Single cell transcriptomic analysis of prostate cancer cells

BMC Molecular Biology

Colm Morrissey³  Robert L Vessella¹  Peter S Nelson⁴  Bruce Montgomery⁴  Paul H Lange¹  Sandy R Larson⁵  Roman Gulati²  Shu Chen²  Jing Xia²  Bryce Lakely⁵  Roger Coleman²  Ilsa Coleman²  Christopher J Welty⁵ 

[1] Department of Veterans Affairs Medical Center, Seattle, WA, USA;Fred Hutchinson Cancer Research Center, Seattle, WA, USA;Genitourinary Cancer Research Laboratory, Department of Urology, University of Washington, Box 356510, Seattle, WA, 98195, USA;Department of Medicine, University of Washington, Seattle, WA, USA;Department of Urology, University of Washington, Seattle, WA, USA

关键词: Disseminated tumor cells;    Transcriptome;    Single-cell;    Prostate cancer;   

Others  :  1091189
DOI  :  10.1186/1471-2199-14-6

 received in 2012-09-07, accepted in 2013-02-11,  发布年份 2013

【 摘 要 】

Background

The ability to interrogate circulating tumor cells (CTC) and disseminated tumor cells (DTC) is restricted by the small number detected and isolated (typically <10). To determine if a commercially available technology could provide a transcriptomic profile of a single prostate cancer (PCa) cell, we clonally selected and cultured a single passage of cell cycle synchronized C4-2B PCa cells. Ten sets of single, 5-, or 10-cells were isolated using a micromanipulator under direct visualization with an inverted microscope. Additionally, two groups of 10 individual DTC, each isolated from bone marrow of 2 patients with metastatic PCa were obtained. RNA was amplified using the WT-Ovation™ One-Direct Amplification System. The amplified material was hybridized on a 44K Whole Human Gene Expression Microarray. A high stringency threshold, a mean Alexa Fluor® 3 signal intensity above 300, was used for gene detection. Relative expression levels were validated for select genes using real-time PCR (RT-qPCR).

Results

Using this approach, 22,410, 20,423, and 17,009 probes were positive on the arrays from 10-cell pools, 5-cell pools, and single-cells, respectively. The sensitivity and specificity of gene detection on the single-cell analyses were 0.739 and 0.972 respectively when compared to 10-cell pools, and 0.814 and 0.979 respectively when compared to 5-cell pools, demonstrating a low false positive rate. Among 10,000 randomly selected pairs of genes, the Pearson correlation coefficient was 0.875 between the single-cell and 5-cell pools and 0.783 between the single-cell and 10-cell pools. As expected, abundant transcripts in the 5- and 10-cell samples were detected by RT-qPCR in the single-cell isolates, while lower abundance messages were not. Using the same stringency, 16,039 probes were positive on the patient single-cell arrays. Cluster analysis showed that all 10 DTC grouped together within each patient.

Conclusions

A transcriptomic profile can be reliably obtained from a single cell using commercially available technology. As expected, fewer amplified genes are detected from a single-cell sample than from pooled-cell samples, however this method can be used to reliably obtain a transcriptomic profile from DTC isolated from the bone marrow of patients with PCa.

【 授权许可】

   
2013 Welty et al; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Holcomb IN, Grove DI, Kinnunen M, Friedman CL, Gallaher IS, Morgan TM, Sather CL, Delrow JJ, Nelson PS, Lange PH, Ellis WJ, True LD, Young JM, Hsu L, Trask BJ, Vessella RL: Genomic alterations indicate tumor origin and varied metastatic potential of disseminated cells from prostate cancer patients. Cancer Res 2008, 68:5599-5608.
• [2]Morgan TM, Lange PH, Porter MP, Lin DW, Ellis WJ, Gallaher IS, Vessella RL: Disseminated tumor cells in prostate cancer patients after radical prostatectomy and without evidence of disease predicts biochemical recurrence. Clin Cancer Res 2009, 15:677-683.
• [3]Oberneder R, Riesenberg R, Kriegmair M, Bitzer U, Klammert R, Schneede P, Hofstetter A, Riethmuller G, Pantel K: Immunocytochemical detection and phenotypic characterization of micrometastatic tumour cells in bone marrow of patients with prostate cancer. Urol Res 1994, 22:3-8.
• [4]Riethdorf S, Wikman H, Pantel K: Review: Biological relevance of disseminated tumor cells in cancer patients. Int J Cancer 2008, 123:1991-2006.
• [5]Pfitzenmaier J, Ellis WJ, Arfman EW, Hawley S, McLaughlin PO, Lange PH, Vessella RL: Telomerase activity in disseminated prostate cancer cells. BJU Int 2006, 97:1309-1313.
• [6]Shiozawa Y, Pedersen EA, Patel LR, Ziegler AM, Havens AM, Jung Y, Wang J, Zalucha S, Loberg RD, Pienta KJ, Taichman RS: GAS6/AXL axis regulates prostate cancer invasion, proliferation, and survival in the bone marrow niche. Neoplasia 2010, 12:116-127.
• [7]Wikman H, Vessella R, Pantel K: Cancer micrometastasis and tumour dormancy. APMIS 2008, 116:754-770.
• [8]Hartmann CH, Klein CA: Gene expression profiling of single cells on large-scale oligonucleotide arrays. Nucleic Acids Res 2006, 34:e143.
• [9]Klein CA, Seidl S, Petat-Dutter K, Offner S, Geigl JB, Schmidt-Kittler O, Wendler N, Passlick B, Huber RM, Schlimok G, Baeuerle PA, Riethmuller G: Combined transcriptome and genome analysis of single micrometastatic cells. Nat Biotechnol 2002, 20:387-392.
• [10]Kurimoto K, Yabuta Y, Ohinata Y, Ono Y, Uno KD, Yamada RG, Ueda HR, Saitou M: An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis. Nucleic Acids Res 2006, 34:e42.
• [11]Brady G, Barbara M, Iscove NM: Representative in vitro cDNA amplification from individual hemopoietic cells and colonies. Meth Mol Cell Biol 1990, 2:17-25.
• [12]Ellis WJ, Pfitzenmaier J, Colli J, Arfman E, Lange PH, Vessella RL: Detection and isolation of prostate cancer cells from peripheral blood and bone marrow. Urology 2003, 61:277-281.
• [13]Dixon WJ: Analysis of extreme values. Ann Math Stat 1950, 21:488-506.
• [14]Smyth GK: Limma: linear models for microarray data. In Bioinformatics and Computational Biology Solutions using R and Bioconductor. Edited by Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W. Springer, New York; 2005:397-420.
• [15]Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005, 102:15545-15550.
• [16]Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E, Houstis N, Daly MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B, Lander ES, Hirschhorn JN, Altshuler D, Groop LC: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 2003, 34:267-273.
• [17]Islam S, Kjallquist U, Moliner A, Zajac P, Fan JB, Lonnerberg P, Linnarsson S: Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res 2011, 21:1160-1167.
• [18]Cann GM, Gulzar ZG, Cooper S, Li R, Luo S, Tat M, Stuart S, Schroth G, Srinivas S, Ronaghi M, Brooks JD, Talasaz AH: mRNA-Seq of single prostate cancer circulating tumor cells reveals recapitulation of gene expression and pathways found in prostate cancer. PLoS One 2012, 7:e49144.
• [19]van de Stolpe A, Pantel K, Sleijfer S, Terstappen LW, den Toonder JM: Circulating tumor cell isolation and diagnostics: toward routine clinical use. Cancer Res 2011, 71:5955-5960.
• [20]Economos C, Morrissey C, Vessella RL: Circulating tumor cells as a marker of response: implications for determining treatment efficacy and evaluating new agents. Curr Opin Urol 2012, 22:190-196.
• [21]Melin J, Quake SR: Microfluidic large-scale integration: the evolution of design rules for biological automation. Annu Rev Biophys Biomol Struct 2007, 36:213-231.