Single-Cell Digital Lysates Generated by Phase-Switch Microfluidic Device Reveal Transcriptome Perturbation of Cell Cycle.

With conventional gene expression profiling, information concerning cellular heterogeneity is often lost in the physical mixing and averaging of millions of cells. Single-cell transcriptome analysis has the potential to address these issues. However, there is a need to determine how many cells are needed to draw meaningful conclusions in each single-cell study. Here, we introduce the concept of "digital lysate" for assessing cellular heterogeneity with a phase-switch microfluidic platform and apply it to construct a molecular map of transcriptome perturbation during the cell cycle. Using a phase-switch droplet microfluidic platform and next-generation sequencing, we obtained transcriptomes of single cells by random sampling. Digital lysates were generated by permutating and averaging multiple single-cell transcriptomes. In our studied cell populations, digital lysates converged to physical lysates ( r = 0.93), and the sample-to-sample repeatability was comparable to that of conventional analysis of a physical lysate ( r = 0.98). After determining the number of cells needed, single-cell transcriptomes were used to organize cells into a map by molecular similarity, and the map was validated by cell cycle-specific markers ( p = 0.003). Cell cycle regulatory genes were inferred using this molecular map and verified with siRNA assays. The study described here provides an effective approach, the generation and analysis of digital lysates, to investigate cellular heterogeneity.

An automated microfluidic device for assessment of mammalian cell genetic stability.

Single-cell transcriptome contains reliable gene regulatory relationships because gene-gene interactions only happen within a mammalian cell. While the study of gene-gene interactions enables us to understand the molecular mechanism of cellular events and evaluate molecular characteristics of a mammalian cell population, its complexity requires an analysis of a large number of single-cells at various stages. However, many existing microfluidic platforms cannot process single-cells effectively for routine molecular analysis. To address these challenges, we develop an integrated system with individual controller for effective single-cell transcriptome analysis. In this paper, we report an integrated microfluidic approach to rapidly measure gene expression in individual cells for genetic stability assessment of a cell population. Inside this integrated microfluidic device, the cells are individually manipulated and isolated in an array using micro sieve structures, then transferred into different nanoliter reaction chambers for parallel processing of single-cell transcriptome analysis. This device enables us to manipulate individual single-cells into nanoliter reactor with high recovery rate. We have performed gene expression analysis for a large number of HeLa cells and 293T cells expanded from a single-cell. Our data shows that even the house-keeping genes are expressed at heterogeneous levels within a clone of cells. The heterogeneity of actin expression reflects the genetic stability, and the expression distribution is different between cancer cells (HeLa) and immortalized 293T cells. The result demonstrates that this platform has the potential for assessment of genetic stability in cancer diagnosis.

Highly parallel genome-wide expression analysis of single mammalian cells.

BACKGROUND: We have developed a high-throughput amplification method for generating robust gene expression profiles using single cell or low RNA inputs. METHODOLOGY/PRINCIPAL FINDINGS: The method uses tagged priming and template-switching, resulting in the incorporation of universal PCR priming sites at both ends of the synthesized cDNA for global PCR amplification. Coupled with a whole-genome gene expression microarray platform, we routinely obtain expression correlation values of R(2)~0.76-0.80 between individual cells and R(2)~0.69 between 50 pg total RNA replicates. Expression profiles generated from single cells or 50 pg total RNA correlate well with that generated with higher input (1 ng total RNA) (R(2)~0.80). Also, the assay is sufficiently sensitive to detect, in a single cell, approximately 63% of the number of genes detected with 1 ng input, with approximately 97% of the genes detected in the single-cell input also detected in the higher input. CONCLUSIONS/SIGNIFICANCE: In summary, our method facilitates whole-genome gene expression profiling in contexts where starting material is extremely limiting, particularly in areas such as the study of progenitor cells in early development and tumor stem cell biology.