Vikas Bansal, Germany

German Center for Neurodegenerative Diseases (DZNE) Biomedical Data Science & Machine Learning Group
Currently a Junior Group Leader at DZNE Tuebingen, Germany. Prior to this, I finished my PhD with the prestigious Marie Curie Early Stage Researcher fellowship as a computational biologist and did around 3 years of postdoctoral research. I had been curious and investigating biological questions independently using high throughput data analysis and computational models. Main interests include high-throughput sequencing data analysis, epigenetics, algorithm designing and biostatistics. Long term goal - Utilising artificial intelligence in life sciences to understand human pathology. Keywords ● Bioinformatics | Computational Biology ● Biostatistics ● Biomedical Informatics ● Machine Learning Techniques ● Research Writing ● Teaching and Supervision

Presenter of 3 Presentations

 Conference Calendar

Computational approaches for interpreting and integrating scRNA‐seq data

Session Name
INDUSTRY SYMPOSIUM (NO CME/CPD CREDIT)
Session Type
SPONSORED SYMPOSIUM
Date
12.03.2021, Friday
Session Time
10:00 - 12:00
Room
Industry Symposia 1
Lecture Time
10:00 - 10:30
Presenter
Session Icon
Live

LIVE DISCUSSION

Session Name
LIVE DISCUSSION - DISEASE MODELLING WITH HUMAN CELLS -TREATMENT STRATEGIES
Session Type
LIVE SYMPOSIUM DISCUSSION
Date
10.03.2021, Wednesday
Session Time
17:00 - 17:30
Room
Live Symposia Discussion E
Lecture Time
17:00 - 17:30
Presenter
Session Icon
Live

A SINGLE-CELL RNA AND CHROMATIN PROFILING OF PARKINSON’S DISEASE PATIENT IPSC-DERIVED DOPAMINERGIC NEURONS

Session Name
DISEASE MODELLING WITH HUMAN CELLS -TREATMENT STRATEGIES
Session Type
SYMPOSIUM
Date
10.03.2021, Wednesday
Session Time
12:00 - 14:00
Room
On Demand Symposia E
Lecture Time
12:30 - 12:45
Presenter
Session Icon
On-Demand

Abstract

Aims

Induced pluripotent stem cell (iPSC)-derived dopaminergic (DA) neurons are an important model to study Parkinson’s disease (PD). About 100 iPSC used in this study were derived from subjects within the Parkinson’s Progression Marker Initiative (PPMI) and have their genetic background characterized. We included healthy controls, idiopathic Parkinson's Disease (iPD) patients with mutations in LRRK2, GBA and SNCA as well as unaffected mutation carriers. Using single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq), we aim to explore the cell population heterogeneity, compare the transcriptome profiles and define molecular networks that are perturbed in PD, cell-type specific enrichment of common-SNPs and novel associations with brain disorders, providing disease insight, therapeutic targets and relevant readouts for interventional testing.

Methods

Dopaminergic neurons were produced with an automated culture system, harvested on day 65 and diluted to 1000 cells/ul for capture on the 10X Genomics Chromium controller. We used Seurat (v3) within the R environment for filtering, normalization, integration of multiple single-cell libraries, unsupervised clustering, visualization, and differential expression analysis. We performed pseudotime analysis using Monocle (v3).

Results

Cell clustering produced multiple transcriptionally distinct populations. iPSC-derived dopaminergic neurons cluster showed the highest correlation with the human substantia nigra dopaminergic neurons. Comparing control cell lines and cell lines with different mutations allow us to identify differentially expressed genes that can be associated with PD across cell types.

Conclusions

Our results deliver multiple novelties: (A) a first catalogue of the cell type-specific information for patient-specific iPSC-derived dopaminergic neurons; (B) potential novel genetic associations with PD.

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