Research

Our research programme aims to innovate and engineer novel technologies to help understand and manage infectious diseases using single molecule detection, single cell analysis, quantitative genomics and high-resolution imaging. Using highly sensitive detection of biological macromolecules like proteins or nucleic acids by both optical and non-optical methods, we determine the molecular behaviour at the single molecule level as well as its distribution at the population level.

Tools for Biosensing and Lab-on-chip Devices

We are developing sensitive and easy-to-implement fluorescence diagnostic assays for low resource settings. Our assay development builds on our know-how and diverse expertise in optics, microfluidics, biosensors and engineering. For example, we have demonstrated how DNA aptamer based sensing of proteins can be implemented using protein induced fluorescence enhancement that is rapid, sensitive and modular (Umrao, et al. Sens. & Actuators: Chem. 2018 ). Similarly, we developed a sensitive smartphone assay for antibiotic (kanamycin) detection that takes advantage of the ratiometric nature of the FRET signature (Umrao, et al. RSC Advances 2019 ).

To push the limits of non-optical detection of cells, we have developed a low-cost disposable Lab-On-Chip device for cell-in-droplet counting. We established a novel multi-layer device fabrication methodology that uses fusible alloy to replace metal microelectrodes but demonstrates high sensitivity for cell impedance measurements in a flow cytometry configuration (Panwar and Roy, MicroE Engineering 2019).

Our key findings:

    • DNA aptamers with a single cyanine dye (used as a reporter) for protein binding and employed for rapid and inexpensive diagnostic as well as screening assays.

    • We show that DNA aptamer based FRET detection of small molecules (like antibiotics) can be robustly implemented using a smartphone camera.

    • Using our highly sensitive fusible alloy impedance microelectrodes, we demonstrated accurate quantification of label free cells in aqueous droplets (for droplet cell assays).


  1. S Umrao, V Jain, Anusha, B Chakraborty, R Roy, "Protein-induced fluorescence enhancement as aptamer sensing mechanism for thrombin detection." Sensors and Actuators B: Chemical 267: 294-301 doi:10.1016/j.snb.2018.04.039 (2018)

  2. S Umrao, Anusha S, V Jain, B Chakraborty, R Roy, "Smartphone-based kanamycin sensing with ratiometric FRET" RSC Advances, 9, 6143-6151 doi:10.1039/C8RA10035G (2019)

  3. J Panwar and R Roy, "Fusible alloy 'in-contact' microelectrodes: a rapid and economic microfabrication alternative for microfluidic impedance cytometry" MicroE Engineering, doi: https://doi.org/10.1016/j.mee.2019.111010 (2019)

Understanding Self-Assembly of Macromolecular Nanostructures

Biomolecular self-assembly is important for several cellular life processes, as well as for growth and virulence of pathogens. A key challenge in studying self-assembly of (cytotoxic) macro-assemblies lies in tracking the dynamics of their formation via pathways comprising of a large number of diffraction-limited heterogeneous intermediates. We addressed this problem in the study of 2D pore assembly of bacterial pore forming toxin, Cytolysin A (implicated in bacterial virulence) on phospholipid membranes. Using a custom-built total-internal-reflection fluorescence (TIRF) microscope combined with single particle tracking and photobleaching analysis, we demonstrated, that the conformations and oligomeric states of the membrane bound toxin protein can be accurately extracted (Sathyanarayana, et al. PNAS 2018). This allowed us to show how cholesterol, a key component of mammalian cell membranes, allowed the toxin to selectively target the mammalian cells. Due to broad structural similarities between pore-forming toxins, these mechanisms to impart membrane selectivity could be common.

Our key findings:

  • Dynamical evolution of 2D protein mobility provided a signature of two distinct membrane bound states (that we demonstrated represented distinct states of membrane insertion).

  • Specific role of cholesterol in selectively stabilizing the transmembrane inserted conformational state of the toxin was discovered.

  • Site directed mutagenesis revealed cholesterol binding motifs in the membrane inserted helices responsible for efficient pore formation.

  • Distribution of the membrane bound oligomers from photobleaching experiments revealed how cholesterol enhanced oligomerization of the toxin further assisting pore mediated cell lysis.

Furthermore, using artificially designed polymer supported bilayers that are better mimics of cell membranes, we are currently exploring the effect of molecular crowding on the diffusional properties and kinetics of molecular assembly on the membrane.


  1. P Sathyanarayana, S Maurya, A Behera, M Ravichandran, SS Visweswariah, KG Ayappa, and R Roy, "Cholesterol promotes Cytolysin A activity by stabilizing the intermediates during pore formation" PNAS (USA), 115 (31) E7323-E7330, doi:10.1073/pnas.1721228115 (2018)

Virus Metagenomics and Immune Response to Infections

Molecular heterogeneity is a key hallmark of the RNA viruses due to their high mutation rates. Therefore, RNA viruses like Dengue propagate as a quasispecies of closely related genotypes in the host. This helps the virus in rapid adaptation, acquiring drug resistance and enabling their immune escape. Sequence diversity and virulence of a viral species is regulated by the host selection pressures at different levels of its life cycle. Since imaging cannot capture the large number of variants, we focus on adapting next generation sequencing (NGS) to accurately quantify variants of genomic RNA (or gene) and probe the population level distributions.

Unfortunately, full genome data on Dengue viral strains prevalent in India and world-wide has been limited. As part of a multi-center team spanning several research institutions and hospitals across India working on epidemiology of Dengue, we established expertise in viral genomics and data analysis. By focusing our sequencing pipeline on recovery efficiency and small sample volume, we could sequence prevalent Dengue virus strains directly from stored serum samples from prior years (Kar, et al. IJID 2018 ).

Our key findings:

  • Direct sequencing of Dengue virus from clinical serum samples allowed us to recover 21 complete genome sequences (the largest such dataset from India).

  • We found that two closely related strains of the Cosmopolitan genotype of Dengue serotype 2 dominated (> 90%) during 2012-15.

  • Almost all of the envelope (E) protein mutations mapped to its exposed surface in its dimeric conformation suggesting immunological selection being the major driving force in evolution of the Dengue virus.

This study is being extended to characterize Dengue strains from multiple centers across India to understand the evolutionary dynamics of Dengue virus over the last decade. In parallel, working in collaboration with Prof. Narendra Dixit (ChemE, IISc), we are developing mathematical models of immune response dynamics that can help us connect experimental observations and develop quantitative understanding of underlying immunological phenomena in response to viral infections. For example, recently we showed how the level of T-cell exhaustion in persistent infection can determine the outcome of antiviral therapy in Hepatitis C viral infection (Baral, et al. Immun. & Cell Biol. 2018 ).


  1. M Kar, A Nisheetha, A Kumar, S Jagtap, J Shinde, M Singla, S Marimuthu, A Pandit, A Chandele, SK Kabra, S Krishna, R Roy, R Lodha, C Pattabiraman, G Medegeshi, "Isolation and molecular characterization of Dengue virus clinical isolates from pediatric patients in New Delhi" International J of Infectious Diseases, pii:S1201-9712(18)34953-1 doi:10.1016/j.ijid.2018.12.003 (2018)

  2. S Baral, R Roy, NM Dixit, “Modelling how reversal of immune exhaustion elicits cure of chronic hepatitis C after the end of treatment with direct-acting antiviral agents” Immunology & Cell Biology, 96(9), 969-980, doi:10.1111/imcb.12161 (2018)

Emergence of Cellular Heterogeneity

Heterogeneous outcomes of cell infection (where only a fraction of cells are infected in spite of the entry of pathogen) is a common observation. Infection by a small number of pathogens, heterogeneity in cellular gene expression, complex interplay of host proteins with pathogen components and counter-mechanisms by pathogen can contribute to non-intuitive and highly diverse outcomes of virus or bacteria growth during infection. We have employed mathematical modeling and imaging tools to understand contributing factors that leads to emergence of infection heterogeneity. For example, a simple mathematical framework to describe the temporal evolution of various viral molecules by modeling all the intracellular processes relevant to the viral RNA (for the Flaviviridae family) was developed (Chhajer, et. al. under preparation). We used a stochastic framework to address resource (viral RNA) sharing during the start of infection when viral RNA is present in low copy. This model accurately captures the experimentally measured viral dynamics of Hepatitis C virus. Based on our findings, we propose a new definition of Multiplicity of Infection (MOI) that would be useful in analysis of candidate fitness functions like infectivity, viral loads and mutability.

Our key findings:

  • We discovered that the flavivirus lifecycle is characterized by regimes where majority of the viral RNA genome is sequentially associated with translation, replication and packaging.

  • Compartmentalization of viral replication in vesicular membranous structures (VMS, as suggested by earlier reports) is a key feature of flaviviral lifecycle since the viral dynamics without compartments is not reproducible.

  • The stochastic model predicts the importance of additional cellular processes like maturation of the VMS that have not been investigated before and its role in heterogeneous outcomes of infection.


  1. H Chajjer, VA Rizvi and R Roy, "Life cycle process dependencies of positive-sense RNA viruses suggest strategies for inhibiting productive cellular infection" BioRxiv https://www.biorxiv.org/content/10.1101/2020.09.19.304576v1 (2020)