When Yue Yun began her doctoral studies in computational genetics, sequencing even a handful of base pairs with confidence felt like a triumph. Less than two decades later, it is standard to read entire genomes in hours using next-generation sequencing (NGS). For Yun, now Senior Director of NGS R&D at Takara Bio USA, the real hurdle now is resolution, particularly for applications like biomarker discovery.
“The trend in NGS was towards bulk analysis for many years,” Yun reflects. “The immediate challenge is that researchers want to move forward by revealing more details from smaller quantities of source data.”
This is the impetus behind innovations in single-cell sequencing. The biological variations that matter most for applications like biomarker discovery—rare mutations, subtle splicing events, the position of one cell in relation to another—get lost in the noise of bulk sequencing. Labs need tools that look deeper, not just bigger.
From bulk to the single cell
Bulk sequencing enabled dramatic progress, but it can mask the diversity hidden within tissues. Tumor cells, for example, can display distinctive expression profiles that get drowned out within larger pools of healthy cells.
“The fundamental question is how to identify a rare signal in a large population,” says Yun. Single-cell sequencing addresses the limitations of bulk sequencing by allowing researchers to capture each cell’s unique biology.
Takara Bio was among the first to commercialize single-cell whole genome amplification and full-length transcriptomics solutions, giving researchers tools to uncover biological features at this unprecedented resolution. The first generation of single-cell methods, though, forced a choice: sequencing depth on a few hundred cells, or throughput across many. Resolution alone was not enough—the underlying amplification and reverse-transcription chemistry still introduced its own distortions.
Breaking through bias in RNA workflows
In RNA sequencing, library preparation bias has formed one of the biggest hurdles. Even with the increased resolution of single-cell RNA sequencing (scRNA‐seq), which can reveal the nuances of expression within and between individual cells, many of the same library preparation challenges remain. Conventional methods often overrepresent some regions of transcripts while missing others, obscuring isoforms and gene fusion events that may drive disease.
Biases introduced during the reverse transcription stage of library preparation, like those introduced by the choice of reverse transcriptase enzyme or secondary structures that impact binding, make it harder to map gene fusion or alternative splicing events.1
Researchers now appreciate that these features are a significant contributor to overall variation in the transcriptome. “More and more people recognize that transcriptional regulation is not only about expressed and non-expressed or high- or low-expression genes,” says Yun. “It's really about the isoform and alternative-splicing side, and sometimes gene fusion, too.”
Scaling up without losing depth
The introduction of full-length total RNA sequencing solutions for single cells helped bridge these gaps, providing a more complete view with enhanced depth. Low throughput, however, limited sequencing runs to just a few hundred cells.2 New high-throughput technology breaks through this barrier and opens up scRNA-seq for researchers conducting in-depth biomarker discovery assays.
The first of its kind, Takara Bio’s Shasta® Single Cell System scales full gene-body coverage up to 100,000 cells per run. Compatibility with any cell size—capturing everything from small nuclei to large cardiomyocytes—opens the door to studying a far broader spectrum of biology.
“I think very soon, we are going to reach a pivotal moment in single-cell NGS,” says Yun. “The improvements are beginning in biomarker discovery, but soon we’ll start seeing similar enhancements in screening as well.”
Deeper DNA coverage can enhance tumor analysis
Similar challenges have emerged in single-cell DNA analysis. Whole-genome amplification (WGA) enables the sequencing of an entire genome when starting material is scarce, as with single-cell sequencing. This approach can reveal key biomarkers, such as copy number variations (CNVs), that bulk or targeted methods might miss.3
“Mutations like CNVs might only be present in 20 or 30 cells out of a 1,500 cell sample,” says Yun. “That’s where the high resolution of single-cell sequencing is invaluable.”
Similar to RNA-seq, sequencing coverage and throughput have a large impact on the utility of single-cell WGA sequencing for biomarker discovery. Uneven coverage due to amplification bias and small sample sizes increases the likelihood of missing rare events. To overcome this, Takara Bio designed their Shasta Whole-Genome Amplification Kit to provide even coverage at higher throughputs, analyzing up to 1,500 cells per run.
Building pipelines for single-cell data
As single-cell technologies scale, they generate larger and more complex datasets. The unique data produced by single-cell analysis require specially designed pipelines. Yun, who began her career as a genomic bioinformatician, describes their evolution.
“We might start by borrowing a pipeline or analysis tool from bulk sequencing, but then you have to design a lot of new things on top of it to make it fit single-cell data,” she explains. That includes accounting for the higher sensitivity of single-cell reads and applying new statistical approaches to much smaller cell populations.
The key, Yun says, is iteration between wet-lab and bioinformatics teams. Each informs the other, and progress must be anchored with known data benchmarks to establish ground truth. These benchmarks are essential to evaluating the performance of different approaches and quality control efforts.
ISTOCK, ROMASET
Restoring context through spatial ‘omics
Even the richest datasets from traditional single-cell approaches strip away something vital: context. Dissociating cells from tissue for single-cell sequencing erases information about where those cells sit in the tissue.4 “That dissociation can cause cell loss, bias the mix of cell types, and most importantly, remove their spatial context,” says Christina Fan, Co-Founder of Curio Biosciences, which was acquired by Takara Bio.
This layer of data is critical to understanding solid tumor heterogeneity and how tumor cells are positioned relative to immune cells, fibroblasts, or blood vessels. The spatial relationship often determines whether a cancer spreads or how it responds to therapy. “Spatial genomics enables researchers to conduct analyses such as ligand-receptor analysis and cell-cell interaction and communication,” Fan adds.
Established spatial technologies are complex, limiting their accessibility. Microscopy-based techniques provide high resolution but rely on costly instruments and complicated algorithms to identify individual cells. Sequencing-based methods that use capture arrays to retain spatial information can scale more easily but similarly rely on complex algorithms to infer which signals come from which cells.
Amidst rising demand for spatial context, the latest innovations in the field are geared toward simpler, integrated techniques that add spatial information without disrupting existing single-cell workflows. Takara Bio developed a spatial mapping kit that accomplishes exactly that upstream of dissociation, integrating with them in a one-hour step.
The Trekker® Single-Cell Spatial Mapping Kits tag each cell with a position-specific barcode prior to dissociation, which provides “direct measurement of which cell contributes which signal, without relying on algorithms to guess or separate mixed signals from multiple cells,” explains Fan. This approach maintains the sensitivity of single-cell sequencing while directly tying signals to the cells that produce them, enabling more confident insights into the tumor microenvironment and beyond.
Toward a multiomic future
The sequencing frontier has shifted from bulk sequencing speed and throughput to enhancing and scaling single-cell analysis with richer data: capturing isoforms, preserving rare variant signals, and adding spatial context.
“We’ve made huge advancements in single-cell analysis,” says Yun. “Now the challenge is to apply these techniques to enduring problems in biomarker discovery, and that’s just not trivial at all. This is the front end of our research right now, and we’re excited to get to work.”
For Yun and Fan, these advances represent more than incremental improvements. They are the foundation for a new era of single-cell multiomics and spatial multiomics, where genomic, transcriptomic, and spatial layers of information converge to illuminate complex tissue biology. Yun reports her team is currently working towards these broader goals, including adding epigenomic CUT&Tag data to Shasta technology. This multiomics analysis is still in its infancy, but to Yun, represents a clear direction of travel: “The world is moving to a single-cell, multi-modal omics future.”
Takeaway for Lab Leaders
These approaches point to three shifts in laboratory practice:
- Throughput and sensitivity no longer require compromise. The Shasta® system enables large-scale, high-resolution RNA and DNA analysis.
- Spatial information can be integrated directly into existing workflows. Trekker® technology provides spatial context without heavy infrastructure.
- Multiomics is becoming practical. These advances set new benchmarks for biomarker discovery, positioning labs to move beyond single datasets and toward integrated, higher-confidence insights.
Takara Bio’s continuing innovations are reshaping single-cell sequencing from a specialized tool into a foundational platform for the next decade of discovery.
References
1. Finotello F, Lavezzo E, Bianco L, et al. Reducing bias in RNA sequencing data: a novel approach to compute counts. BMC Bioinformatics. 2014;15(1):S7. doi:10.1186/1471-2105-15-S1-S7
2. Hayashi T, Ozaki H, Sasagawa Y, Umeda M, Danno H, Nikaido I. Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs. Nat Commun. 2018;9:619. doi:10.1038/s41467-018-02866-0
3. Smolander J, Khan S, Singaravelu K, et al. Evaluation of tools for identifying large copy number variations from ultra-low-coverage whole-genome sequencing data. BMC Genomics. 2021;22(1):357. doi:10.1186/s12864-021-07686-z
4. Jia Q, Chu H, Jin Z, Long H, Zhu B. High-throughput single-сell sequencing in cancer research. Sig Transduct Target Ther. 2022;7(1):145. doi:10.1038/s41392-022-00990-4
