Biology subjects

Hariharan, S.

Publications and source records attributed to Hariharan, S..

12 recordsLinked to original sources

bioRxiv2026

Bats use Dip Echolocation to overcome rhythmic noise

Active sensing systems are known to adapt the structure of sensory signals. Whether they can improve perception by controlling when sensory information is acquired remains unclear. We show that echolocating fruit bats exposed to rhythmic noise preferentially emit calls during recurring low-noise periods, a behaviour we term "dip echolocation". Dip echolocation occurred in laboratory and wild bats and represents an active-sensing analogue of dip listening in humans. A normative model showed that temporal positioning of calls emerges from a trade-off between sensory information and energetic cost, alongside concurrent adaptations of call structure. Pharmacological inactivation of the frontal auditory field disrupted precise temporal control, implicating a role for frontal cortical circuits in adaptive vocal timing. These findings identify adaptive vocal timing as an active-sensing strategy for overcoming acoustic interference.

animal behavior and cognition↗

bioRxiv2026

Ultrasound Activated Nanobubbles Induce Durable Systemic Antitumor Immunity

Clinical outcomes in aggressive breast cancer vary widely, in part because the tumor microenvironment is structured to exclude immune infiltration. Low antigen load, dysfunctional antigen-presenting cells, T cell exclusion and exhaustion, and a stiff extracellular matrix that physically restricts immune cell trafficking work together to form a suppressive barrier that current immunotherapies struggle to overcome. We addressed this barrier using ultrasound (US)-activated nanobubbles (NBs), a drug-free intervention based on perfluoropropane-filled nanoparticles. The size and deformable phospholipid shell enable NBs to achieve deep tumor penetration and a uniform distribution throughout the entire tumor. Upon ultrasound activation, NBs generate localized mechanical forces that restore extracellular matrix elasticity, disrupt tumor transport barriers, and drive HMGB1 release, re-engaging endogenous antitumor immunity without pharmacological agents. In a syngeneic triple-negative breast cancer model, US-NB treatment depleted immunosuppressive myeloid cells 3-fold within 3 hours, followed by a greater than 5-fold increase in the ratio of antigen-experienced to suppressive T cells at 48 hours. US-NB drives rapid infiltration of CD4+ and CD8+ T cells within 48 hours. US-NB treatment achieved an 85% cure rate in the D2A1 model; cured animals maintained durable systemic immune memory, rejecting both local and systemic tumor rechallenge. Consistent therapeutic benefit was observed in a luminal B-like mammary tumor model (E0771), supporting activity across breast cancer subtypes. These results establish US-NB mechanical immunomodulation as a drug-free therapeutic strategy capable of generating robust and durable antitumor immunity, acting through biophysical tissue properties rather than tumor-specific molecular targets. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=90 SRC="FIGDIR/small/714247v1_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@8f274eorg.highwire.dtl.DTLVardef@5da575org.highwire.dtl.DTLVardef@19defborg.highwire.dtl.DTLVardef@7e630e_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2024

Echo-locate: Cerebellar activity predicts vocalization in fruit bats

Echolocating bats exhibit remarkable auditory behaviors, enabled by adaptations within and outside their auditory system. Yet, research in echolocating bats has focused mostly on brain areas that belong to the classic ascending auditory pathway. This study provides direct evidence linking the cerebellum, an evolutionarily ancient and non-classic auditory structure, to vocalization and hearing. We report that in the fruit-eating bat Carollia perspicillata, external sounds can evoke cerebellar responses with latencies below 20 ms. Such fast responses are indicative of early inputs to the bat cerebellum. In vocalizing bats, distinct spike train patterns allow the prediction with over 85% accuracy of the sound they are about to produce, or have just produced, i.e., communication calls or echolocation pulses. Taken together, our findings provide evidence of specializations for vocalization and hearing in the cerebellum of an auditory specialist. TeaserThe cerebellum of fruit-eating bats responds to sounds and predicts future and past vocalizations

neuroscience↗

bioRxiv2024

Natural killer cells associate with epithelial cells in the pancreatic ductal adenocarcinoma tumor microenvironment

Pancreatic ductal adenocarcinoma (PDAC) exhibits dense fibrosis and immune exclusion. While fibrosis has been studied globally and at the region-of-interest level, its impact on stromal-ductal architecture and immune cell localization remains unknown. Here, we establish cancer-associated fibroblast (CAF)-stratified ductal spatial architecture as a fundamental determinant of immune exclusion in PDAC. Focusing on malignant PDAC epithelial ductal regions, the critical interface where immune cells must access tumor epithelium, we demonstrate that periductal fibroblast organization dictates leukocyte proximity. Through integrative analysis of treatment-naive patient samples from three independent cohorts - including imaging mass cytometry, multiplex immunohistochemistry, and single-cell RNA sequencing - we uncovered that activated, pro-inflammatory leukocytes preferentially localized near malignant ducts in regions with low fibroblast density. Stratifying epithelial-ductal regions by CAF abundance revealed a graded constraint: increasing fibroblast content corresponded to reduced leukocyte-epithelial proximity and elevated collagen I deposition. Despite their exclusion in high-CAF ducts, leukocytes in low-CAF ducts retained functional competence. Mechanistically, ligand-receptor inference implicated collagen-CD44 signaling as an adhesion axis anchoring immune cells within fibroblast-rich zones, with CD44 blockade enhancing natural killer cell invasion and motility in fibrotic spheroid models. Thus, by establishing ductal regions as critical spatial units of immune exclusion, these findings provide a framework for dissecting stromal-immune interactions and reveal targetable "stromal checkpoints" that can be leveraged to overcome CAF-driven barriers to leukocyte motility and infiltration in PDAC. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/593868v3_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@17df874org.highwire.dtl.DTLVardef@142c809org.highwire.dtl.DTLVardef@15af4a7org.highwire.dtl.DTLVardef@74183a_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2023

Development and validation of a high throughput screening platform to enable target identification in skeletal muscle cells from Duchenne Muscular Dystrophy (DMD) patients

Duchenne muscular dystrophy (DMD) is a progressive and fatal muscle degenerating disease caused by dystrophin deficiency. Effective methods for drug discovery for the treatment of DMD requires systems to be physiologically relevant, scalable, and effective. To this end, the Myoscreen platform offers a scalable and physiologically relevant system for generating and characterizing patient-derived myotubes. Morphological profiling is a powerful technique involving the simultaneous measurement of hundreds of morphological parameters from fluorescence microscopy images and using machine learning to predict cellular activity. Here, we describe combining the Myoscreen platform and high dimensional morphological profiling to accurately predict a phenotype associated with the lack of Dystrophin expression in patient derived myotubes. Using this methodology, we evaluated a series of Dystrophin-associated protein complex (DAPC) candidates and identified that the combination of Utrophin and - Sarcoglycan yielded highest morphological differences between DMD and non-DMD donors. Finally, we validated this methodology by knocking down Dystrophin expression in non-DMD cells as well as introducing Dystrophin expression in DMD cells. Knocking down Dystrophin in non- DMD cells shifted their morphological profile to one that is similar to DMD cells while introducing Dystrophin in DMD cells shifted their morphological profile towards non-DMD cells. In conclusion, we have developed a platform that accurately predicts the DMD disease phenotype in a disease relevant cell type. Ultimately this platform may have wide applications in the drug development process include identification of disease modifier genes, screening of novel therapeutic moieties, and as a potency assay for future therapeutics.

cell biology↗

bioRxiv2023

Interplay between ATRX and IDH1 mutations governs innate immune responses in diffuse gliomas

Stimulating the innate immune system has been explored as a therapeutic option for the treatment of gliomas. Inactivating mutations in ATRX, defining molecular alterations in IDH-mutant astrocytomas, have been implicated in dysfunctional immune signaling. However, little is known about the interplay between ATRX loss and IDH mutation on innate immunity. To explore this, we generated ATRX knockout glioma models in the presence and absence of the IDH1R132H mutation. ATRX-deficient glioma cells were sensitive to dsRNA-based innate immune agonism and exhibited impaired lethality and increased T-cell infiltration in vivo. However, the presence of IDH1R132Hdampened baseline expression of key innate immune genes and cytokines in a manner restored by genetic and pharmacological IDH1R132H inhibition. IDH1R132H co-expression did not interfere with the ATRX KO-mediated sensitivity to dsRNA. Thus, ATRX loss primes cells for recognition of dsRNA, while IDH1R132H reversibly masks this priming. This work reveals innate immunity as a therapeutic vulnerability of astrocytoma.

cancer biology↗

bioRxiv2023

JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations

Image-based profiling has emerged as a powerful technology for various steps in basic biological and pharmaceutical discovery, but the community has lacked a large, public reference set of data from chemical and genetic perturbations. Here we present data generated by the Joint Undertaking for Morphological Profiling (JUMP)-Cell Painting Consortium, a collaboration between 10 pharmaceutical companies, six supporting technology companies, and two non-profit partners. When completed, the dataset will contain images and profiles from the Cell Painting assay for over 116,750 unique compounds, over-expression of 12,602 genes, and knockout of 7,975 genes using CRISPR-Cas9, all in human osteosarcoma cells (U2OS). The dataset is estimated to be 115 TB in size and capturing 1.6 billion cells and their single-cell profiles. File quality control and upload is underway and will be completed over the coming months at the Cell Painting Gallery: https://registry.opendata.aws/cellpainting-gallery. A portal to visualize a subset of the data is available at https://phenaid.ardigen.com/jumpcpexplorer/.

bioinformatics↗

bioRxiv2022

Deep Representation Learning Determines Drug Mechanism of Action from Cell Painting Images

Fluorescent-based microscopy screens carry a broad range of phenotypic information about how compounds affect cellular biology. From changes in cellular morphology observed in these screens, one key area of medicinal interest is determining a compounds mechanism of action. However, much of this phenotypic information is subtle and difficult to quantify. Hence, creating quantitative embeddings that can measure cellular response to compound perturbation has been a key area of research. Here we present a deep learning enabled encoder called MOAProfiler that captures phenotypic features for determining mechanism of action from Cell Painting images. We compared our method with both a traditional computer vision means of feature encoding via CellProfiler and a deep learning encoder called DeepProfiler. The results, on two independent and biologically different datasets, indicated that MOAProfiler encoded MOA-specific features that allowed for more accurate clustering and classification of compounds over hundreds of different MOAs.

bioinformatics↗

bioRxiv2022

Optimizing the Cell Painting assay for image-based profiling

In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations based on their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells, and predicting assay outcomes using machine learning, among many others. Here we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, Golgi apparatus, plasma membrane, endoplasmic reticulum, and mitochondria. The original protocol was updated in 2016 based on several years experience running it at two sites, after optimizing it by visual stain quality. Here we describe the work of the Joint Undertaking for Morphological Profiling (JUMP) Cell Painting Consortium, aiming to improve upon the assay via quantitative optimization, based on the measured ability of the assay to detect morphological phenotypes and group similar perturbations together. We find that the assay gives very robust outputs despite a variety of changes to the protocol and that two vendors dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1-2 weeks for a typically sized batch of 20 or fewer plates; feature extraction and data analysis take an additional 1-2 weeks. Key references using this protocolVirtual screening for small-molecule pathway regulators by image-profile matching (https://doi.org/10.1016/j.cels.2022.08.003) - recent work examining the ability to use collected Cell Painting profiles to screen for regulators of a number of diverse biological pathways. JUMP Cell Painting dataset: images and profiles from two billion cells perturbed by 140,000 chemical and genetic perturbations (DOI) - the description of the main JUMP master public data set, using this protocol in the production of >200 TB of image data and >200 TB of measured profiles. Key data used in this protocolCell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes (https://doi.org/10.1038/nprot.2016.105) - this paper provides the first step-by-step Cell Painting protocol ever released.

bioinformatics↗

bioRxiv2019

Assessing the role of long-noncoding RNA in nucleus accumbens in subjects with alcohol dependence

Recently, long noncoding RNA (lncRNA) were implicated in the etiology of alcohol dependence (AD). As lncRNA provide another layer of complexity to the transcriptome, assessing their expression in the brain is the first critical step towards understanding lncRNA functions in AD. To that end, we profiled the expression of lncRNA and protein coding genes (PCG) in nucleus accumbens (NAc) from 41 subjects with AD and 41 controls. At false discovery rate (FDR) of 5%, we identified 69 and 309 differentially expressed lncRNA and PCG genes, respectively. Using co-expression network analyses, we identified three lncRNA and five PCG modules significantly correlated with AD at Bonferroni adj. p[≤]0.05. To better understand lncRNA functions, we integrated the lncRNA and PCG hubs from the significant AD modules; at FDR of 5%, we identified 3 151 positive and 2 255 negative correlations supporting the functional role of lncRNA in the development of AD. Gene enrichment analysis revealed that PCG significantly correlated with lncRNA are, among others, enriched for neuronal and immune related processes. To highlight the mechanisms, by which genetic variants contribute to AD, we integrated lncRNA and PCG hubs with genome-wide SNP data. At FDR[≤]0.3, we identified 276 expression quantitative trait loci (eQTL), affecting the expression of 20 and 256 lncRNA and PCG hubs, respectively. Our study is the first to profile lncRNA expression in nucleus accumbens in a large postmortem alcohol brain sample and our results may provide novel insights into the regulation of the brain transcriptome across disease.

neuroscience↗

bioRxiv2019

Improving drug discovery using image-based multiparametric analysis of epigenetic landscape.

With the advent of automatic cell imaging and machine learning, high-content phenotypic screening has become the approach of choice for drug discovery due to its ability to extract drug specific multilayered data and compare it to known profiles. In the field of epigenetics such screening approaches has suffered from the lack of tools sensitive to selective epigenetic perturbations. Here we describe a novel approach Microscopic Imaging of Epigenetic Landscapes (MIEL) that captures patterns of nuclear staining of epigenetic marks (e.g. acetylated and methylated histones) and employs machine learning to accurately distinguish between such patterns (1). We demonstrated that MIEL has superior resolution compared to conventional intensity thresholding techniques and enables efficient detection of epigenetically active compounds, function-based classification, flagging possible off-target effects and even predict novel drug function. We validated MIEL platform across multiple cells lines and using dose-response curves to insure the robustness of this approach for the high content high throughput drug discovery.

cell biology↗

bioRxiv2018

Multiparametric Signature of Glioblastoma Differentiation Revealed by Imaging of Cellular Epigenetic Landscapes

The resistance of Glioblastoma (GBM) to conventional cytotoxic drugs has prompted novel therapeutic strategies, including differentiating tumor propagating cells (TPCs) into less tumorigenic cells using small molecule inducers of TPC differentiation. However, high-throughput screening for such molecules is hampered by the lack of robust markers of GBM differentiation. To obtain a signature of differentiated TPCs, we developed \"Microscopic Imaging of Epigenetic Landscapes\" (MIEL), which captures patterns of nuclear staining for epigenetic marks to derive feature-fingerprints of individual cells. We confirmed MIELs ability to accurately distinguish multiple cell fates and identified a multiparametric epigenetic signature of differentiated TPCs. Critically, we validated epigenetic imaging-based signature using global gene expression thus providing the proof of principle for the MIELs ability to select and prioritize small molecules, which induce TPC differentiation.

cancer biology↗