Single Cell Sequencing vs Bulk Sequencing

Why Single Cell?

Over the past decade, numerous advances in biotechnology have translated to improvements in the medical arena. Yet, limitations in analytical tools still remain and present significant roadblocks to the study of pathologies and the development of life-changing therapies.

Today, many genotyping workflows rely on bulk assays, which mix DNA across cells and report average readouts. Although appropriate for some questions, these assays do not preserve the information of individual cells, and therefore provide only limited knowledge about biological complexity. Single-cell DNA sequencing, on the other hand, evaluates the genotype of individual cells, and thus provides a richer picture of the sample.

This is especially pertinent for applications where information at the cellular level matters. Some of the most pressing challenges include elucidating the complexity of heterogeneous diseases like cancer, and the underlying mechanisms driving them. Other hurdles lie within the process of pharmacological development, in which each therapeutic candidate must be characterized with sufficient granularity to ensure safety and efficacy.

Bulk Sequencing vs Single-cell DNA Sequencing

In conventional bulk sequencing (left), DNA is mixed across cells such that genetically distinct subpopulations (e.g. subclones) cannot be identified. Genotypic measurements (e.g. variant allele frequencies, VAF) therefore represent a population average. In single-cell DNA sequencing (right), genetic targets are barcoded in each cell. This allows for genotypic readouts for different subpopulations of cells.

Why Multi-omics?

Conventional workflows in disease research and therapeutic development involve running multiple separate assays, often on different instruments, to get answers. This approach has several drawbacks.

First, precious samples that are difficult to obtain must be used in multiple assays — leaving little for downstream analyses. Second, the data integration and analysis of multi-assay studies is complex and typically requires bioinformatics expertise to glean correlations across different datasets. Third, conducting multiple assays on different “omics” platforms can get expensive, and will often require longer timelines before researchers get answers to their experimental questions.

Fortunately, innovative multi-omics technologies like the Tapestri Platform are offering a solution to these issues. Tapestri simultaneously measures DNA and cell-surface proteins across thousands of individual cells.

By collapsing genotypic and phenotypic assays into a single workflow, Tapestri both preserves precious samples and enables multi-parameter data to be gathered quickly and easily. This powerful approach is offering an unprecedented look into the nuances of biology and transforming how we explore disease and develop therapies.

Illustration of ex vivo and in vivo cell and gene therapies.

Different technologies enable varying levels of resolution in the fields of oncology and cell and gene therapies. Whereas bulk sequencing portrays only population-level metrics of cells, single-cell sequencing and multi-omics oncover cell-to-cell variation in a variety of aspects relevant to cancer and advanced therapeutics.

Single-cell Applications

Single-cell analysis is advancing several scientific areas, including oncology and the development of advanced therapeutics. For instance, Tapestri is helping advance cancer translational research, where information about tumor architecture can be used to inform diagnosis, prognosis, and treatment strategies in a highly personalized fashion.

Tapestri is also helping advance the development of novel cell and gene therapies. By measuring multiple genotypic alterations across cells, therapy developers achieve deeper characterization of gene-modified cell therapies and assess in vivo gene therapies in model systems.

The benefits of single-cell analysis extend to academia and industries alike. Pharma and biopharma can capitalize on the highly sensitive analysis of Tapestri to assess therapy resistance, MRD, and characterize cell-based drug candidates.

Oncology

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Cell and Gene Therapy

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Why Single Cell?

Why Multi-omics?

Single-cell Applications

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