Biology subjects

Ramanathan, V.

Publications and source records attributed to Ramanathan, V..

4 recordsLinked to original sources

bioRxiv2025

Predicting Molecular Taste: Multi-Label and Multi-Class Classification

Predicting the taste of chemical compounds is a complex task and has been a challenge for decades. This study explores the application of machine learning to predict taste profiles of chemical compounds using the ChemTastesDB dataset, comprising 2,944 tastants categorized into 44 taste labels and 9 taste classes. Addressing the challenges of label imbalance and correlation, the dataset was preprocessed using iterative stratified sampling and feature representations such as Mordred descriptors, Morgan fingerprints, and Daylight fingerprints. Baseline random forest models, along with binary relevance and classifier chains, were employed for multi-label classification, with evaluation metrics including micro-averaged F1 scores, precision, and recall. Results demonstrated that binary relevance models, particularly with Morgen fingerprints, achieved superior F1 scores, outperforming classifier chains likely due to random label ordering. Label correlation analysis via co-occurrence matrices and community detection revealed significant associations between taste labels, providing deeper insights into molecular taste interactions. Feature importance analysis highlighted structural elements influencing taste prediction. This work underscores the potential of computational models in advancing flavor science and paves the way for future exploration with deep learning and optimized label dependencies.

bioinformatics↗

bioRxiv2025

Genome structure mapping with high-resolution 3D genomics and deep learning

Gene expression is often regulated by distal enhancers through cell-type-specific 3D looping interactions, but compre-hensive mapping of these interactions across cell types is experimentally intractable. To address this gap, we introduce an integrated approach where we generate ultra-deep Region Capture Micro-C (RCMC) and Micro-C data specifically designed for state-of-the-art deep learning architectures. We developed Cleopatra, an attention-based deep learning model that takes epigenomic inputs and is pre-trained on genome-wide Micro-C data followed by fine-tuning with high-resolution RCMC data. Cleopatra accurately predicts 3D maps at sub-kilobase bin sizes and unprecedented resolution, enabling us to generate ultra-high-resolution, genome-wide 3D contact maps across four human cell types. These maps revealed cell-type-specific microcompartments and over 900,000 loops across the cell types, about half of which are cell-type-specific. Using Cleopatra maps, we observe that promoters form about a dozen loops on average, and that expression increases monotonically with the number of loops, indicating that looping is associated with higher gene expression. We further show the enhancer-promoter loops are often anchored by CTCF, and nominate new transcription factors that may regulate cell-type-specific enhancer-promoter interactions. Overall, we establish a framework for ultra-high-resolution 3D genome mapping, providing a broadly applicable resource for gaining new insights into cell-type-specific gene regulation.

genomics↗

bioRxiv2024

Multi-modal, Label-free, Optical Mapping of Cellular Metabolic Function and Oxidative Stress in 3D Engineered Brain Tissue Models

Brain metabolism is essential for the function of organisms. While established imaging methods provide valuable insights into brain metabolic function, they lack the resolution to capture important metabolic interactions and heterogeneity at the cellular level. Label-free, two-photon excited fluorescence imaging addresses this issue by enabling dynamic metabolic assessments at the single-cell level without manipulations. In this study, we demonstrate the impact of spectral imaging on the development of rigorous intensity and lifetime label-free imaging protocols to assess dynamically metabolic functions over time in 3D engineered brain tissue models comprised of human induced neural stem cells, astrocytes, and microglia. Specifically, we rely on multi-wavelength spectral imaging to identify the excitation/emission profiles of key cellular fluorophores within human brain cells, including NAD(P)H, LipDH, FAD, and lipofuscin. These enable the development of methods to mitigate lipofuscins overlap with NAD(P)H and flavin autofluorescence to extract reliable optical metabolic function metrics from images acquired at two excitation wavelengths over two emission bands. We present fluorescence intensity and lifetime metrics reporting on redox state, mitochondrial fragmentation, and NAD(P)H binding status in neuronal monoculture and the triculture systems to highlight the functional impact of metabolic interactions between different cell types. Our findings reveal significant metabolic differences between neurons and glial cells, shedding light on metabolic pathway utilization, including the glutathione pathway, OXPHOS, glycolysis, and fatty acid oxidation. Collectively, our studies establish a label-free, non-destructive approach to assess the metabolic function and interactions among different brain cell types relying on endogenous fluorescence and illustrate the complementary nature of the information that is gained by combining intensity and lifetime-based images. Such methods can improve understanding of physiological brain function and dysfunction that occurs at the onset of cancers, traumatic injuries and neurodegenerative diseases.

bioengineering↗

bioRxiv2023

Event-based Single Molecule Localization Microscopy (eventSMLM) for High Spatio-Temporal Super-resolution Imaging

Photon emission by single molecules is a random event with a well-defined distribution. This calls for event-based detection in single-molecule localization microscopy. The detector has the advantage of providing a temporal change in photons and emission characteristics within a single blinking period (typically, [~] 30 ms) of a single molecule. This information can be used to better localize single molecules within a user-defined collection time (shorter than average blinking time) of the event detector. The events collected over every short interval of time / collection time ([~] 3 ms) give rise to several independent temporal photon distributions (tPSFs) of a single molecule. The experiment showed that single molecules intermittently emit photons. So, capturing events over a shorter period / collection time than the entire blinking period gives rise to several realizations of the temporal PSFs (tPSFs) of a single molecule. Specifically, this translates to a sparse collection of active pixels per frame on the detector chip (image plane). Ideally, multiple realizations of single-molecule tPSF give several position estimates of the single-molecules, leading to multiple tPSF centroids. Fitting these centroid points by a circle provides an approximate position (circle center) and geometric localization precision (determined by the FWHM of the Gaussian) of a single molecule. Since the single-molecule estimate (position and localization precision) is directly driven by the data (photon detection events on the detector pixels) and the recorded tPSF, the estimated value is purely experimental rather than theoretical (Thomsons formula). Moreover, the temporal nature of the event camera and tPSF substantially reduces noise and background in a low-noise environment. The method is tested on three different test samples (1) Scattered Cy3 dye molecules on a coverslip, (2) Mitochondrial network in a cell, and (3) Dendra2HA transfected live NIH3T3 cells (Influenza-A model). A super-resolution map is constructed and analyzed based on the detection of events (temporal change in the number of photons). Experimental results on transfected NIH3T3 cells show a localization precision of [~] 10 nm, which is [~] 6 fold better than standard SMLM. Moreover, imaging HA clustering in a cellular environment reveals a spatio-temporal PArticle Resolution (PAR) (2.3lp x{tau} ) of 14.11 par where 1 par = 10-11 meter.second. However, brighter probes (such as Cy3) are capable of [~] 3.16 par. Cluster analysis of HA molecules shows > 81% colocalization with standard SMLM, indicating the consistency of the proposed eventSMLM technique. The single-molecule imaging on live cells reveals temporal dynamics (migration, association, and dissociation) of HA clusters for the first time over 60 minutes. With the availability of event-based detection and high temporal resolution, we envision the emergence of a new kind of microscopy that is capable of high spatio-temporal particle resolution in the sub-10 par regime.

biophysics↗