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Aug 1, 2010 (Vol. 30, No. 14)

Gene-Expression Profiling Goes Solo

Single-Cell Technique Embraced for What It Can Reveal about Molecular Mechanisms

  • RT-qPCR

    Researchers at the Lundberg Lab for Cancer Research at Gothenburg University reported that they have optimized the steps in RT-qPCR for more accurate single-cell gene-expression profiling. Anders Stahlberg, Ph.D., investigator, said that it’s important to understand single-cell biology and due to the small number of transcripts being measured, it’s often difficult to separate technical variation from biologically relevant cell-to-cell variation. His group combined various existing methods and focused on enhancing steps in the process.

    “RT-qPCR has high sensitivity and reproducibility but hasn’t become common practice for single-cell gene-expression profiling. We used the best of all the steps and optimized them,” Dr. Stahlberg said. Cell collection was done via glass capillaries with a wide tip (~10 µm) to allow passage of intact cells, ensuring collection of all mRNA.

    A control for contamination of the buffer surrounding the cells was analyzed with the single-cell samples. Cells must be lysed and all available mRNA accessible to the enzyme. As a control for the workflow, Dr. Stahlberg said, an RNA spike can be added to the lysis buffer, and it should be unique compared to the transcriptome of the single cells being analyzed.

    For the reverse transcription step, it’s important to use an optimized gene-specific primer identical to the reverse primer, as well as to test different reverse-transcriptases because efficiency varies greatly. Since the number of genes analyzed from a single cell is limited by the number of transcripts, Dr. Stahlberg said his research shows that more than 20 target molecules are needed for accurate quantification.

    Data analysis can be difficult due to the variation in mRNA levels among cells. “You need to normalize to compensate for variation between samples.” This is to distinguish between experimental and biological variability. “People working from more cell population measurements think you should normalize single-cell data against a reference gene, but this is wrong. It’s important to know the principals of single-cell gene-expression profiling and how to standardize it and how to look at the data.”

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