Differential Expression

of Genes During Oogenesis in C. elegans

Rose Runyan and Emaan Kapadia

Oogenesis Along the Spatial Axis of the Worm

Background

• Bulk-RNA seq & microarray has been previously done due to small size of C. elegans germline
• lack detail on different stages
• Micro-dissections recently allow single-cell RNA-seq
• current knowledge limited to regulation via RBP and miRNA binding

Project Goals

Use single-cell RNA-seq to analyze gene expression differences in oocyte developmental stages

• identify gene expression patterns

Single-Cell versus Bulk

• Gene expression at individual cell level
• High resolution insights
• Discovery of new genes or cell types

• Average gene expression across many cells
• Simpler with lower data complexity
• Population level insights

Single Cell RNA-Seq

Bulk RNA-Seq

Library Prep - Microdissection

Typical scRNA-seq

Single-Cell RNA-Sequencing

NYU Center for Genomics and Systems Biology

“scRNA-seq approach for bulk RNA”

One segment

Project Pipeline

1. Obtain raw RNA sequencing files
2. Process raw data
1. cut off adaptor sequences and perform quality control
2. align reads to a reference genome
3. obtain transcript per million (TPM) estimates
3. Analyze gene expression
• perform differential analysis using DEseq
• visualize gene expression profiles using UMAP

Project Pipeline

• Obtain raw RNA sequencing files
• Process raw data
• cut off adaptor sequences and perform quality control
• align reads to a reference genome
• obtain transcript per million (TPM) estimates
• Analyze gene expression
• perform differential analysis using DEseq
• visualize gene expression profiles using UMAP

Obtaining Files

Each segment of the germline was analyzed as a “single cell” during sample collection

fetching files and converting into fasta format

Project Pipeline

• Obtain raw RNA sequencing files
• Process raw data
• cut off adaptor sequences and perform quality control
• align reads to a reference genome
• obtain transcript per million (TPM) estimates
• Analyze gene expression
• perform differential analysis using DEseq
• visualize gene expression profiles using UMAP

Quality Trimming

Trim Galore performs quality trimming and auto-detects Illumina adaptors for trimming

low-quality bases trimmed off of read

minimum length of read

Quantifying TPM

Using default settings, Salmon obtains transcripts per million (TPM) estimates of genes and transcripts

TPM are used for filtering steps, further quality control, and gene analysis

align illumina reads to

c. elegans reference genome

align illumina reads to

c. elegans reference genome

Quantifying TPM

Using default settings, Salmon obtains transcripts per million (TPM) estimates of genes and transcripts

TPM are used for filtering steps, further quality control, and gene analysis

align illumina reads to

c. elegans reference genome

Project Pipeline

• Obtain raw RNA sequencing files
• Process raw data
• cut off adaptor sequences and perform quality control
• align reads to a reference genome
• obtain transcript per million (TPM) estimates
• Analyze gene expression
• perform differential analysis using DEseq
• visualize gene expression profiles using UMAP

Visualization of Gene Expression Across Oocyte Development - Test

Proof of concept (n=1)

Visualization of Gene Expression Across Oocyte Development

UMAP combining multiple samples (n=5)

Visualization of Gene Expression Across Oocyte Development

UMAP combining multiple samples (n=5)

Challenges/Caveats

• Paper reproducibility
• Preprint with missing data in supplements
• Filtering steps were skipped in our analysis due to missing data
• Different RNA-seq analysis tools
• Monocle versus DEseq

Conclusions

• Reproducibility ! ! !
• Confirmed differential expression between oogenesis stages
• In addition to known post-transcriptional/translational changes

Future Directions

GO term analysis on DEGs

DEGs between segments

Thank You!

Questions?