Experimental Methods and Data‑Analysis Tools for Single‑Cell Epitranscriptome Profiling

Cui et al. Sig Transduct Target Ther 7, 334 (2022). 

The epitranscriptome—the collection of chemical modifications that decorate RNA molecules—adds a dynamic and complex layer of regulation to gene expression. More than 170 distinct RNA modifications have been catalogued, but until recently our ability to map these modifications was limited to bulk samples, obscuring the underlying cellular heterogeneity. Advances in single‑cell RNA sequencing (scRNA‑seq) coupled with bespoke chemical or enzymatic methods now permit the detection of epitranscriptomic marks at or near single‑base resolution in individual cells. This review summarises the current state of experimental approaches for profiling four of the most prevalent and functionally important modifications—N6‑methyladenosine (m6A), 5‑methylcytosine (m5C), pseudouridine (Ψ) and adenosine‑to‑inosine (A‑to‑I) editing—alongside the bioinformatic tools required to analyse such data. We highlight how these technologies are revealing the role of RNA modifications in development, ageing, host–pathogen interactions and across the tree of life, and we discuss the remaining technical and computational challenges that must be overcome to fully interpret the single‑cell epitranscriptome.

1 Single Cell Epitranscriptome Profiling: Basics

RNA molecules are not just passive messengers between DNA and protein. They are sculpted by a diverse set of chemical modifications that influence splicing, export, stability and translation. Collectively these marks constitute the epitranscriptome, a regulatory layer that is installed by writers, removed by erasers and interpreted by reader proteins. Although more than 170 modifications have been described, the majority of functional knowledge comes from abundant RNA species such as tRNAs and rRNAs because their high copy number facilitated early mapping efforts. Internal modifications on messenger RNAs (mRNAs)—notably m6A, m5C, Ψ and A‑to‑I editing—were reported decades ago but only became accessible to high‑throughput studies with the advent of next‑generation sequencing technologies. The distribution of m6A, for example, is biased toward the 3′ untranslated region and near the stop codon, underscoring the importance of precise localisation for understanding function.

Bulk epitranscriptomic studies average signals across thousands or millions of cells, masking cell‑to‑cell variability. In tissues with complex cellular compositions this averaging can confound interpretation—for instance, an apparent change in modification level might reflect a shift in cell‑type proportions rather than altered modification dynamics. By integrating scRNA‑seq with epitranscriptomic detection, researchers can now deconvolve this heterogeneity, correlating a cell’s identity with the modification landscape of its transcripts. These single‑cell approaches allow questions such as whether a particular modification guides lineage commitment, mediates stress responses or modulates viral replication to be addressed directly.

2 Experimental methodologies for single‑cell RNA modification profiling

The central technical obstacle for single‑cell epitranscriptomics is the minute amount of RNA available from a single cell. Methods developed for bulk samples often require microgram quantities of input and labour‑intensive enrichment steps that are incompatible with picogram‑scale starting material. To surmount these challenges, investigators have adapted existing techniques for low input or devised new strategies that bypass problematic steps altogether. Below, we outline key approaches for each modification and summarise their advantages and limitations.

2.1 Detecting m6A

Antibody‑based enrichment • Early m6A mapping relied on immunoprecipitation (m6A‑seq/MeRIP‑seq) in which fragmented RNA is incubated with an m6A‑specific antibody to enrich modified fragments. These methods provided transcriptome‑wide maps but offered only regional (~100–200 nt) resolution and required substantial input material. picoMeRIP‑seq optimises recovery from picogram‑scale input and has enabled profiling in scarce cell types (e.g., single oocytes) by minimising losses during purification. scm6A‑seq takes a multiplexing approach: RNA from individual cells is fragmented, dual‑barcoded and pooled for a single immunoprecipitation, reducing batch effects and enabling relative quantification across cells. However, antibody dependence and coarse resolution remain limitations.

Enzyme‑based detection • The antibody‑free method DART‑seq (Deamination Adjacent to RNA modification Targets sequencing) uses a fusion of the m6A‑binding YTH domain with the cytidine deaminase APOBEC1. When expressed in cells, this fusion enzyme induces C‑to‑U edits adjacent to m6A sites. These edits manifest as C‑to‑T mutations during sequencing, marking the modification with single‑base precision. Adaptations such as scDART‑seq or transgenic DART mice extend this approach to single cells in culture and in vivo. The main drawbacks are that the fusion protein must be introduced into the cells and that only m6A sites with nearby cytosines can be detected.

Direct RNA sequencing (DRS) • Oxford Nanopore Technologies’ sequencers measure ionic current as native RNA passes through a nanopore. Modified bases alter this current signature, allowing m6A to be detected without immunoprecipitation or reverse transcription. Machine‑learning models (e.g., m6Anet or SingleMod) have been trained to call modifications from these signals. While DRS provides single‑molecule, single‑base resolution and retains full‑length transcript information, its application to single cells is currently hampered by comparatively high input requirements, limited throughput and higher error rates.

2.2 Detecting m5C

Bisulfite sequencing • The gold standard for mapping m5C is RNA bisulfite sequencing (RNA‑BS‑seq). Sodium bisulfite converts unmodified cytosines to uracil, while methylated cytosines are resistant and remain unchanged. After reverse transcription and sequencing, genuine m5C sites appear as cytosines in the reads. The harsh chemical treatment degrades RNA, a serious problem for single‑cell samples. Variants such as UBS‑seq (Ultrafast Bisulfite Sequencing) use ammonium bisulfite to shorten reaction times and reduce degradation, but true single‑cell RNA‑BS‑seq remains a challenge. Antibody‑based m5C‑RIP‑seq and mechanism‑based approaches like Aza‑IP exist but suffer from low resolution or only detect targets of specific methyltransferases.

2.3 Detecting pseudouridine (Ψ)

CMC‑based chemical labelling • Ψ is traditionally mapped using N‑cyclohexyl‑N′‑(2‑morpholinoethyl)carbodiimide metho‑p‑toluenesulfonate (CMC). CMC reacts with Ψ to form a bulky adduct that causes reverse transcriptase to stall one nucleotide downstream. Techniques such as Pseudo‑Seq and PSI‑seq leverage these termination events to identify Ψ sites. While single‑base resolution is possible, the protocols require large amounts of RNA and multiple purification steps, making them incompatible with single‑cell samples. Recent improvements streamline library construction, but routine single‑cell detection is not yet feasible.

Cui et al. Sig Transduct Target Ther 7, 334 (2022). 

2.4 Detecting A‑to‑I editing

A‑to‑I editing, catalysed by ADAR enzymes, is unique among RNA modifications because it creates a base that base‑pairs like guanosine. During cDNA synthesis and sequencing, edited positions therefore appear as A‑to‑G mismatches relative to the reference genome. In principle, standard scRNA‑seq data contain information on editing events. In practice, low per‑cell coverage means that very few reads support any given site. To increase confidence, researchers pool reads from cells of the same type—creating “pseudo‑bulk” datasets—and use variant calling pipelines to distinguish true edits from genomic polymorphisms or sequencing errors. Although this sacrifices single‑cell resolution, it allows cell‑type‑specific editing patterns to be characterised without specialised library preparation.

Table 1 – Comparison of methods for single‑cell RNA modification detection

Modification	Representative method	Key principle	Strengths	Limitations
m6A	picoMeRIP‑seq	Antibody enrichment of fragments	Picogram‑scale input; no genetic modification required	Low spatial resolution; antibody dependency
m6A	scm6A‑seq	Dual barcoding and pooled immunoprecipitation	Reduces batch effects; relative quantification across cells	Low resolution; complex library prep
m6A	scDART‑seq	Fusion of m6A reader and cytidine deaminase induces C‑to‑U edits	Antibody‑free; single‑base resolution; compatible with in vivo models	Requires expression of fusion protein; only detects sites with nearby cytosines
m5C	UBS‑seq	Bisulfite conversion leaves methylated Cs untouched	Single‑base resolution; established chemistry	Harsh conditions degrade RNA; high input requirement
Ψ	Pseudo‑Seq/PSI‑seq	CMC adduct induces reverse‑transcription stops	Single‑base resolution	Requires high RNA input; labour‑intensive; not yet single‑cell compatible
A‑to‑I	scRNA‑seq plus variant calling	Inosine is read as guanosine	No special library prep; leverages existing scRNA‑seq datasets	Low coverage necessitates pseudo‑bulking; sensitive to SNP contamination

3 Computational pipelines for data analysis

Single‑cell epitranscriptomic datasets pose unique computational challenges. Unlike conventional scRNA‑seq data, reads may carry deliberate mismatches (e.g., C‑to‑T conversions in bisulfite sequencing or DART‑seq) or truncate at modification sites. Furthermore, the sparse capture of transcripts in single cells results in many missing values. There is currently no universal pipeline; instead, researchers assemble bespoke workflows using specialised tools at each step.

3.1 Alignment and quality control

Aligning reads to a reference genome is complicated by the presence of systematic mismatches. For bisulfite data, reference genomes with C‑to‑T conversions are used so that methylated and unmethylated cytosines align correctly. DART‑seq and similar data require tolerant alignment settings so that edited bases are not discarded as errors. Quality control involves standard scRNA‑seq metrics—library size, gene count per cell, fraction of mitochondrial reads—and modification‑specific metrics such as bisulfite conversion efficiency. Deduplication using unique molecular identifiers (UMIs) is essential to obtain accurate modification counts.

3.2 Modification site calling and quantification

The strategy for identifying modified sites depends on the experimental design:

• Peak‑based calling – For enrichment methods (e.g., MeRIP‑seq), algorithms adapted from ChIP‑seq (such as MACS2) identify peaks where read coverage is enriched over a control input. Specialised packages like MeTPeak or MeRIPtools implement statistical tests suited to m6A data.
• Variant‑based calling – For approaches that induce mutations (A‑to‑I, DART‑seq, bisulfite sequencing), variant callers are used to detect mismatches at specific positions. Tools such as scAllele or RESIC work with scRNA‑seq data but still require pooling across cells for adequate depth.
• Signal‑based calling – For nanopore DRS, machine‑learning models examine raw ionic current traces to classify each k‑mer as modified or unmodified. Software like m6Anet, SingleMod, Tombo or EpiNano are examples in this rapidly evolving domain.
• Predictive modelling – An alternative is to infer modification levels from gene expression and sequence context. Models such as Scm6A and SigRM use features like the expression of writers, erasers and readers, motif presence and local sequence composition to predict m6A status. While indirect, these approaches leverage vast existing scRNA‑seq datasets and circumvent the need for specialised library preparation.

3.3 Downstream analysis

Once modifications are called, they can be integrated into single‑cell analysis frameworks (e.g., Seurat or Scanpy) as additional “assays” alongside gene expression. Researchers cluster cells based on their modification profiles, perform trajectory inference to study dynamic changes and test for differential modification between cell types or conditions. The sparse and often binary nature of modification data necessitates new statistical models for differential analysis. Pipelines such as scDown aim to provide an end‑to‑end workflow, but the field remains fragmented and rapidly evolving.

Table 2 – Examples of bioinformatic tools for single‑cell epitranscriptomic analysis

Category	Tool	Function/Notes	Supported modifications
Peak calling	MeTPeak / MeRIPtools	Identify enriched regions from antibody‑based data	m6A (MeRIP‑seq)
Variant calling	scAllele, RESIC	Call SNVs or RNA edits from pooled scRNA‑seq	A‑to‑I, DART‑seq, SNPs
Bisulfite analysis	MethSCAn	Analyse single‑cell bisulfite‑seq (principles extend to RNA‑BS‑seq)	m5C
Nanopore analysis	m6Anet, SingleMod	Detect modifications from nanopore current signals	m6A (and other modifications)
Predictive modelling	SigRM, Scm6A	Infer modification status from gene expression and sequence	m6A
Downstream analysis	Seurat, Scanpy, scDown	Clustering, trajectory analysis and integration	Any derived modification matrix

4 Applications across the tree of life

Epitranscriptomic marks are conserved across kingdoms, but their functions and regulation are often lineage‑specific. Single‑cell approaches are uncovering how these modifications contribute to development, stress responses and host–pathogen interactions in diverse organisms.

4.1 Animal systems

Neurobiology – In the mouse brain, scDART‑seq has revealed cell‑type‑specific m6A landscapes. Microglia display notably low m6A levels compared with neurons and other glia, and many m6A sites show age‑dependent dynamics. These findings implicate m6A in neural development and ageing.

Early development – Low‑input methods such as picoMeRIP‑seq and scm6A‑seq have mapped the dynamic m6A landscape during oocyte maturation and early embryogenesis. These maps suggest that m6A deposition is tightly regulated during the earliest cell fate decisions.

Hematopoiesis – By pooling scRNA‑seq reads within clusters, researchers have identified lineage‑specific A‑to‑I editing patterns in human haematopoietic stem and progenitor cells. Editing at sites within the 3′ UTR of EIF2AK2 changes as cells commit to different lineages, hinting at a role for RNA editing in balancing self‑renewal and differentiation.

4.2 Plants

In plants, m6A influences flowering, embryogenesis and responses to abiotic stress. Although single‑cell epitranscriptomic studies in plants remain nascent, the core m6A machinery is conserved. Interestingly, the m6A consensus motif differs: while mammals often modify RRACH motifs, plants exhibit a URUAY motif and frequently possess an expanded repertoire of writer, eraser and reader genes, particularly in long‑lived tree species. Applying single‑cell methodologies to plant systems is expected to illuminate how these epitranscriptomic dialects mediate development and environmental adaptation.

4.3 Host–virus interactions

Viruses co‑opt and manipulate host epitranscriptomic machinery to their advantage. Herpesviruses such as HSV‑1 mislocalise the host m6A writer complex to reduce m6A levels on both viral and host transcripts, possibly dampening antiviral responses. Flaviviruses (e.g., Dengue and Zika) have been studied using viscRNA‑Seq, which captures both host and viral RNA from the same cell; this revealed extreme heterogeneity in viral load and identified host factors with virus‑specific effects. Coronaviruses, including SARS‑CoV‑2, decorate their genomes with m6A, pseudouridine and 2′‑O‑methylation to promote replication and evade innate immunity. Retroviruses such as HIV‑1 enhance host m6A writer activity, altering m6A patterns on specific host transcripts and modulating viral replication in a cell‑type‑specific manner. Single‑cell approaches are essential for dissecting these finely tuned interactions because viral burden and host responses vary dramatically between individual cells.

4.4 Microbial systems

Single‑cell epitranscriptomics in microbes is technically challenging but offers intriguing insights:

• Bacteria – Lack of poly(A) tails and overwhelming rRNA content complicate scRNA‑seq. Specialised protocols like MATQ‑seq and RiboD‑PETRI address these issues. Initial nanopore studies suggest that m6A on bacterial mRNA is scarce and may arise from off‑target activity of tRNA/rRNA methyltransferases rather than regulatory events. However, stress conditions can alter bacterial modification landscapes, indicating potential roles in adaptation.
• Archaea – Archaea feature unique modifications (e.g., archaeosine) and complex processing intermediates in their rRNAs. Nanopore sequencing has mapped many of these modifications, but the archaeal mRNA epitranscriptome remains largely unexplored.
• Fungi – Filamentous fungi perform widespread mRNA A‑to‑I editing using adenosine deaminases acting on tRNA (ADATs), especially during sexual reproduction. This editing frequently creates nonsynonymous codon changes and can provide fitness advantages by producing both edited and unedited protein isoforms. Combining single‑cell transcriptomics with epitranscriptomic mapping is beginning to reveal how fungal pathogens coordinate these processes during host infection.

5 Summary and outlook

Single‑cell epitranscriptomics has transitioned from concept to reality thanks to innovations in chemistry, enzymology and sequencing. These methods reveal cell‑type‑specific and dynamic patterns of RNA modifications that were invisible in bulk data and are beginning to illuminate how the epitranscriptome shapes development, ageing and immune responses. Nonetheless, significant hurdles remain. Sparse transcript capture forces reliance on pseudo‑bulk strategies that sacrifice true single‑cell resolution, and no routine single‑cell methods exist for some modifications (notably pseudouridine). The rapid pace of experimental development has outstripped progress in bioinformatics, leaving many groups to assemble ad hoc pipelines.

Looking ahead, the field is likely to progress along several fronts. Multi‑modal sequencing strategies that can detect multiple modification types on the same molecule will provide a more holistic view of RNA regulation. Spatial epitranscriptomics will situate modification patterns within their tissue context, clarifying how microenvironment influences the epitranscriptome. CRISPR‑based RNA editing tools, which fuse programmable Cas proteins to deaminases or methyltransferases, promise to enable targeted writing or erasing of modifications, allowing direct tests of causality. As methods mature and become more accessible, single‑cell epitranscriptomic profiling is poised to deliver new biomarkers for disease diagnosis, prognostication and therapy monitoring.

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Finally! The Bioinformatics Tools You've Been Waiting For

The field of RNA sequencing (RNA-Seq) has revolutionized our ability to understand gene expression and regulation, generating vast amounts of complex data. To effectively process, analyze, and interpret this data, a comprehensive suite of bioinformatics tools has been developed. These tools are meticulously designed to handle each distinct stage of the RNA-Seq workflow, transforming raw sequencing reads into meaningful biological insights. From initial quality control to advanced functional interpretation, the diverse array of available software ensures robust and accurate analysis, empowering researchers to unlock the secrets hidden within the transcriptome.

RNA-Seq data analysis involves multiple steps, and a wide array of bioinformatics tools have been developed to handle each stage. These tools can be broadly categorized by their function within the RNA-Seq workflow:

1. Quality Control and Pre-processing:

• FastQC: A widely used tool for quality control of raw sequencing reads, providing summaries of sequence quality, GC content, adapter content, and overrepresented sequences.
• MultiQC: Aggregates results from multiple QC tools (like FastQC) into a single, comprehensive report.
• Trimmomatic: Used for trimming low-quality bases, adapter sequences, and other unwanted sequences from reads.
• Cutadapt: Another popular tool for removing adapter sequences, primers, and poly-A tails.
• Picard: Provides various tools for manipulating and quality controlling SAM/BAM files, including checking read uniformity and GC content.
• RSeQC: Focuses on quality control of RNA-Seq data at various stages, including alignment and quantification.
• Qualimap: Performs quality control on alignment data.

2. Read Alignment/Mapping:

These tools align the RNA-Seq reads to a reference genome or transcriptome. They often account for splicing events (where exons are joined).

• STAR (Spliced Transcripts Alignment to a Reference): A highly popular and fast spliced aligner known for its accuracy in mapping splice junctions.
• HISAT2 (Hierarchical Indexing for Spliced Alignment of Transcripts): Another fast and memory-efficient spliced aligner.
• Bowtie/Bowtie2: General-purpose aligners, with Bowtie2 being suitable for aligning longer reads and supporting gapped alignments.
• BWA (Burrows-Wheeler Aligner): A software package for mapping low-divergent sequences to a large reference genome.

3. Quantification (Expression Estimation):

These tools quantify gene or transcript expression levels from aligned reads. They can be broadly divided into alignment-based and alignment-free (pseudoalignment) methods.

• Salmon: A highly popular and fast tool for quantifying transcript abundances using a pseudoalignment approach.
• Kallisto: Similar to Salmon, uses pseudoalignment for rapid and accurate quantification.
• RSEM (RNA-Seq by Expectation Maximization): Quantifies gene and isoform expression using an expectation-maximization algorithm.
• featureCounts: A widely used tool for counting reads that map to genomic features (e.g., genes, exons).
• HTSeq-count: Another tool for counting reads mapped to genomic features.
• StringTie/StringTie2: Can assemble transcripts and then quantify their expression.
• Cufflinks: A classic tool for assembling transcripts and estimating their abundance (often used as part of the "Tuxedo suite" with TopHat and Cuffdiff, though more modern tools are often preferred now).

4. Differential Expression Analysis:

These tools identify genes or transcripts that are significantly differentially expressed between different experimental conditions. Many are R Bioconductor packages.

• DESeq2: A very popular R package for differential gene expression analysis based on a negative binomial distribution.
• edgeR: Another widely used R package for differential expression analysis, also based on the negative binomial model.
• Limma-Voom: An R package that uses linear models and a "voom" transformation to handle RNA-Seq count data for differential expression.
• DEXSeq: Specifically designed for differential exon usage analysis.
• Swish: (often used with Salmon/Kallisto) for transcript-level differential expression.

5. Alternative Splicing Analysis:

• rMATS: Detects and quantifies various types of alternative splicing events.
• SpliceTrap: Identifies alternative splicing events.

6. Transcriptome Assembly (De Novo and Genome-Guided):

Used when a reference genome is unavailable or to discover novel transcripts.

• Trinity: A widely used de novo transcriptome assembler.
• Oases: Another de novo assembler, often used in conjunction with Velvet.
• SOAPdenovo-Trans: A de novo transcriptome assembler.
• StringTie/StringTie2: Can also perform genome-guided transcriptome assembly.

7. Functional Annotation and Pathway Analysis:

Once differentially expressed genes are identified, these tools help in understanding their biological context.

• GOseq: Performs Gene Ontology (GO) enrichment analysis, accounting for gene length bias.
• DAVID: A comprehensive functional annotation tool for genes and proteins.
• GSEA (Gene Set Enrichment Analysis): Determines whether a defined set of genes shows statistically significant differences in expression between two biological¹ states.
• KEGG pathway analysis: Tools that link genes to pathways in the KEGG database (e.g., enricher from clusterProfiler in R).
• Ingenuity Pathway Analysis (IPA): A commercial tool for pathway and network analysis.

8. Single-Cell RNA-Seq (scRNA-Seq) Specific Tools:

The unique characteristics of single-cell data (e.g., sparsity, high dropout rate) necessitate specialized tools.

• Seurat: A popular R package for quality control, analysis, and visualization of scRNA-Seq data.
• Scanpy: A Python-based ecosystem for single-cell data analysis.
• CellRanger: 10x Genomics' pipeline for processing and analyzing scRNA-Seq data generated from their platforms.
• STARsolo: A module within STAR optimized for single-cell RNA-seq alignment and counting.
• alevin-fry: (often used with Salmon) for single-cell transcript quantification.

9. Visualization Tools:

• Integrated Genome Viewer (IGV): For visualizing aligned reads and genomic features.
• R packages (e.g., ggplot2, ComplexHeatmap, pheatmap): For creating various plots like heatmaps, PCA plots, volcano plots, etc.
• Python libraries (e.g., matplotlib, seaborn): Similar to R packages for data visualization.

This list is not exhaustive, as the field of RNA-Seq bioinformatics is constantly evolving with new tools being developed. The choice of tools often depends on the specific research question, the type of RNA-Seq data (bulk vs. single-cell, stranded vs. unstranded, etc.), and the computational resources available.