Esha Manchanda

Doctoral Researcher

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Hi! I am Esha, a first year PhD student at the Lee Kong Chian School of Medicine, Nanyang Technological University. I previously completed my Masters in Biomedical Data Science at Nanyang Technological University and graduated with a Bachelors from Ashoka University with a Gold Medal in Computer Science.

I am passionate about improving human health through the confluence of technology and medicine. My research interests lie at the intersection of neuroscience, immunology, machine learning and epigenetics. I am especially interested in remyelination in multiple sclerosis.

My PhD research is supervised by Dr Tianrong Yeo and Professor Andrew Tan, and focuses on the analysis of spatial and single-cell omics in multiple sclerosis.

Beyond my research, I love playing badminton, running, solo travelling and learning korean!


Ongoing Work

  1. Hi
    Dysregulation of the Metabolic-Inflammatory Axis in Progressive Multiple Sclerosis

    Esha Manchanda, Eka Saipuljumri, Jialiu Zeng, Chih Hung Lo
    In Preparation 
    Presented as a talk and poster at SSBBI & Neuroscience Singapore 2024
    Slides

    Metabolic impairments are shown to be implicated in the pathogenesis of neuroinflammatory diseases such as MS, and therapies aimed at enhancing metabolic functions could potentially attenuate neuroinflammation and open new avenues for treatment of progressive MS. In this study, we are investigating the role of mitochondrial, autophagic, and lysosomal dysfunction in progressive MS, exploring their interconnected roles in driving neuroinflammation and neurodegeneration.

  2. Hi
    A LSTM Approach to Blood Glucose Predictions for Personalized T1D Management

    Esha Manchanda, Jialiu Zeng, Chih Hung Lo
    In Preparation 
    Presented as a poster at FEBS-IUBMB-ENABLE 2024

    Type one diabetics need to be their own pancreas – it’s almost as though pumping blood to your body required having to know when it’s time to pump blood, rather than having your heart do it automatically. To ease the burden on T1Ds and improve blood glucose regulation, automated insulin delivery systems are being developed. Moreover, managing T1D is significantly individualized— two people can have completely different blood glucose dynamics under identical exogenous conditions, and current systems are not well-suited to capture the unique disease patterns of a single individual. Therefore, in this work, we are building a personalised blood glucose prediction model to address the inherent inter-patient variability in people with T1D.

Past Work

  1. Undergraduate Thesis Candidate, Ashoka University
    August 2023 - May 2024

    In my thesis, I explored the relationship between the Epstein-Barr Virus and Multiple Sclerosis. Through a mathematical modeling approach, my work studies the potential link between EBV and MS, closely examining the immune system's response to EBV infection. It investigates the role of T-cell exhaustion in uncontrolled EBV lytic activity in B cells, leading to chronic inflammation and potentially the development of MS.

    Manuscript
  2. Research Intern, Max Hospitals & Ashoka University
    January 2023 - June 2023

    Over the past year, I have developed a growing interest in the gut microbiome and its impact on health. I worked on a project employing model-agnostic meta-learning techniques aimed at predicting diseases through the targeted analysis of fecal gut microbiome data. Beyond this, my interest extends to exploring the intricate connections between the gut microbiome and various health dimensions, including its role in both wellness and disease pathology.

    Report
  3. Computational Genomics Research Intern, Ashoka University
    September 2022 - December 2022

    Traditional bulk methods analyze an average over many cells, which can mask variations between individual cells. Single cell analysis helps investigate the heterogeneity between cells. Deciphering cell-cell heterogeneity at the chromatin level is very important while developing accurate disease models and determining patient responses to specific drugs. I conducted a comprehensive review of algorithms for single-cell analysis of accessible chromatin (scATAC-seq) to assess the computational challenges associated with scATAC-seq analysis including inherently sparse data, determination of features, peak calling, the impact of sequencing coverage and noise, and clustering performance.

    4. Teaching

    I really enjoy teaching, and inspired by my many great teachers, I adopt a constructivist teaching style—where I focus on guiding students to construct knowledge rather than passively absorb information.

    Teaching Assistantships

    Workshops Conducted

    • IEEE Ashoka PySci Computational Biology Workshop (November 2023)
      [Hands-on exercise]
    • Computer Science Society Tools for Web Scraping (October 2021)
      [Code]