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This Article Full Text Full Text (PDF) Supplemental Data All Versions of this Article: clinchem.2009.131433v1 55/12/2162    most recent Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Ståhlberg, A. Articles by Semb, H. PubMed PubMed Citation Articles by Ståhlberg, A. Articles by Semb, H.

Quantitative Transcription Factor Analysis of Undifferentiated Single Human Embryonic Stem Cells

¹ Lundberg Laboratory for Cancer, Department of Pathology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden;² TATAA Biocenter, Gothenburg, Sweden;³ Department of Clinical Sciences, Lund University, Clinical Research Centre, UMAS, Malmö, Sweden;⁴ Department of Bioengineering, Imperial College London, London, UK;⁵ Department of Ophthalmology and Program in Neurobiology, Children’s Hospital Boston, Harvard Medical School, Boston, MA;⁶ Stem Cell Center, Lund University, Lund, Sweden.

^aAddress correspondence to: A.S. at Lundberg Laboratory for Cancer, Department of Pathology, Sahlgrenska Academy at University of Gothenburg, Gula Straket 8, SE- 413 45 Gothenburg, Sweden. Fax +46-31828733; e-mail anders.stahlberg{at}tataa.com. H.S. at Stem Cell Center, Lund University, BMC B10, SE-221 84 Lund, Sweden. Fax +46-462223600; e-mail henrik.semb{at}med.lu.se.

Background: Human embryonic stem cells (hESCs) require expression of transcription factor genes POU5F1 (POU class 5 homeobox 1), NANOG (Nanog homeobox), and SOX2 [SRY (sex determining region Y)-box 2] to maintain their capacity for self-renewal and pluripotency. Because of the heterogeneous nature of cell populations, it is desirable to study the gene regulation in single cells. Large and potentially important fluctuations in a few cells cannot be detected at the population scale with microarrays or sequencing technologies. We used single-cell gene expression profiling to study cell heterogeneity in hESCs.

Methods: We collected 47 single hESCs from cell line SA121 manually by glass capillaries and 57 single hESCs from cell line HUES3 by flow cytometry. Single hESCs were lysed and reverse-transcribed. Reverse-transcription quantitative real-time PCR was then used to measure the expression POU5F1, NANOG, SOX2, and the inhibitor of DNA binding genes ID1, ID2, and ID3. A quantitative noise model was used to remove measurement noise when pairwise correlations were estimated.

Results: The numbers of transcripts per cell varied >100-fold between cells and showed lognormal features. POU5F1 expression positively correlated with ID1 and ID3 expression (P < 0.05) but not with NANOG or SOX2 expression. When we accounted for measurement noise, SOX2 expression was also correlated with ID1, ID2, and NANOG expression (P < 0.05).

Conclusions: We demonstrate an accurate method for transcription profiling of individual hESCs. Cell-to-cell variability is large and is at least partly nonrandom because we observed correlations between core transcription factors. High fluctuations in gene expression may explain why individual cells in a seemingly undifferentiated cell population have different susceptibilities for inductive cues.