Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Summary

Presented here is a protocol to discover the overexpressed driver genes maintaining the established cancer stem-like cells derived from colorectal HT29 cells. RNAseq with available bioinformatics was performed to investigate and screen gene expression networks for elucidating a potential mechanism involved in the survival of targeted tumor cells.

Abstract

Cancer stem cells play a vital role against clinical therapies, contributing to tumor relapse. There are many oncogenes involved in tumorigenesis and the initiation of cancer stemness properties. Since gene expression in the formation of colorectal cancer-derived tumorspheres is unclear, it takes time to discover the mechanisms working on one gene at a time. This study demonstrates a method to quickly discover the driver genes involved in the survival of the colorectal cancer stem-like cells in vitro. Colorectal HT29 cancer cells that express the LGR5 when cultured as spheroids and accompany an increase CD133 stemness markers were selected and used in this study. The protocol presented is used to perform RNAseq with available bioinformatics to quickly uncover the overexpressed driver genes in the formation of colorectal HT29-derived stem-like tumorspheres. The methodology can quickly screen and discover potential driver genes in other disease models.

Introduction

Colorectal cancer (CRC) is a leading cause of death with high prevalence and mortality worldwide^1,2. Due to gene mutations and amplifications, cancer cells grow without proliferative control, which contributes to cell survival³, anti-apoptosis⁴, and cancer stemness^5,6,7. Within a tumor tissue, tumor heterogeneity allows tumor cells to adapt and survive during therapeutic treatments⁸. Cancer stem cells (CSCs), with a higher rate of self-renewal and pluripotency than differential cancer types, are principally responsible for tumor recurrence^9,10 and metastatic CRC¹¹. CSCs present more drug resistance^12,13,14 and anti-apoptosis properties^15,16, thus surviving tumor chemotherapies.

Here, in order to investigate the potential mechanism for stemness in the selected CRC stem cells, RNAseq was performed to screen differentially expressed genes in tumor spheroids. The cancer cells can form spheroids (also called tumorspheres) when grown in low adherence conditions and stimulated by growth factors added to the cultured medium, including EGF, bFGF, HGF, and IL6. Therefore, we selected CRC HT29 tumor cells that resist chemotherapies with an increase in phosphorylated STAT3 when treated with oxaliplatin and irinotecon¹⁷. In addition, HT29 expressed higher stemness markers when cultured in the described culture conditions. The HT29-derived CSC model expressed higher amounts of leucine-rich repeat-containing G-protein-coupled receptor 5 (LGR5)¹⁸, a specific marker of CRC stem cells^19,20. Moreover, CD133, considered a general biomarker for cancer stem cells, is also highly expressed in the HT29 cell line²¹. This protocol's purpose is to discover groups of driver genes in the established cancer stem-like tumorspheres based on bioinformatics datasets as opposed to investigating individual oncogenes²². It investigates potential molecular mechanisms through RNAseq analysis followed by available bioinformatics analyses.

Next generation sequencing is a high-throughput, easily available, and reliable DNA sequencing method based on computational help, used to comprehensively screen driver genes for guiding tumor therapies²³. The technology is also used for detecting gene expression from reverse transcription of an isolated RNA sample²⁴. However, when screening with RNAseq, the most important genes to target with therapy may not have the highest expression differential between experimental and control samples. Therefore, some bioinformatics were developed for classifying and identifying genes based on current datasets such as KEGG²⁵, GO^26,27, or PANTHER²⁸, including Ingenuity Pathway Analysis (IPA)²⁹ and NetworkAnalyst³⁰. This protocol shows the integration of RNAseq and NetworkAnalyst to quickly discover a group of genes in the selected HT29-derived spheroids compared to parental HT29 cells. Application of this method to other disease models is also suggested for discovering differences in important genes.

Compared to investigation of individual gene expression, a high-throughput technique provides advantages to find potential driver genes easily for tumor precision medicine. With useful datasets such as KEGG, GO, or PANTHER, specific genes can be identified based on the disease models, signaling pathways, or specific functions, and this allows quickly focusing on specific, important genes, saving time and research costs. A similar application is used in previous studies^14,18,31. Particularly, a tumor is more complicated because different types of tumors express distinguishing genes and pathways for survival and proliferation. Therefore, this protocol can pick up genes distinguishing different tumor types under different circumstances. There is the potential to find effective strategies against cancers by understanding the mechanism of specific gene expression.

Protocol

1. Cell culture and tumorsphere formation

1. Culture HT29 cells in a 10 cm dish containing Dulbecco’s modified eagle medium (DMEM) with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin antibiotic (P/S).
2. Grow the cells in an incubator at 37 °C with 5% CO₂ and 95% humidity under aseptic conditions, until they reach 80% confluency.
3. Trypsinize HT29 cells with 1 mL of 0.25% trypsin for 5 min at 37 °C and consequently neutralize the trypsin by adding 2 mL of DMEM with 10% FBS and 1% P/S.
4. Count the HT29 cells using a hemocytometer.
5. Add 2,000 cells/well to a low attached 6 well plates with 2 mL of serum-free DMEM with 1% P/S and supplemented with 0.2% B27, 20 ng/mL of epidermal growth factor (EGF), 20 ng/mL of fibroblast growth factor (bFGF), 20 ng/mL of hepatocyte growth factor (HGF), and 20 ng/mL of interleukin 6 (IL6).
6. Grow the cells at 37 °C with 5% CO₂ and 95% humidity under aseptic conditions.
7. Add 0.5 mL of cancer stem cell medium every 2 days until the tumorspheres measure >100 μm in diameter for at least 7 days.
8. Observe and measure the tumorsphere diameter using an inverted microscope with a digital cell imaging system.
9. When the tumorspheres reach >100 μm in diameter, trypsinize the tumorspheres with 0.25% trypsin for 5 min at 37 °C and neutralize the trypsin by adding 2x the volume of growth medium. Count the cells using a hemocytometer.
10. Centrifuge at 1,200 rpm for 10 min and remove the supernatant.
11. Incubate 5 x 10⁴ cells with 2 µL of anti-LGR5-PE and 2 µL of anti-CD133-PE in 100 µL of DMEM individually for 30 min at room temperature, shaking at 200 rpm.
    NOTE: CD133 is a general cancer stem cell biomarker that is highly expressed in HT29 cells.
12. Add 900 µL of PBS and analyze the LGR5 and CD133 expression using flow cytometry. A change in fluorescence in the FL2-H channel indicates gene expression.

2. RNA isolation

NOTE: Use a commercial kit (see Table of Materials) with a rapid column for RNA isolation following the manufacturer’s instructions.

1. Add 50 µL of PBS to the harvested cells (2 x 10⁵ cells) and resuspend them with pipetting.
2. Add 200 µL of lysis buffer containing 2 µL of beta-mercaptoethanol (β-ME). Vortex quickly and let stand for 5 min.
3. Centrifuge the solution at 16,000 x g for 10 min. Collect the supernatant and mix with 200 µL of 70% ethanol.
4. Use the attached column to remove the solvent by centrifugation at 14,000 x g for 1 min.
5. Wash using wash solution 1 and 2 to completely remove non-RNAs by centrifugation at 14,000 x g for 1 min.
6. Centrifuge again at 14,000 x g for 2 min to remove residual ethanol.
7. Add 50 µL of distilled water, centrifuge at 14,000 x g for 1 min, and collect the solution.
8. Measure the RNA concentration using OD₂₆₀ with a spectrophotometer.
   RNA concentration (µg/mL) = (OD₂₆₀) x (40 µg RNA/mL) and OD₂₆₀/OD₂₈₀ > 2. RNA samples should have an RNA integrity number (RIN) > 7.

3. RNAseq profiling and bioinformatics analysis

NOTE: RNAseq analysis was performed commercially (see Table of Materials) to investigate the differential genes in the HT29-derived tumorspheres compared to parental HT29 cells.

1. Use commercial services for RNAseq steps, including library construction, library quality control, and DNA sequencing.
2. The data report should contain important information, including the read counts, log2 fold change, and p value. Select the differential genes according to the following parameters: genes with a > 1 log2 fold change with read counts >100 in the HT29 tumorsphere group, and genes <-1 log2 fold change with read count >100 in the HT29 parental group. In this case, a p value < 0.05 was considered acceptable and the data were used (Table 1).
   NOTE: Here, a gene count >100 was used as the threshold to continue the study of a particular gene and validate its expression.
3. Use statistical analysis software (see Table of Materials), to show a heatmap and identify overexpressed genes >1 and downregulated genes < 1 in log2 fold change.
4. Use R software to draw the Volcano Plot with x: log2 fold change; y: -log10 (p value) to show the differential genes.
   1. Install R library
      install.packages(library(calibrate)) 
   2. Read the data in RStudio with the following program:
      res <-read.csv("/Users/xxx.csv", header=T)
      head(res)
      with(res, plot(log2FoldChange, -log10(pvalue), pch=19, main="HT29CSC vs HT29", xlim=c(-6,6), col="#C0C0C0"))
      with(subset(res, pvalue<.05 & log2FoldChange>1), points(log2FoldChange, -log10(pvalue), pch=19, col="red"))
      with(subset(res, pvalue<.05 & log2FoldChange<(-1)), points(log2FoldChange, -log10(pvalue), pch=19, col="blue"))
      with(subset(res, pvalue>.05), points(log2FoldChange, -log10(pvalue), pch=19, col="#444444"))
      abline(h=1.3, lty=2)
      abline(v=1, lty=2)
      abline(v=(-1), lty=2)
   3. Execute the run to obtain the Volcano Plot.

4. Driver gene selection

1. Select Single gene Input in NetworkAnalyst.
2. Copy and paste the selected overexpressed genes from Table 1 with “human” specified as the organism and ID type Official Gene Symbol.
   NOTE: Alternatively, use Ensembl Gene ID for copy and paste.
3. Insert the data by clicking Upload and Proceed to analyze it using protein-protein interaction (PPI) following genetic PPI.
4. Use the STRING interactome database with a confidence score cutoff of 900 to show the seed genes cross-linking the uploaded genes. The seed genes associating with more individual genes were selected as driver genes that may be involved in maintaining formation of HT29-derived tumorspheres.
   NOTE: There are three interactome datasets for use: IMEx, STRING, and Rolland. STRING contains higher confidence experimental evidence. With lower uploaded gene numbers, IMEx can be selected to predict and pick up the driver genes in the interactome networks.
5. Select Proceed in the mapping overview.
6. Select White in the Background and Force Atlas in the Layout knob.
7. Select PANTHER BP to analyze the upregulation gene group.
   NOTE: This shows that HSPA5 was responsible for anti-apoptosis in the HT29-derived tumorspheres in this study (Figure 3A). To narrow down the specific functional field, KEGG, GO, or PANTHER classification can be used alternatively to select the specific driver genes.

Results

To establish the model for investigating the mechanism in cancer stem cells, colorectal HT29 cells were used to culture the cancer stem-like tumorspheres in vitro in a low-attachment plate containing B27, EGF, bFGF, HGF, and IL6. The tumorspheres >100 µm in diameter were formed in 7 days (Figure 1A). The tumorspheres were trypsinized to single cells and analyzed using flow cytometry to detect LGR5 and CD133 expression. LGR5 increased in the HT29-drived tumorspheres from 1.1% to 11.4% and ...

Discussion

In this study, cultured cancer stem-like tumorspheres were used as a model in analyzing RNAseq data with available bioinformatics. For a disease model, HT29-derived tumorspheres were used. Because the tumorspheres have drug resistance against tumor therapies, the established model can be used to investigate the detailed mechanisms of resistance by investigating differences in gene expression. Moreover, genomic technology using RNAseq with available bioinformatics provides rapid understanding of the study model so the gen...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
iRiS Digital Cell Imaging System	Logos Biosystems, Inc	I10999	for observing the formation of tumorspheres
Flow cytometry	BD biosciences	FACSCalibur	for detecting the LGR5 and CD133 in the tumorspheres
anti-LGR5-PE	Biolegend	373803	LGR5 detection reagent
anti-CD133-PE	Biolegend	372803	CD133 detection reagent
EGF	GenScript	Z00333	for culture of tumorspheres
bFGF	GenScript	Z03116	for culture of tumorspheres
HGF	GenScript	Z03229	for culture of tumorspheres
IL6	GenScript	Z03034	for culture of tumorspheres
PureLink RNA extraction kit	Invitrogen	12183025	isolate total RNA for RNAseq analysis
RNAseq performance	Biotools, Taiwan		RNAseq analysis is done commerially by Biotools, Ttaiwan
NetworkAnalyst	Institute of Parasitology, McGill University, Montreal, Quebec, Canada		http://www.networkanalyst.ca/
Prism	GraphPad Software		a statistical analysis software