Biology subjects

Srinivasan, G.

Publications and source records attributed to Srinivasan, G..

5 recordsLinked to original sources

bioRxiv2025

An open-source pipeline for calcium imaging and all-optical physiology in human stem cell-derived neurons

High-throughput profiling of neuronal activity at single-cell resolution is essential for advancing our understanding of brain function, enabling large-scale functional screens, and modeling neurological disorders. However, existing approaches are limited by scalability, manual data processing, and variability, thus restricting their ability to detect disease-associated phenotypes. Here, we present a scalable, open-source platform that integrates optogenetic stimulation, calcium imaging, automated data acquisition, and a fully integrated analysis pipeline. By combining spontaneous and evoked activity profiling, the system enables robust quantification of dynamic neuronal responses across hundreds of stem cell-derived human neurons and multiple timepoints, facilitating phenotyping at both cellular and network levels. We validated the platform by recapitulating established activity phenotypes in neurodevelopmental disorders including CDKL5 Deficiency Disorder and SSADH deficiency. In addition, we generated CRISPR-Cas9 knock-in human induced pluripotent stem cell (hiPSC) lines stably expressing the genetically encoded calcium indicator GCaMP6s to model network dysfunction in Tuberous Sclerosis Complex (TSC). Using this system, we further demonstrated functional rescue of the altered neuronal activity observed in the TSC following pharmacological intervention. By linking single-cell dynamics to population-level phenotypes, this framework provides a powerful and broadly applicable tool for disease modeling, mechanistic studies, and therapeutic screening across a range of neurological disorders.

neuroscience↗

bioRxiv2023

DeltaAI: Semi-Autonomous Tissue Grossing Measurements and Recommendations using Neural Radiance Fields for Rapid, Complete Intraoperative Histological Assessment of Tumor Margins

Mohs Micrographic Surgery (MMS) aims to excise cutaneous cancer with real-time margin analysis. However, manual tissue grossing and analysis can be inefficient, so we propose DeltaAI, a novel workflow that utilizes Neural Radiance Fields (NeRF) to enable rapid tissue grossing and generate a 3D model in an augmented reality (AR) environment. In our study, we captured 30-second videos of 17 MMS specimens using a photogrammetry turntable and cellphone camera. Preprocessing the tissues with segmentation models, we created a dataset of 923, 360-degree-view, images per video (17 videos). Using COLMAP, we estimated poses for sparse tissue reconstructions and trained the NeRF model for 3D volumetric tissue renderings. The results demonstrated that DeltaAI generated more accurate and complete 360-degree, 3D tissue renderings compared to previous models, while also achieving significantly faster runtimes. Our proposed semi-autonomous NeRF-based workflow has the potential to enhance the speed of MMS specimen processing, measurement, report generation, and margin assessment. It can inform real-time grossing decisions, automate the export of electronic health record data, and facilitate time-efficient and complete cancer excisions. Moreover, DeltaAI can contribute to the wider adoption of AI technology in clinical settings by improving tissue modeling for manual grossing.

pathology↗

bioRxiv2023

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining

The advent of spatial transcriptomics technologies has heralded a renaissance in research to advance our understanding of the spatial cellular and transcriptional heterogeneity within tissues. Spatial transcriptomics allows investigation of the interplay between cells, molecular pathways and the surrounding tissue architecture and can help elucidate developmental trajectories, disease pathogenesis, and various niches in the tumor microenvironment. Photoaging is the histological and molecular skin damage resulting from chronic/acute sun exposure and is a major risk factor for skin cancer. Spatial transcriptomics technologies hold promise for improving the reliability of evaluating photoaging and developing new therapeutics. Current challenges, including limited focus on dermal elastosis variations and reliance on self-reported measures, can introduce subjectivity and inconsistency. Spatial transcriptomics offer an opportunity to assess photoaging objectively and reproducibly in studies of carcinogenesis and discern the effectiveness of therapies that intervene on photoaging and prevent cancer. Evaluation of distinct histological architectures using highly-multiplexed spatial technologies can identify specific cell lineages that have been understudied due to their location beyond the depth of UV penetration. However, the cost and inter-patient variability using state-of-the-art assays such as the 10x Genomics Spatial Transcriptomics assays limits the scope and scale of large-scale molecular epidemiologic studies. Here, we investigate the inference of spatial transcriptomics information from routine hematoxylin and eosin-stained (H&E) tissue slides. We employed the Visium CytAssist spatial transcriptomics assay to analyze over 18,000 genes at a 50-micron resolution for four patients from a cohort of 261 skin specimens collected adjacent to surgical resection sites for basal and squamous keratinocyte tumors. The spatial transcriptomics data was co-registered with 40x resolution whole slide imaging (WSI) information. We developed machine learning models that achieved a macro-averaged median AUC and F1 score of 0.80 and 0.61 and Spearman coefficient of 0.60 in inferring transcriptomic profiles across the slides, and accurately captured biological pathways across various tissue architectures.

pathology↗

bioRxiv2023

Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis

Graph-based deep learning has shown great promise in cancer histopathology image analysis by contextualizing complex morphology and structure across whole slide images to make high quality downstream outcome predictions (ex: prognostication). These methods rely on informative representations (i.e., embeddings) of image patches comprising larger slides, which are used as node attributes in slide graphs. Spatial omics data, including spatial transcriptomics, is a novel paradigm offering a wealth of detailed information. Pairing this data with corresponding histological imaging localized at 50-micron resolution, may facilitate the development of algorithms which better appreciate the morphological and molecular underpinnings of carcinogenesis. Here, we explore the utility of leveraging spatial transcriptomics data with a contrastive crossmodal pretraining mechanism to generate deep learning models that can extract molecular and histological information for graph-based learning tasks. Performance on cancer staging, lymph node metastasis prediction, survival prediction, and tissue clustering analyses indicate that the proposed methods bring improvement to graph based deep learning models for histopathological slides compared to leveraging histological information from existing schemes, demonstrating the promise of mining spatial omics data to enhance deep learning for pathology workflows.

pathology↗

bioRxiv2019

Dendritic speeding of synaptic potentials in an auditory brainstem principal neuron

Principal cells of the medial nucleus of the trapezoid body (MNTB) in the mammalian auditory brainstem receive most of their strong synaptic inputs directly on the cell soma. However, these neurons also grow extensive dendrites during the first four postnatal weeks. What are the functional roles of these dendrites? We studied the morphology and growth of the dendrites in the mouse MNTB using both electron microscopy and confocal fluorescence imaging from postnatal day 9 (P9; pre-hearing) to P30. The soma of principal cells sprouted 1 to 3 thin dendrites (diameter ~ 1.5 microns) by P21 to P30. Each dendrite bifurcated into 2-3 branches and spanned an overall distance of about 80 to 200 microns. By contrast, at P9-11 the soma had 1 to 2 dendrites that extended for only 25 microns on average. Patch clamp experiments revealed that the growth of dendrites during development correlates with a progressive decrease in the input resistance, whereas acute removal of dendrites during brain slicing leads to higher input resistances. Accordingly, recordings of excitatory postsynaptic potentials (EPSPs) evoked by afferent fiber stimulation show that EPSP decay is faster in P21-24 neurons with intact dendrites than in neurons without dendrites. This dendritic speeding of the EPSP reduces the decay time constant 5-fold, which will impact significantly synaptic current summation and the ability to fire high-frequency spike trains. These data suggest a novel role for dendrites in auditory brainstem neurons: the speeding of EPSPs for faster and more precise output signal transfer.\n\nSignificance StatementAuditory circuits that compute sound localization express different types of specialized synapses. Some are capable of fast, precise and sustained synaptic transmission. As the paradigm example, principal cells of the MNTB receive a single calyx-type nerve terminal on their soma and this large excitatory synapse produces fast and brief supra-threshold EPSPs that can trigger trains of high frequency spikes. However, these neurons also extend thin and long dendrites with unknown function. We examined the relationship between dendritic morphology, passive electrical properties and EPSP waveform. We found that more mature neurons with intact dendrites have lower input resistances and short EPSP waveforms, ideally suited for conveying precise timing information, whereas immature neurons with shorter dendrites and higher input resistance have longer lasting EPSPs.

neuroscience↗