Single-Cell Methylomes Offer Clues to Aging
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A new single-cell omics model might help scientists better understand brain function. As reported in the September 24 Nature Neuroscience, researchers led by Kate Meyer, Duke University, Durham, North Carolina, profiled the mRNA methylomes of 7,702 cells from the mouse cerebral cortex. They found that transcripts are less methylated in microglia than in other cells, that methylation patterns distinguish neuronal subtypes, and that, for thousands of transcripts, including some linked to cognition or neurodegeneration, this post-transcriptional modification changes with age. Of 15 nucleotides on the APP transcript that are methylated, 14 ditch the hydrocarbon once mice reach 15 months. What that means for protein production remains to be seen.
- New mouse model enables single-cell messenger RNA methylomics.
- Modified transcripts distinguish cell types and subtypes.
- APP mRNA became hypomethylated with age.
Methylation of nitrogen 6 in the adenosine ring, or m6A, is the most common chemical change cells make to their own mRNA. The modification can be both physiological and pathological, and bulk analysis shows that it occurs in the brain more than in other tissues and organs. But if, and how, methylation patterns differ among brain cells has been difficult to tease out because it typically requires a lot of RNA for analysis.
Now, Meyer and colleagues have found a way to map m6A sites that is compatible with single-cell RNA-Seq. They constructed a chimera comprising the cytidine deaminase APOBEC1 and the YTH binding domain found in m6A “readers.” Readers are proteins that bind methylated sites to regulate mRNA translation. They form a troika with “writers,” aka methylases, and “erasers,” aka de-methylases, to control all manner of biological processes.
Expressed in cells, the reader component of this chimera found m6A sites, then the deaminase converted adjacent cytidines to uracil, which is detectable by RNA-Seq. The authors called the method DART, or deamination adjacent to RNA modification targets.
First author Matthew Tegowski and colleagues aimed this dart at mice. They engineered animals to ubiquitously express a version of APOBEC1-YTH that can be induced by giving the animal tamoxifen. Once turned on, it robustly converted cytidine-to-uracil throughout the brain. Bulk DART-Seq identified almost 18,000 methylation sites in the cortex, about 13,500 in the hippocampus, and nearly 11,400 in the cerebellum. Pairwise comparison between these regions identified up to 3,000 sites that were differentially methylated.
Which cells might account for the difference? Tegowski turned to single-cell DART-Seq, finding 28,412 distinct RNA methylation sites among 7,702 cells in the cortex. Most of the 11 main cell types had about the same number of methylated transcripts, except microglia. They stood out as having far less, despite expressing equivalent levels of the DART machinery as did other cells (image at right). The authors confirmed the dearth of methylation in purified microglia using mass spectrometry of polyA RNA, and a chemical assay of total RNA (Liu et al., 2023).
Did cells methylate the same RNAs? For the most part, yes. Among cells of the same type, fewer than 20 transcripts were found to be differentially methylated in a cell-to-cell comparison. Things were more heterogenous when comparing different cell types, however. Compared to astrocytes, glutamatergic neurons methylated an additional 200 RNAs.
Further, there were some transcripts, particularly of immediate early genes involved in cell signaling, that were differentially methylated among neurons of different cortical layers. Clustering by methylation, rather than by transcriptome, Tegowski identified 18 distinct subtypes of glutamatergic neuron. Each had between 70 and 200 differentially methylated transcripts.
Some mRNAs differed at just one or two m6A sites, others at 10 or more. Some of those most differentially methylated play important roles in glutamatergic signaling, such as Grin2b and Gria2, which encode subunits 2b and 2 of the NMDA receptor and AMPA receptors, respectively. These m6A cluster subtypes were distinct from transcriptomics-based subtypes (image below).
A Different Type of Subtype. DART-Seq-based cluster analysis identified 18 subtypes of glutamatergic neurons (left, color-coded). Many are represented among distinct transcript-based subtypes (right), showing that m6A clusters do not merely reflect mRNA abundance. [Courtesy of Tegowski et al., Nature Neuroscience, 2024.]
The authors also found that methylation changed with age. Comparing methylomes of 8- to 9-week-old mice with those of 14- to 15-month-olds revealed little change in some cells, including microglia, pericytes, and smooth muscle cells, but hundreds of changes in other cells, including astrocytes, oligodendrocytes, GABAergic, and glutamatergic neurons.
The latter were most affected, with methylation of more than 800 mRNAs changing with age, most being hypermethylated. Those most differentially methylated included Gria2, APP, and Lrp1, which encodes a lipoprotein receptor that has been linked to AD (Mar 2020 news). Taking a closer look at APP, the scientists found that 14 of 15 or its methylation sites lose the methyl with age.
What could all this mean for protein expression and cell biology? Curiously, the authors found no link between methylation status and transcript levels. This was true across the different glutamatergic neuron subtypes, and for the transcripts that were most heavily or hyper- or hypomethylated, such as Grin2b in glutamatergic subtypes or APP in older mice. The authors think that the main role of methylation may not be to control RNA stability or abundance.
That said, this modification has been shown to control translation. “It is possible that m6A-mediated regulation of protein production or RNA localization helps drive functional outputs of the cellular heterogeneity we observed,” the authors noted. They also posit that methylation might affect APP in a pathological context or that the hypomethylation seen in microglia might change if these cells are activated. “… [I]t will be interesting to profile m6A methylation in the aged brain from Alzheimer's disease or other disease models and investigate whether differential methylation of App and other disease-associated transcripts impacts their expression,” they wrote.—Tom Fagan
References
News Citations
Paper Citations
- Liu C, Sun H, Yi Y, Shen W, Li K, Xiao Y, Li F, Li Y, Hou Y, Lu B, Liu W, Meng H, Peng J, Yi C, Wang J. Absolute quantification of single-base m6A methylation in the mammalian transcriptome using GLORI. Nat Biotechnol. 2023 Mar;41(3):355-366. Epub 2022 Oct 27 PubMed.
Further Reading
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Primary Papers
- Tegowski M, Prater AK, Holley CL, Meyer KD. Single-cell m6A profiling in the mouse brain uncovers cell type-specific RNA methylomes and age-dependent differential methylation. Nat Neurosci. 2024 Sep 24; PubMed.
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The University of Queensland
In recent years, scientists have started to be investigate the role of m6A-RNA regulation in brain aging and Alzheimer’s disease. Where, and how, m6A changes in the transcriptome are critical questions that have primarily been approached using bulk tissue analyses, coupled with an antibody-based immunoprecipitation, in animal models and human postmortem brain (Shafik et al., 2021; Huang et al., 2023; Castro-Hernández et al., 2023). This paper by Tegowski et al. reports the first instances of single-nucleotide and single-cell m6A-transcriptomic mapping of the mouse brain. The authors pioneered a tool for detecting m6A in cellular RNA that uses a modified RNA-editing enzyme, APOBEC, fused to the m6A-binding YTH domain. They call the method “DART,” ordeamination adjacent to RNA modification targets. Now, by generating a DART transgenic mouse line, the authors could perform in vivo labelling of m6A followed by single-cell sequencing (scDART-Seq) analysis, a much-anticipated tool for revealing the distribution of m6A in highly heterogenous tissue such as the brain.
Indeed, the scDART-Seq analysis revealed cell-specific regulation of m6A in the brain, particularly clustered within glutamatergic neuronal populations of different cortical layers. Changes in methylation in the aged mouse cortex also most prominently affected glutamatergic neurons, including transcripts associated with neurodegenerative disease. While neuronal cells generally had high levels of methylated sites, amongst all cell types in the mouse cortex, microglia appeared to be particularly deprived of m6A. As these are the brain-resident immune cells, this novel finding triggers questions regarding m6A and reactive microglia in neurodegenerative disease, where these cells undergo morphological, molecular, and functional remodelling in response to brain challenges.
Single-cell m6A-profiling datasets are valuable resources for the neuroscience community. The next exciting adventure for the DART mice would be to test distinct behavioural exposures or stressors and determine how the brain RNA methylome changes in cell-type- and stimulus-dependent manner in the context of health and disease.
References:
Shafik AM, Zhang F, Guo Z, Dai Q, Pajdzik K, Li Y, Kang Y, Yao B, Wu H, He C, Allen EG, Duan R, Jin P. N6-methyladenosine dynamics in neurodevelopment and aging, and its potential role in Alzheimer's disease. Genome Biol. 2021 Jan 5;22(1):17. PubMed.
Huang H, Song R, Wong JJ, Anggono V, Widagdo J. The N6-methyladenosine RNA landscape in the aged mouse hippocampus. Aging Cell. 2023 Jan;22(1):e13755. Epub 2022 Dec 9 PubMed.
Castro-Hernández R, Berulava T, Metelova M, Epple R, Peña Centeno T, Richter J, Kaurani L, Pradhan R, Sakib MS, Burkhardt S, Ninov M, Bohnsack KE, Bohnsack MT, Delalle I, Fischer A. Conserved reduction of m6A RNA modifications during aging and neurodegeneration is linked to changes in synaptic transcripts. Proc Natl Acad Sci U S A. 2023 Feb 28;120(9):e2204933120. Epub 2023 Feb 22 PubMed.
VIB-Center for Molecular Neurology
This study is very exciting and opens new ways to assess cell heterogeneity in the brain that were not possible before. We have, and are, learning a lot from single-cell transcriptomes, and these new technologies would allow us to dive more deeply into the mechanism governing cellular diversity in the healthy and diseased brain.
It is striking to see how different neuronal subtypes have differentially methylated RNAs, and it would be very interesting to link that to protein expression to see what would be the functional impact.
It is also interesting to see a generally lower level of methylation in microglia. This is, of course, only exploring cells in their steady state, and given the reactive nature of microglia, I would be very intrigued to see how this would change in disease conditions, as well as what role RNA methylation may play in different cell states.
The observation regarding the differential methylation of APP RNA in aging is very thought-provoking. This could indicate that over time, the dosage of APP may change, regardless of the levels of RNA expression, which could subsequently be contributing to amyloid accumulation in late-onset AD patients.
I feel that both the findings on microglia and APP deserve follow-up studies because they bring new insights into the biology of AD.
University of Alabama at Birmingham
Over the past decade, there have been significant technological advancements in single-cell transcriptome and epigenome profiling, as well as in spatial transcriptomics and proteomics. These methods have been crucial in understanding altered gene regulation mechanisms in Alzheimer’s disease (Anderson et al., 2023; Morabito et al., 2021; Mathys et al., 2024). Recently, global profiling of the epitranscriptome at the single-cell level has become possible with techniques like scDART-Seq, developed by the Meyer lab (Tegowski et al., 2022). In this new study, scDART-Seq was applied in vivo using a transgenic mouse model, enabling precise and sensitive profiling of m6A at the single cell level across any tissue and at any time.
In applying this method to mouse brain tissue, the authors found that m6A levels were generally consistent across most cell types, except for microglia, which exhibited significantly lower levels. As stated by the authors, this finding aligns with previous research indicating altered m6A levels in activated microglia populations, and it would be valuable to replicate these analyses in an Alzheimer’s disease mouse model (Li et al., 2021). Furthermore, the study showed that m6A modifications could distinguish subclusters within excitatory neurons independently of gene expression changes, particularly affecting genes involved in synaptic transmission. As noted by the authors, this could be attributed to the role of m6A in mRNA localization to neurites, essential for proper synapse function. It would be interesting to integrate these findings with spatial measurements of these mRNAs and their protein products to determine if this is indeed disrupting localization or local mRNA translation at the synapse.
Additionally, the study found age-related hypermethylation in glutamatergic neurons, while App m6A levels decreased with age specifically in this cell type. Understanding how these changes impact APP function, localization, or translation efficiency, particularly in the context of neurodegeneration, will be an important area for future research.
These technological advances in single-cell profiling enhance our ability to interpret how changes in the transcriptome, epitranscriptome, and epigenome in specific cell types contribute to neurodegeneration.
References:
Anderson AG, Rogers BB, Loupe JM, Rodriguez-Nunez I, Roberts SC, White LM, Brazell JN, Bunney WE, Bunney BG, Watson SJ, Cochran JN, Myers RM, Rizzardi LF. Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer's disease-specific cis-regulatory elements. Cell Genom. 2023 Mar 8;3(3):100263. Epub 2023 Feb 2 PubMed.
Li Q, Wen S, Ye W, Zhao S, Liu X. The potential roles of m6A modification in regulating the inflammatory response in microglia. J Neuroinflammation. 2021 Jul 5;18(1):149. PubMed.
Mathys H, Boix CA, Akay LA, Xia Z, Davila-Velderrain J, Ng AP, Jiang X, Abdelhady G, Galani K, Mantero J, Band N, James BT, Babu S, Galiana-Melendez F, Louderback K, Prokopenko D, Tanzi RE, Bennett DA, Tsai LH, Kellis M. Single-cell multiregion dissection of Alzheimer's disease. Nature. 2024 Aug;632(8026):858-868. Epub 2024 Jul 24 PubMed.
Morabito S, Miyoshi E, Michael N, Shahin S, Martini AC, Head E, Silva J, Leavy K, Perez-Rosendahl M, Swarup V. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer's disease. Nat Genet. 2021 Aug;53(8):1143-1155. Epub 2021 Jul 8 PubMed.
Tegowski M, Flamand MN, Meyer KD. scDART-seq reveals distinct m6A signatures and mRNA methylation heterogeneity in single cells. Mol Cell. 2022 Feb 17;82(4):868-878.e10. Epub 2022 Jan 25 PubMed.
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