Dataset Information

Single cell transcriptomic analysis of prostate cancer cells.

ABSTRACT:

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.

SUBMITTER: Welty CJ

PROVIDER: S-EPMC3599075 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Publications

Single cell transcriptomic analysis of prostate cancer cells.

Welty Christopher J CJ Coleman Ilsa I Coleman Roger R Lakely Bryce B Xia Jing J Chen Shu S Gulati Roman R Larson Sandy R SR Lange Paul H PH Montgomery Bruce B Nelson Peter S PS Vessella Robert L RL Morrissey Colm C

BMC molecular biology 20130216

<h4>Background</h4>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 vi ...[more]

PMID: 23414343

Similar Datasets

Project description:The ability to interrogate circulating tumor cells (CTC) and disseminated tumor cells (DTC) is restricted by the small number detected and isolated (typically <10). We wanted 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, we analyzed 10 individual DTC isolated from each of 2 patients with metastatic PCa. We have shown that a transcriptomic profile can be 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, but this method can be used to obtain a transcriptomic profile from DTC isolated from the bone marrow of patients with PCa. Custom Agilent 44K whole human genome expression oligonucleotide microarrays were used to profile clonally selected and cultured single passage of cell cycle synchronized C4-2B PCa cells isolated using a micromanipulator under direct visualization with an inverted microscope into ten sets of single, 5-, or 10-cells. Single disseminated tumor cells were isolated from bone marrow (BM) samples of two advanced prostate cancer patients. Essentially, a two-step selection process was employed, in which anti-CD45 and anti-CD61 conjugated to immunomagnetic beads were used for negative selection, and anti-HEA was used for positive selection. Cells were then fluorescently stained for BerEP4, counter stained with RPE anti-CD45, and individually selected (10 single cells each per patient) under fluorescent light using a micropipette system for further analysis. RNA was amplified using the WT-Oviation one-direct system and hybridization against a common reference pool of prostate tumor cell lines. Data from C42B cell data and data from single cells isolated from the bone marrow of patients were normalized and analyzed separately.

Project description:Accurate risk classification of men with localized high-risk prostate cancer directly affects treatment management decisions and patient outcomes. A wide range of risk assessments and classifications are available. However, each one has significant limitations to distinguish between indolent and aggressive prostate cancers. Circulating tumor cells (CTCs) may provide an alternate additional source, beyond tissue biopsies, to enable individual patient-specific clinical assessment, simply because CTCs can reveal both tumor-derived and germline-specific genetic information more precisely than that gained from a single diagnostic biopsy. In this study, we combined a filtration-based CTC isolation technology with prostate cancer CTC immunophenotyping to identify prostate cancer CTCs. Next, we performed 3-D telomere profiling prior to laser microdissection and single-cell whole-exome sequencing (WES) of 21 CTCs and 4 lymphocytes derived from 10 localized high-risk prostate cancer patient samples. Localized high-risk prostate cancer patient CTCs present a high number of telomere signals with lower signal intensities (short telomeres). To capture the genetic diversity/heterogeneity of high-risk prostate cancer CTCs, we carried out whole-exome sequencing. We identified 202,241 single nucleotide variants (SNVs) and 137,407 insertion-deletions (indels), where less than 10% of these genetic variations were within coding regions. The genetic variation (SNVs + indels) and copy number alteration (CNAs) profiles were highly heterogeneous and intra-patient CTC variation was observed. The pathway enrichment analysis showed the presence of genetic variation in nine telomere maintenance pathways (patients 3, 5, 6, and 7), including an important gene for telomere maintenance called telomeric repeat-binding factor 2 (TRF2). Using the PharmGKB database, we identified nine genetic variations associated with response to docetaxel. A total of 48 SNVs can affect drug response for 24 known cancer drugs. Gene Set Enrichment Analysis (GSEA) (patients 1, 3, 6, and 8) identified the presence of CNAs in 11 different pathways, including the DNA damage repair (DDR) pathway. In conclusion, single-cell approaches (WES and 3-D telomere profiling) showed to be useful in unmasking CTC heterogeneity. DDR pathway mutations have been well-established as a target pathway for cancer therapy. However, the frequent CNA amplifications found in localized high-risk patients may play critical roles in the therapeutic resistance in prostate cancer.

Dataset Information

Single cell transcriptomic analysis of prostate cancer cells.

Background

Results

Conclusions

Publications

Single cell transcriptomic analysis of prostate cancer cells.

Similar Datasets

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