Single Cell Trascriptome Analysis

Single-cell transcriptomics examines the gene expression level of individual cells in each population by simultaneously measuring the messenger RNA (mRNA) concentration of hundreds to thousands of genes. The unraveling of heterogenous cell populations, reconstruction of cellular developmental trajectories, and modeling of transcriptional dynamics are made possible through the analysis of this transcriptomic data.

It is becoming increasingly apparent that a single-cell resolution, rather than bulk-level datasets, will be necessary to understand the structure and function of the brain. At a compositional level, single-cell transcriptomics has revealed that there are many populations of rare cell types in the brain. At an operational level, neuronal activity can frequently be heterogeneous and cell-type-specific, and thus techniques that lack single-cell resolution potentially obfuscate these heterogeneous components. Finally, cellular activity can commonly be sparsely distributed across cells, and bulk approaches may lack the sensitivity to identify such sparse activity. For all these reasons, single-cell methodologies have immense opportunities to interrogate the brain at a foundational operating level.

Current Methods for Single-cell Transcriptome Study

• Full-length RNA sequencing methods

Full-length single-cell RNA sequencing (scRNA-seq) allows entire transcriptome sequencing with possible quantification and detection of gene expression, transcript isoforms, single nucleotide polymorphisms (SNPs), and mutations.

• Tag-based RNA sequencing methods

Tag-based RNA sequencing methods are mainly used to estimate transcript abundances at the cost of information of splice isoforms by sequencing either the 5’-end or the 3’-end of the transcripts. To pool all the samples in a single tube for sequencing library preparation, the ends are usually equipped with cell labels for distinguishing different cells or molecular labels for accurately evaluating transcript copies. Barcode labels come in three forms, depending on the method of addition, including liquid barcoding, bead-based barcoding, and split-pool barcoding.

Fig.1 Tag-based RNA sequencing methods. (Song, 2019)

• Total RNA sequencing methods

Previous study has reported a novel single-cell whole-transcriptome method - single-cell universal poly (A) - independent RNA sequencing (SUPeR-seq), where random oligonucleotides (AnchorX-T15N6) instead of oligo (dT) primers were used for the first-strand cDNA synthesis. Then terminal deoxynucleotidyl transferase (TdT) and dATP (mixed with 1% ddATP) were introduced to add a poly (A) tail to the 3’ end, followed by the second-strand cDNA synthesis using a different primer (AnchorYT24). The entire cDNAs were further amplified by PCR, purified, and deep sequenced. This method detected more genes than traditional scRNA-seq due to co-measurement of different types of RNA, although only 20%-30% non-poly (A) RNAs were detected, indicating the need for higher sensitivity to non-poly (T) RNA.

Fig.2 Schematic diagram of a single cell transcriptomic experiment. (Kulkarni, 2019)

Application of Single Cell Transcriptomics in Neuroscience

• Single-cell transcriptomics has uncovered novel cell types and elucidated neuronal and glial diversity within various brain regions and across species. This technology has also enabled the molecular examination of “cell-states” as well as cell types. Novel methodologies to implement single-cell transcriptomics on both fixed and frozen postmortem human brain tissue using nuclei are providing insight into human brain evolution, development, and function.
• Using scRNA-seq to study neurological disorders has been a major technical advancement given the enormous diversity of cell types within the brain. A new methodology that identifies single-cell single-nucleotide polymorphism sequencing (SNP-seq) could reliably detect both mutant and normal cells in postmortem human brain tissue. This single-cell transcriptomic tool could be broadly applied to other X-linked neurodevelopmental disorders, such as Fragile-X syndrome or X-linked intellectual disability.
• Studies using scRNA-seq in control vs knockout studies in mice have analyzed changes in cellular composition, differentially expressed genes, and pseudo-time or pseudo-differentiation tools at cellular resolution with deletion or manipulation of a given gene. Given the immense cellular heterogeneity of any given brain region, single-cell transcriptomics allows for the identification of cell types particularly vulnerable in disease states and for addressing the question of why these cell types are particularly vulnerable. Another benefit of using single-cell transcriptomic within these comparative approaches is the ability to examine both cell-autonomous and non-cell-autonomous changes.

Fig.3 Example schematic of a comparative single-cell transcriptomic study. (Kulkarni, 2019)

Promising Implications for Advancing Neuroscience by Single-cell Transcriptomics

• Such techniques will provide a comprehensive identification of the cell-type “building blocks” of the brain, which allow such building blocks to be mapped across space, time, and conditions. Via marker gene expression, these building blocks can also be mapped onto previous research that used complementary phenotyping techniques.
• Provide functional predictions that can help guide, inform, and interpret experiments, both at the level of cell types themselves as well as genes expressed within cell types.
• Facilitate examination of predictions by illustrating specific ways to access cell types, as well as molecular features within these cell types.

Single-cell transcriptome analysis will eventually permit connections between gene expression networks, cell lineage, and phenotype of individual cells. Combined with live cell imaging, this is potentially a powerful tool for tracing cell lineage during development or cell differentiation, especially in conjunction with fluorescent protein reporters. Creative Biolabs is focused on the development of research tools for basic neuroscience. We can offer our customers a variety of different services to facilitate the application of single-cell transcriptomics in neuroscience research. If you need, please feel free to contact us.

References

1. Song, Y.L.; et al. Single cell transcriptomics: moving towards multi-omics. Analyst. 2019, 144: 3172-3189.
2. Kulkarni, A.; et al. Beyond bulk: a review of single cell transcriptomics methodologies and applications. Curr Opin Biotechnol. 2019 Aug;58: 129-136.