Dissecting Cellular Heterogeneity in Complex Tissues using Single-Cell RNA-Seq

Bulk RNA-Seq provides an average gene expression profile across a population of cells, masking the inherent heterogeneity within tissues. Single-cell RNA-Seq (scRNA-Seq) overcomes this limitation by profiling the transcriptome of individual cells, allowing for the identification of distinct cell types and their unique gene expression signatures. This project will involve the computational analysis of publicly available or in-house generated scRNA-Seq data from a complex tissue or biological system.

Expected Outcomes: The student will contribute to the characterization of cellular heterogeneity within the studied system, identifying novel cell populations and their unique transcriptional profiles. This project will provide a deeper understanding of tissue organization and function at single-cell resolution. The student will develop expertise in analyzing complex scRNA-Seq datasets.

Name of research group, project, or lab
Computational Systems Biology Lab
Website
https://sites.google.com/view/csblab/home
Why join this research group or lab?
The Computational Systems Biology Lab (CSBLab) works on the intersection of biology and computation, with a focus on systems responses of diseases in plants and animal model systems. We utilize state-of-the-art systems biology frameworks to study transcriptomics changes at different scales (Bulk RNA-Seq, single-cell RNA-Seq, and spatial transcriptomics) and multi-omics profiles to delineate the factors underlying cellular functions.

We are looking for enthusiastic students with interest to learn the necessary biological data science skills which will prepare them for upcoming research or educational challenges. Students will gain hands-on experience in processing and interpreting complex genomic datasets. The research experience emphasizes in the development of essential bioinformatics skills and the use of cutting-edge software tools to explore biological data. Throughout the research, students will apply their knowledge to real-world datasets, culminating in module-based projects that showcase their analytical and presentation skills. This research experience aims to equip students with the necessary tools and knowledge to tackle contemporary research questions in genomics, fostering critical thinking and data interpretation skills.

Note: If student want to continue their research in the CSBLab after Summer/Fall, they can do so. 

Representative publication
Mishra_CSBLab.pdf
Logistics Information:
Project categories
Applied and Computational Mathematics and Statistics
Biological Sciences
Chemical and Biomolecular Engineering
Chemistry and Biochemistry
Student ranks applicable
Junior
Senior
Graduate Student
Student qualifications
  • Strong interest in genomics, molecular biology, and computational analysis.
  • Basic understanding of genetics and gene regulation.
  • Familiarity with command-line interfaces and basic programming concepts (e.g., Python or R) is a plus but not required. We will provide training.
  • Enthusiasm for learning new bioinformatic tools and techniques.
  • Should have access to a laptop and willing to work on cloud through ND CRC.
Hours per week
2 credits / 6-12 hours
3 credits / 12+ hours
Summer - Full Time
Summer - Part Time
Compensation
Research for Credit
Unpaid - Volunteer
Number of openings
2
Techniques learned

The student will gain hands-on experience in:

  • scRNA-Seq Data Processing: Understanding the steps involved in scRNA-Seq data generation and learning to perform quality control, filtering, and normalization of raw sequencing data.
  • Dimensionality Reduction and Clustering: Applying computational algorithms (e.g., PCA, t-SNE, UMAP) to reduce the high dimensionality of scRNA-Seq data and identify distinct clusters of cells based on their gene expression profiles.
  • Cell Type Identification and Annotation: Utilizing marker gene identification and leveraging existing knowledge bases to annotate the identified cell clusters and assign cell type identities.
  • Differential Gene Expression Analysis (Single-Cell Level): Identifying genes that are differentially expressed between different cell types or conditions within specific cell populations.
  • Trajectory Inference and Pseudotime Analysis (Optional): Exploring computational methods to reconstruct developmental trajectories or dynamic processes within the studied tissue by ordering cells based on their transcriptional similarity.
  • Visualization and Interpretation: Generating insightful visualizations (e.g., t-SNE/UMAP plots, violin plots, heatmaps) to represent cellular heterogeneity and gene expression patterns.
Contact Information:
Mentor
Bharat Mishra
bmishra2@nd.edu
Principal Investigator
Name of project director or principal investigator
Bharat Mishra
Email address of project director or principal investigator
bmishra2@nd.edu
2 sp. | 9 appl.
Hours per week
2 credits / 6-12 hours (+3)
2 credits / 6-12 hours3 credits / 12+ hoursSummer - Full TimeSummer - Part Time
Project categories
Applied and Computational Mathematics and Statistics (+3)
Applied and Computational Mathematics and StatisticsBiological SciencesChemical and Biomolecular EngineeringChemistry and Biochemistry