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Day 5: Student project

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Abdoallah Sharaf edited this page Jul 14, 2026 · 4 revisions

Student Project: RNA-seq Analysis of UV-Induced Cell Apoptosis

Project Overview

For your final project, you will independently analyze an RNA-seq dataset generated during the VTK 2024 practical course. The objective is to apply the complete RNA-seq workflow that you learned throughout this practical course to investigate the transcriptional response of HeLa Kyoto wild-type (WT) cells following UV-induced DNA damage.

Unlike the guided exercises based on Boström et al. (2017), this project is designed to give you the opportunity to perform the analysis independently. You are encouraged to consult software documentation, online resources, and the course materials whenever necessary.

The aim is not simply to obtain a list of differentially expressed genes, but to demonstrate that you understand each stage of an RNA-seq analysis workflow—from raw sequencing reads to biological interpretation.

Experimental Design

Biological Question

How does UV-induced DNA damage alter gene expression in HeLa cells over time, and which biological pathways are associated with the cellular response and apoptosis?

Experimental Setup

At the beginning of each VTK practical week:

  • 5 × 10⁶ HeLa Kyoto WT cells were seeded in a Petri dish containing 5 mL growth medium.
  • Cells were cultured for 24 hours.
  • Cells were exposed to 20 mJ UV irradiation to induce DNA damage.
  • Samples were harvested at:
    • 0 hours (Control)
    • 1 hour
    • 3 hours
  • Each condition contains three biological replicates, resulting in 9 RNA samples.

After harvesting:

  1. Cells were lysed directly on the dish using 300 μL Milli-Q water.
  2. The lysate was transferred into a microcentrifuge tube.
  3. The plate was rinsed with an additional 100 μL Milli-Q water, which was added to the same tube.
  4. Samples were immediately snap-frozen in liquid nitrogen.
  5. Samples were stored at −80°C until RNA extraction and sequencing.

Experimental Design

Condition Time Biological Replicates
Control 0 h 3
UV-treated 1 h 3
UV-treated 3 h 3

Total samples: 9

Sample Metadata

Sample Cell Type Time Replicate
0h_Rep1 HeLa Kyoto WT 0 h 1
0h_Rep2 HeLa Kyoto WT 0 h 2
0h_Rep3 HeLa Kyoto WT 0 h 3
1h_Rep1 HeLa Kyoto WT 1 h 1
1h_Rep2 HeLa Kyoto WT 1 h 2
1h_Rep3 HeLa Kyoto WT 1 h 3
3h_Rep1 HeLa Kyoto WT 3 h 1
3h_Rep2 HeLa Kyoto WT 3 h 2
3h_Rep3 HeLa Kyoto WT 3 h 3

Raw Sequencing Data

The sequencing data are paired-end Illumina FASTQ files.

Example:

ls VTK2024-RNAseq/*.fastq.gz
T0_MAZP0_1.fastq.gz
T0_MAZP0_2.fastq.gz
T0_PF0h_1.fastq.gz
T0_PF0h_2.fastq.gz
T0_SH0_1.fastq.gz
T0_SH0_2.fastq.gz

T1_Lh1_1.fastq.gz
T1_Lh1_2.fastq.gz
T1_MK1h_1.fastq.gz
T1_MK1h_2.fastq.gz
T1_MW1h_1.fastq.gz
T1_MW1h_2.fastq.gz

T3_IA03_1.fastq.gz
T3_IA03_2.fastq.gz
T3_JL3h_1.fastq.gz
T3_JL3h_2.fastq.gz
T3_SP3_1.fastq.gz
T3_SP3_2.fastq.gz

Each sample consists of two FASTQ files:

  • Read 1 (R1)
  • Read 2 (R2)

Project Workflow

You are expected to perform the same RNA-seq workflow that we completed for Boström et al. (2017).

1. Data Pre-processing

  • Create a suitable project directory structure.
  • Inspect the raw FASTQ files.
  • Perform quality assessment using FastQC.
  • Examine sequencing quality.
  • Remove adapters and low-quality bases using fastp.
  • Reassess read quality after trimming.

2. Reference Preparation

  • Download or use the provided reference transcriptome.
  • Build (or use) the provided Kallisto index.
  • Understand why indexing is required.

3. Transcript Quantification

Use Kallisto to quantify transcript abundance for each sample.

After quantification:

  • Inspect the output files.
  • Understand TPM, estimated counts, and abundance estimates.
  • Organize all outputs into a single directory.

4. Differential Gene Expression Analysis

Using R, perform the downstream analysis:

  • Import Kallisto outputs using tximport.
  • Generate gene-level counts.
  • Create the DESeq2 dataset.
  • Normalize the data.
  • Perform differential expression analysis.

Generate the following visualizations where appropriate:

  • PCA plot
  • Sample distance heatmap
  • MA plot
  • Volcano plot
  • Heatmap of differentially expressed genes

5. Functional Enrichment Analysis

Perform functional analysis of the differentially expressed genes.

Include:

  • Gene Ontology (GO) enrichment
  • KEGG pathway enrichment
  • Appropriate visualizations
  • Biological interpretation

6. Biological Interpretation

Interpret your results.

Consider questions such as:

  • Which genes respond to UV treatment?
  • Which genes are up- or down-regulated?
  • Which pathways are enriched?
  • Are apoptosis-related pathways activated?
  • How do the responses differ between 1 hour and 3 hours?
  • Are your findings consistent with current biological knowledge?

Optional: Using Nextflow

Throughout the practical sessions, every analysis step was performed manually to help you understand the underlying methods and software.

Since you are now familiar with Nextflow and workflow management systems, you are encouraged to complete this project using an appropriate Nextflow RNA-seq pipeline (e.g. an nf-core RNA-seq workflow) if you wish.

Using a workflow management system is considered best practice because it improves:

  • Reproducibility
  • Scalability
  • Automation
  • Portability

Regardless of the approach you choose, you should understand every step of the analysis and be able to explain and interpret your results.

Deliverables

Submit:

  • Project directory
  • Analysis scripts (R or R Markdown)
  • Figures
  • Differential expression results
  • Functional enrichment results

Prepare a short report (approximately 2–4 pages) including:

  1. Introduction
  2. Experimental Design
  3. Methods
  4. Results
  5. Functional Enrichment Analysis
  6. Discussion
  7. Conclusions

Assessment

Your project will be assessed on:

  • Correct application of the RNA-seq workflow
  • Organization and reproducibility
  • Quality of analyses and figures
  • Appropriate statistical analysis
  • Biological interpretation
  • Quality of the final report

The emphasis is on demonstrating your understanding of the complete RNA-seq analysis workflow rather than simply reproducing expected results.

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