Nestle FO
15. Ryten M
16. Durbin R
17. McCarthy MI
18. Deloukas P
19. Dermitzakis ET
20. Weale ME
21. Bataille V
22. Spector TD
23. UK Brain Expression consortium
24. MuTHER consortium
(2013) Gene expression changes with age in skin, adipose tissue, blood and brain
Genome Biology 14:R75.
https://doi.org/10.1186/gb-2013-14-7-r75
- Google Scholar
1. Hanna J
2. Markoulaki S
3. Schorderet P
4. Carey BW
5. Beard C
6. Wernig M
7. Creyghton MP
8. Steine EJ
9. Cassady JP
10. Foreman R
11. Lengner CJ
12. Dausman JA
13. Jaenisch R
(2008) Direct reprogramming of terminally differentiated mature B lymphocytes to pluripotency
Cell 133:250–264.
https://doi.org/10.1016/j.cell.2008.03.028
- Google Scholar
1. Harries LW
(2014) MicroRNAs as mediators of the ageing process
Genes 5:656–670.
https://doi.org/10.3390/genes5030656
- Google Scholar
1. He L
2. Hannon GJ
(2004) MicroRNAs: small RNAs with a big role in gene regulation
Nature Reviews Genetics 5:522–531.
https://doi.org/10.1038/nrg1379
- Google Scholar
1. Horvath S
2. Erhart W
3. Brosch M
4. Ammerpohl O
5. von Schonfels W
6. Ahrens M
7. Heits N
8. Bell JT
9. Tsai P-C
10. Spector TD
11. Deloukas P
12. Siebert R
13. Sipos B
14. Becker T
15. Rocken C
16. Schafmayer C
17. Hampe J
(2014) Obesity accelerates epigenetic aging of human liver
PNAS 111:15538–15543.
https://doi.org/10.1073/pnas.1412759111
- Google Scholar
1. Horvath S
2. Garagnani P
3. Bacalini MG
4. Pirazzini C
5. Salvioli S
6. Gentilini D
7. Di Blasio AM
8. Giuliani C
9. Tung S
10. Vinters HV
11. Franceschi C
(2015b) Accelerated epigenetic aging in down syndrome
Aging Cell 14:491–495.
https://doi.org/10.1111/acel.12325
- Google Scholar
1. Horvath S
2. Levine AJ
(2015) HIV-1 infection accelerates age according to the epigenetic clock
Journal of Infectious Diseases 212:1563–1573.
https://doi.org/10.1093/infdis/jiv277
- Google Scholar
1. Horvath S
2. Pirazzini C
3. Bacalini MG
4. Gentilini D
5. Di Blasio AM
6. Delledonne M
7. Mari D
8. Arosio B
9. Monti D
10. Passarino G
11. De Rango F
12. D'Aquila P
13. Giuliani C
14. Marasco E
15. Collino S
16. Descombes P
17. Garagnani P
18. Franceschi C
(2015a) Decreased epigenetic age of PBMCs from Italian semi-supercentenarians and their offspring
Aging 7:1159–1170.
https://doi.org/10.18632/aging.100861
- Google Scholar
1. Horvath S
2. Ritz BR
(2015) Increased epigenetic age and granulocyte counts in the blood of Parkinson's disease patients
Aging 7:1130–1142.
https://doi.org/10.18632/aging.100859
- Google Scholar
1. Horvath S
2. Zhang Y
3. Langfelder P
4. Kahn RS
5. Boks MP
6. van Eijk K
7. van den Berg LH
8. Ophoff RA
(2012) Aging effects on DNA methylation modules in human brain and blood tissue
Genome Biology 13:R97.
https://doi.org/10.1186/gb-2012-13-10-r97
- Google Scholar
1. Horvath S
(2013) DNA methylation age of human tissues and cell types
Genome Biology 14:R115.
https://doi.org/10.1186/gb-2013-14-10-r115
- Google Scholar
1. Hu BY
2. Weick JP
3. Yu J
4. Ma LX
5. Zhang XQ
6. Thomson JA
7. Zhang SC
(2010) Neural differentiation of human induced pluripotent stem cells follows developmental principles but with variable potency
PNAS 107:4335–4340.
https://doi.org/10.1073/pnas.0910012107
- Google Scholar
1. Keating DJ
(2008) Mitochondrial dysfunction, oxidative stress, regulation of exocytosis and their relevance to neurodegenerative diseases
Journal of Neurochemistry 104:298–305.
https://doi.org/10.1111/j.1471-4159.2007.04997.x
- Google Scholar
1. Kondo H
2. Kim HW
3. Wang L
4. Okada M
5. Paul C
6. Millard RW
7. Wang Y
(2016) Blockade of senescence-associated microRNA-195 in aged skeletal muscle cells facilitates reprogramming to produce induced pluripotent stem cells
Aging Cell 15:56–66.
https://doi.org/10.1111/acel.12411
- Google Scholar
1. Lafaille FG
2. Pessach IM
3. Zhang SY
4. Ciancanelli MJ
5. Herman M
6. Abhyankar A
7. Ying SW
8. Keros S
9. Goldstein PA
10. Mostoslavsky G
11. Ordovas-Montanes J
12. Jouanguy E
13. Plancoulaine S
14. Tu E
15. Elkabetz Y
16. Al-Muhsen S
17. Tardieu M
18. Schlaeger TM
19. Daley GQ
20. Abel L
21. Casanova JL
22. Studer L
23. Notarangelo LD
(2012) Impaired intrinsic immunity to HSV-1 in human iPSC-derived TLR3-deficient CNS cells
Nature 491:769–773.
https://doi.org/10.1038/nature11583
- Google Scholar
1. Lapasset L
2. Milhavet O
3. Prieur A
4. Besnard E
5. Babled A
6. Aït-Hamou N
7. Leschik J
8. Pellestor F
9. Ramirez JM
10. De Vos J
11. Lehmann S
12. Lemaitre JM
(2011) Rejuvenating senescent and centenarian human cells by reprogramming through the pluripotent state
Genes & Development 25:2248–2253.
https://doi.org/10.1101/gad.173922.111
- Google Scholar
1. Lee G
2. Papapetrou EP
3. Kim H
4. Chambers SM
5. Tomishima MJ
6. Fasano CA
7. Ganat YM
8. Menon J
9. Shimizu F
10. Viale A
11. Tabar V
12. Sadelain M
13. Studer L
(2009) Modelling pathogenesis and treatment of familial dysautonomia using patient-specific iPSCs
Nature 461:402–406.
https://doi.org/10.1038/nature08320
- Google Scholar
1. Levine ME
2. Lu AT
3. Bennett DA
4. Horvath S
(2015) Epigenetic age of the pre-frontal cortex is associated with neuritic plaques, amyloid load, and Alzheimer's disease related cognitive functioning
Aging 7:1198–1211.
https://doi.org/10.18632/aging.100864
- Google Scholar
(2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets
Cell 120:15–20.
https://doi.org/10.1016/j.cell.2004.12.035
- Google Scholar
1. Liu N
2. Landreh M
3. Cao K
4. Abe M
5. Hendriks GJ
6. Kennerdell JR
7. Zhu Y
8. Wang LS
9. Bonini NM
(2012) The microRNA miR-34 modulates ageing and neurodegeneration in Drosophila
Nature 482:519–523.
https://doi.org/10.1038/nature10810
- Google Scholar
1. Lu T
2. Pan Y
3. Kao SY
4. Li C
5. Kohane I
6. Chan J
7. Yankner BA
(2004) Gene regulation and DNA damage in the ageing human brain
Nature 429:883–891.
https://doi.org/10.1038/nature02661
- Google Scholar
(2013) The hallmarks of aging
Cell 153:1194–1217.
https://doi.org/10.1016/j.cell.2013.05.039
- Google Scholar
1. Marion RM
2. Strati K
3. Li H
4. Tejera A
5. Schoeftner S
6. Ortega S
7. Serrano M
8. Blasco MA
(2009) Telomeres acquire embryonic stem cell characteristics in induced pluripotent stem cells
Cell Stem Cell 4:141–154.
https://doi.org/10.1016/j.stem.2008.12.010
- Google Scholar
1. Marioni RE
2. Shah S
3. McRae AF
4. Chen BH
5. Colicino E
6. Harris SE
7. Gibson J
8. Henders AK
9. Redmond P
10. Cox SR
11. Pattie A
12. Corley J
13. Murphy L
14. Martin NG
15. Montgomery GW
16. Feinberg AP
17. Fallin MD
18. Multhaup ML
19. Jaffe AE
20. Joehanes R
21. Schwartz J
22. Just AC
23. Lunetta KL
24. Murabito JM
25. Starr JM
26. Horvath S
27. Baccarelli AA
28. Levy D
29. Visscher PM
30. Wray NR
31. Deary IJ
(2015) DNA methylation age of blood predicts all-cause mortality in later life
Genome Biology 16:25.
https://doi.org/10.1186/s13059-015-0584-6
- Google Scholar
1. Mattson MP
2. Magnus T
(2006) Ageing and neuronal vulnerability
Nature Reviews Neuroscience 7:278–294.
https://doi.org/10.1038/nrn1886
- Google Scholar
1. Mertens J
2. Paquola AC
3. Ku M
4. Hatch E
5. Böhnke L
6. Ladjevardi S
7. McGrath S
8. Campbell B
9. Lee H
10. Herdy JR
11. Gonçalves JT
12. Toda T
13. Kim Y
14. Winkler J
15. Yao J
16. Hetzer MW
17. Gage FH
(2015) Directly reprogrammed human neurons retain aging-associated transcriptomic signatures and reveal age-related nucleocytoplasmic defects
Cell Stem Cell 17:705–718.
https://doi.org/10.1016/j.stem.2015.09.001
- Google Scholar
1. Miller JD
2. Ganat YM
3. Kishinevsky S
4. Bowman RL
5. Liu B
6. Tu EY
7. Mandal PK
8. Vera E
9. Shim JW
10. Kriks S
11. Taldone T
12. Fusaki N
13. Tomishima MJ
14. Krainc D
15. Milner TA
16. Rossi DJ
17. Studer L
(2013) Human iPSC-based modeling of late-onset disease via progerin-induced aging
Cell Stem Cell 13:691–705.
https://doi.org/10.1016/j.stem.2013.11.006
- Google Scholar
1. Oberdoerffer P
2. Sinclair DA
(2007) The role of nuclear architecture in genomic instability and ageing
Nature Reviews Molecular Cell Biology 8:692–702.
https://doi.org/10.1038/nrm2238
- Google Scholar
1. Pasquinelli AE
(2012) MicroRNAs and their targets: recognition, regulation and an emerging reciprocal relationship
Nature Reviews Genetics 13:271–282.
https://doi.org/10.1038/nrg3162
- Google Scholar
1. Patterson M
2. Chan DN
3. Ha I
4. Case D
5. Cui Y
6. Van Handel B
7. Mikkola HK
8. Lowry WE
(2012) Defining the nature of human pluripotent stem cell progeny
Cell Research 22:178–193.
https://doi.org/10.1038/cr.2011.133
- Google Scholar
1. Pazolli E
2. Stewart SA
(2008) Senescence: the good the bad and the dysfunctional
Current Opinion in Genetics & Development 18:42–47.
https://doi.org/10.1016/j.gde.2007.12.002
- Google Scholar
1. Prigione A
2. Fauler B
3. Lurz R
4. Lehrach H
5. Adjaye J
(2010) The senescence-related mitochondrial/oxidative stress pathway is repressed in human induced pluripotent stem cells
Stem Cells 28:721–733.
https://doi.org/10.1002/stem.404
- Google Scholar
1. Qin B
2. Yang H
3. Xiao B
(2012) Role of microRNAs in endothelial inflammation and senescence
Molecular Biology Reports 39:4509–4518.
https://doi.org/10.1007/s11033-011-1241-0
- Google Scholar
1. Richner M
2. Victor MB
3. Liu Y
4. Abernathy D
5. Yoo AS
(2015) MicroRNA-based conversion of human fibroblasts into striatal medium spiny neurons
Nature Protocols 10:1543–1555.
https://doi.org/10.1038/nprot.2015.102
- Google Scholar
1. Singh NP
2. Danner DB
3. Tice RR
4. Brant L
5. Schneider EL
(1990) DNA damage and rpair with age in individual human lymphocytes
Mutation Research/DNAging 237:123–130.
https://doi.org/10.1016/0921-8734(90)90018-M
- Google Scholar
1. Suhr ST
2. Chang EA
3. Tjong J
4. Alcasid N
5. Perkins GA
6. Goissis MD
7. Ellisman MH
8. Perez GI
9. Cibelli JB
(2010) Mitochondrial rejuvenation after induced pluripotency
PLoS One 5:e14095.
https://doi.org/10.1371/journal.pone.0014095
- Google Scholar
1. Takahashi K
2. Tanabe K
3. Ohnuki M
4. Narita M
5. Ichisaka T
6. Tomoda K
7. Yamanaka S
(2007) Induction of pluripotent stem cells from adult human fibroblasts by defined factors
Cell 131:861–872.
https://doi.org/10.1016/j.cell.2007.11.019
- Google Scholar
1. Takahashi K
2. Yamanaka S
(2006) Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors
Cell 126:663–676.
https://doi.org/10.1016/j.cell.2006.07.024
- Google Scholar
(2011)
NF-κB in aging and disease

Aging and Disease 2:449–465.
- Google Scholar
1. Tomlinson RL
2. Abreu EB
3. Ziegler T
4. Ly H
5. Counter CM
6. Terns RM
7. Terns MP
(2008) Telomerase reverse transcriptase is required for the localization of telomerase RNA to cajal bodies and telomeres in human cancer cells
Molecular Biology of the Cell 19:3793–3800.
https://doi.org/10.1091/mbc.E08-02-0184
- Google Scholar
(2013) Low-level processing of Illumina Infinium DNA Methylation BeadArrays
Nucleic Acids Research 41:e90.
https://doi.org/10.1093/nar/gkt090
- Google Scholar
1. Vera E
2. Studer L
(2015) When rejuvenation is a problem: challenges of modeling late-onset neurodegenerative disease
Development 142:3085–3089.
https://doi.org/10.1242/dev.120667
- Google Scholar
1. Victor MB
2. Richner M
3. Hermanstyne TO
4. Ransdell JL
5. Sobieski C
6. Deng PY
7. Klyachko VA
8. Nerbonne JM
9. Yoo AS
(2014) Generation of human striatal neurons by microRNA-dependent direct conversion of fibroblasts
Neuron 84:311–323.
https://doi.org/10.1016/j.neuron.2014.10.016
- Google Scholar
1. Wilmot B
2. McWeeney SK
3. Nixon RR
4. Montine TJ
5. Laut J
6. Harrington CA
7. Kaye JA
8. Kramer PL
(2008) Translational gene mapping of cognitive decline
Neurobiology of Aging 29:524–541.
https://doi.org/10.1016/j.neurobiolaging.2006.11.008
- Google Scholar
1. Yoo AS
2. Sun AX
3. Li L
4. Shcheglovitov A
5. Portmann T
6. Li Y
7. Lee-Messer C
8. Dolmetsch RE
9. Tsien RW
10. Crabtree GR
(2011) MicroRNA-mediated conversion of human fibroblasts to neurons
Nature 476:228–231.
https://doi.org/10.1038/nature10323
- Google Scholar

Decision letter

Jeremy Nathans

Reviewing Editor; Johns Hopkins University School of Medicine, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your manuscript "Maintenance of age in human neurons generated by micro-RNA-based neuronal conversion of fibroblasts" to eLife. Three experts reviewed your manuscript, and their assessments, together with my own, Jeremy Nathans (the Reviewing editor), form the basis of this letter. As you will see, overall, the reviewers were impressed with the importance of your work.

I am including the three reviews at the end of this letter, as there are a variety of specific and useful comments in them. One part of the data that appears problematic is the comet assay. See the detailed comments by reviewer #1. A second point is the extent to which your work is distinct from or is an advance beyond that reported by Mertens et al. [Cell Stem Cell 17: 705-718. (2015)]. See the comments by reviewer #2. A fuller description of the Mertens et al. data and its relation to yours would be useful for the reader. This could be done in the Discussion section.

Reviewer #1:

Huh et al. used microRNA based reprogramming to generate neurons from fibroblasts, starting with fibroblasts taken from humans over a range of ages, and claim that the reprogrammed neurons reflect the age of the donor based on epigenetic assays, DNA damage assays, gene expression patterns, microRNA expression patterns, telomere length, and other parameters. They convincingly show that the reprogramming works with cells of a range of ages, showing good staining of neuron-specific markers, and impressive changes in gene expression patterns from a fibroblast pattern to a neuronal pattern. They use a variety of assays to support the interpretation that the conversion maintains the "age" of the donor cells. In general, the data support their conclusions well. They use a DNA methylation assay as an assay of "epigenetic age" and show a reasonable correlation between fibroblast age and epigenetic age as measured in the converted neurons.

In their gene expression analysis, the PCA analysis seems to nicely separate converted neurons from fibroblasts as expected, but does not clearly separate older from younger cells of either type. The analysis of microRNA expression patterns does seem to separate older from younger of each type, though based upon a very small number of microRNA.

Regarding their GO analysis (Figure 3) of differentially expressed genes, they appear to refer to "GO terms associated with aging"; some of these terms obviously relate to aging (for example, aging), while many others are not obviously related to aging (negative regulation of metabolic processes, nervous system development, etc.) and it is not clear to me or well explained in the text where these come from or how they are related to aging per se. Please explain.

Comet assay: I am not an expert, but I found this unconvincing. There appears to be a big difference in loading that at least contributes to, if not accounts for, the claimed difference in comet tail length between young and old neurons. Both of the young assays (I assume these are "young" re-programmed neurons) seem underloaded compared to the older cells; and if we were to control in our mind's eye for this loading difference, I am not sure the lengths of the comet tails would really differ much or at all. At best, the difference seems extremely subtle, much less than is illustrated in the figure.

This figure would be stronger if the young re-programmed neurons were run against the young fibroblasts, and then old fibroblasts run against old neurons-this might be what is illustrated but the figure legend does not give enough information to understand it. But then they need to load equal amounts of DNA in each experiment, or explain why the assay is such that this is not a problem.

Reviewer #2:

This manuscript is a follow-up to a previous work from the same authors in which neuronal micro RNAs (miRNAs) were used to directly reprogram human fibroblasts into specific neuronal subtypes. The goal of this paper is to demonstrate that miRNA-derived neurons retain the age profile of their donors. Interestingly, fibroblasts from donors sampling a wide age range (from a few days old to 94 years old), after being reprogrammed into neurons retained the DNA methylation levels, telomere size, transcription profile, levels of DNA damage, etc. This result is in contrast to neurons differentiated from induced pluripotent stem cells (iPSCs) which reset their clock in terms of telomere size, gene expression profile, oxidative stress, etc.

This manuscript is straightforward and technically sound. The authors were thorough in testing all the important age markers and convincingly demonstrate the key point of the paper. However, this work is purely a cell characterization study and therefore only incremental (the actual miRNA conversion itself has been published in several articles before). More importantly, it has already been shown that fibroblasts reprogrammed into neurons (via a different route) retain the age-specific transcriptomic profile, see Mertens et al., Directly Reprogrammed Human Neurons Retain Aging-Associated Transcriptomic Signatures and Reveal Age-Related Nucleocytoplasmic Defects, Cell Stem Cell, 2015. This published paper has only casually been referenced. Along the same lines, in the Introduction, the study was motivated by the following sentence: "Because this neuronal conversion is direct and bypasses pluripotent / multipotent stem cell stages, we reasoned that directly reprogrammed neurons would retain the age signature of the original donor." This statement makes it seem as if this phenomenon has not already been shown before.

At this stage the paper is therefore only incremental and does not warrant publication in eLife. The miRNA-converted neurons could indeed potentially be a powerful tool in studying age-related diseases (just like the reprogramming method published by Mertens et al) and so, at this stage, it would be very valuable to already see a functional comparison between examples of iPSC and miRNA-generated neurons from a diseased patient or a more thorough study on the onset and specific causes of age-related changes.

A comment regarding the data in Figure 4B: the data points for the 1-yo and 56-yo examples are not clear and the retention of telomere lengths of fibroblasts and miRNA-generated neurons does not appear convincing. That said, the 86-yo point, which indeed is the most important point, is convincing. Moreover, a more detailed description of the image is warranted in the legend, for instance, what do the numbers on the left stand for?

Reviewer #3:

This manuscript builds on prior work from the Yoo lab to test whether miRNA coverted neuronal cells derived from fibroblasts retain age-related signatures. I have a few comments regarding this work:

1) It would be of interest to quantitatively examine the expression levels of INK4A locus genes (p16 and ARF) since their upregulation has also been associated with aging.

2) Regarding telomere rejuvenation upon iPS reprogramming, since the referenced papers were published (Agarwal et al., 2010 and Marion et al., 2009), it has been shown that the original telomere length is only incrementally reset with iPS reprogramming (Batista Nature 2011, Moon Nat Gen 2015 and several others). This should be corrected.

3) It may be important to acknowledge that the retention of molecular marks of aging may make it more difficult to do in vitro with these cells. For example, shorter telomere length from older individuals may limit the replicative potential. Along the same lines, I suppose it's important to acknowledge that there may be other features of aging that were not assayed here that may be altered during the conversion process.

https://doi.org/10.7554/eLife.18648.018

Author response

Thank you for pointing out important comments to be addressed in the revision. As you suggested, we addressed reviewer #1’s comments about the comet assay by conducting additional experiments in young and old fibroblasts, and providing further details about technical details employed in the assay. We also provided an extra paragraph illustrating the advances our paper makes beyond the paper by Mertens et al. in the Discussion as suggested by reviewer #2. We furthermore conducted additional analysis to address an interesting question posed in comment #1 by reviewer #3.

Reviewer #1:

We agree that the separation between older and younger cells of either cell type is not clearly demonstrated on this 2D PCA plot. We therefore replaced the previous 2D PCA plots with a 3D PCA plot that more clearly illustrates the segregation of samples not only based on cell type (fibroblast vs. reprogrammed neurons), but also based on age (young vs. old). We hope this PCA plot provides more clear evidence to show the transcriptomic differences between young and old fibroblasts and between young and old reprogrammed neurons.

Thank you for pointing out the number of microRNAs the PCA is based on. The bar graph in Figure 3D represents only the commonly upregulated and downregulated microRNAs with age observed in both fibroblasts and reprogrammed neurons; however, the PCA not only includes these commonly regulated microRNAs, but also includes microRNAs that change with age uniquely in reprogrammed neurons and in fibroblasts. We highlight the commonly changed microRNAs in Figure 3D to illustrate that the age-regulated microRNAs seen in fibroblasts is conserved after direct conversion in reprogrammed neurons.

We agree that we did not clearly explain the age-association of the GO terms for Figure 3—figure supplement 1. We have now included references that relate the cellular processes of select GO terms represented in the bar graph to aging in the Results and Discussion sections of the text.

Thank you for your suggestion. We agree that the differences with age in fibroblasts are important data to illustrate. We have now included the fibroblast data for the comet assay in Figure 4. Likewise, we also supplemented Figure 4 with fibroblast data for the MitoSOX experiment as well. We updated the “Results and Discussion” section of the text and figure legend to reflect these changes. To address the concern regarding the loading amounts, we agree we did not clearly explain in the text the concept of the comet assay. We have now updated our text to describe the comet assay as a single cell analysis. To this end, we load 1x10⁵ whole live cells onto agar then place onto a coverslip. After the cells are embedded in low melting-point agar, we incubate the coverslip in lysis buffer overnight, then we run it through gel electrophoresis to allow a voltage to run through. Each comet represents the DNA content of one cell and the more DNA damage that exists the further the DNA will travel, creating longer comets. Since we aren’t loading DNA specifically and rather whole individual cells, it is unlikely that the differences in comet tail length are attributable to technique such as loading error. We additionally stained the cells with SYBR green and visualized the cells with the same exposure, gain, and fluorescent intensity. We have also updated the figure legend to provide more information to better explain what we represent in the figure.

Reviewer #2:

At this stage the paper is therefore only incremental and does not warrant publication in eLife. The miRNA-converted neurons could indeed potentially be a powerful tool in studying age-related diseases (just like the reprogramming method published by Mertens et al) and so, at this stage, it would be very valuable to already see a functional comparison between examples of iPSC and miRNA-generated neurons from a diseased patient or a more thorough study on the onset and specific causes of age-related changes.

We have now included a more thorough discussion of the aforementioned paper in our Discussion. Specifically, we have added sentences about the microRNA-based conversion approach (which is a different method than the one used in the Mertens et al. study) reaching similar conclusions regarding the maintenance of age-associated transcriptomic changes of starting fibroblasts in reprogrammed neurons. We ensured that we provided proper references throughout the manuscript when we discuss transcriptome changes. However, transcriptome analysis is only a fraction of what we assessed in our study, and we respectively argue that our findings about epigenetic clock measurements and microRNA profiles (as well as cellular properties) are novel and important findings. Aging is a complex biological process and with the implication of utilizing directly converted human neurons to model late-onset diseases, it is important to characterize many hallmarks of aging to achieve a more comprehensive sense of the age status of the reprogrammed neurons, which has been accomplished in our study. The future goals in our lab are to investigate the contribution and role of age-regulated microRNAs in aging.

Thank you for your comments. We have updated our figure to indicate the numbers on the left of our image reflect the kilobase length. We also included the following explanation in our text to “Additionally, neuronal conversion maintained the telomere length of starting fibroblasts, which is virtually unchanged, (Figure 4B) in contrast to the progressive increase in length commonly observed with iPSC reprogramming, where telomeres reach a plateau of around 12-14 kilobases after a few cellular passages (Agarwal et al., 2010; Batista et al., 2011; Marion et al., 2009).”

Reviewer #3:

This manuscript builds on prior work from the Yoo lab to test whether miRNA coverted neuronal cells derived from fibroblasts retain age-related signatures. I have a few comments regarding this work:

1) It would be of interest to quantitatively examine the expression levels of INK4A locus genes (p16 and ARF) since their upregulation has also been associated with aging.

It is interesting to pursue whether CDKN2A levels change in fibroblasts or in reprogrammed neurons with age. To answer this question, we illustrate the age-associated fold-change of CDKN2A in fibroblasts and in reprogrammed neurons (according to our transcriptome profiling data in Figure 2). We further conducted a qPCR using two different primer sets to validate the microarray with qPCR.

Author response image 1

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Additionally, we also conducted immunostaining analysis of CDKN2A (Author response image 2). Collectively, we did not detect significant alteration in CDKN2A expression with aging by microarray and qPCR assays in both fibroblasts and converted neurons. However, we do detect a marked increase in CDKN2A in reprogrammed neurons compared to starting fibroblasts, perhaps a reflection of cells adopting a post-mitotic fate.

Author response image 2

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Thank you for your comments. We have updated our text to “Additionally, neuronal conversion maintained the telomere length of starting fibroblasts, which is virtually unchanged, (Figure 4B) in contrast to the progressive increase in length commonly observed with iPSC reprogramming, where telomeres reach a plateau of around 12-14 kilobases after a few cellular passages (Agarwal et al., 2010; Batista et al., 2011; Marion et al., 2009).”

Thank you for this suggestion. We have incorporated these potential limitations in the Conclusion section of the revised manuscript.

https://doi.org/10.7554/eLife.18648.019

Article and author information

Author details

Christine J Huh
1. Department of Developmental Biology, Washington University School of Medicine, St. Louis, United States
2. Program in Molecular and Cellular Biology, Washington University School of Medicine, St. Louis, United States
Contribution
CJH, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

Competing interests
The authors declare that no competing interests exist.
Bo Zhang

Department of Developmental Biology, Washington University School of Medicine, St. Louis, United States

Contribution
BZ, Conception and design, Analysis and interpretation of data, Drafting or revising the article

Competing interests
The authors declare that no competing interests exist.
Matheus B Victor
1. Department of Developmental Biology, Washington University School of Medicine, St. Louis, United States
2. Program in Neuroscience, Washington University School of Medicine, St. Louis, United States
Contribution
MBV, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

Competing interests
The authors declare that no competing interests exist.
Sonika Dahiya

Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States

Contribution
SD, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

Competing interests
The authors declare that no competing interests exist.
Luis FZ Batista
1. Department of Developmental Biology, Washington University School of Medicine, St. Louis, United States
2. Department of Medicine, Washington University School of Medicine, St. Louis, United States
Contribution
LFZB, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

Competing interests
The authors declare that no competing interests exist.
Steve Horvath
1. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, United States
2. Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, United States
Contribution
SH, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

Competing interests
The authors declare that no competing interests exist.
Andrew S Yoo

Department of Developmental Biology, Washington University School of Medicine, St. Louis, United States

Contribution
ASY, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents

For correspondence
yooa@wustl.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-0304-3247

Funding

National Institute on Drug Abuse (R25 DA027995)

Bo Zhang

National Institutes of Health (K99/R00)

Luis FZ Batista

Washington University in St. Louis (DDRCC)

Luis FZ Batista

National Institutes of Health (4R00HL114732-03)

Luis FZ Batista

Washington University in St. Louis (NIDDK P30 DK052574)

Luis FZ Batista

National Institutes of Health (1U34AG051425-01)

Steve Horvath

National Institutes of Health (5R01, AG042511-02)

Steve Horvath

National Institutes of Health (DP2NS083372-01)

Andrew S Yoo

Ellison Medical Foundation (AG-NS-0878-12)

Andrew S Yoo

Cure Alzheimer's Fund

Andrew S Yoo

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank the Genome Technology Access Center in the Department of Genetics at Washington University School of Medicine for help with processing transcriptome and microRNA microarrays. We also thank UCLA Neuroscience Genomics Core and Helen Ibsen for processing Illumina Methylation450K chips. We are grateful to Shin-ichiro Imai for helpful suggestions on the manuscript. LB is supported by the NIH K99/R00 award (4R00HL114732-03), Washington University DDRCC (NIDDK P30 DK052574) and grants from the V Foundation For Cancer Research, Edward Mallinckrodt Jr. Foundation, and the AA&MDS International Foundation. SH is supported by National Institutes of Health NIH/NIA 1U34AG051425-01 and 5R01 AG042511-02. ASY is supported by the NIH Director's Innovator Award (DP2NS083372-01) and Presidential Early Career Award for Scientists and Engineers, and grants from the Ellison Medical Foundation and Cure Alzheimer’s Fund.

Reviewing Editor

Jeremy Nathans, Johns Hopkins University School of Medicine, United States

Version history

Received: June 9, 2016
Accepted: September 15, 2016
Accepted Manuscript published: September 20, 2016 (version 1)
Version of Record published: October 17, 2016 (version 2)

Copyright

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.