Biology subjects

Madugula, S. S.

Publications and source records attributed to Madugula, S. S..

16 recordsLinked to original sources

bioRxiv2026

TileBac: A Benchmark CryoEM Dataset of Bacteria in Ultralow-Dose Montage Tiles

Current segmentation models are capable of routine identification of biological features in noisy cryogenic electron microscopy (cryoEM) images. However, there are still challenges with complete segmentation of high boundary, thin objects such as bacterial cell envelopes and flagella. Moreover, ultralow-dose cryoEM images pose as an additional challenge to boundary distinctions between the object and background. Here, we present TileBac, a benchmark dataset of ultralow-dose montage tiles of Pantoea sp. YR343 to segment bacterial inner and outer membranes for evaluation of model effectiveness. We show that foundation models outperform convolutional neural networks at continuous bacterial cell envelope segmentation despite having lower performance metrics. We release the TileBac benchmark dataset on Hugging Face for further insights into model architecture development.

microbiology↗

bioRxiv2026

Top Model Decision Tree: Selecting Segmentation Models for Reliable Quantitative Analysis in Low- and Ultralow-Dose CryoEM

Deep learning neural networks provide a powerful approach for segmenting low-contrast cryogenic electron microscopy (cryoEM) images. However, model performance can vary significantly across imaging conditions and may hinder downstream quantitative analyses. Here, we present a structured evaluation workflow to systematically screen segmentation models based on performance, inference speed, robustness across imaging conditions, and reliability of downstream quantitative measurements. Using the Bacterial Cell Envelope Thickness Tool (BCET) as a test case, we evaluate multiple architectures (YOLOv11, YOLO26, U-Net, Detectron2, and SAM3) under low-dose and ultralow-dose cryoEM conditions. While several models achieve high metrics, model choice strongly influences downstream measurements of envelope thickness. Models optimized for high F1-scores may produce unreliable segmentation masks from object crowding, interpolation artifacts or imaging conditions. Our results reveal distinct trade-offs between performance, speed, and robustness amongst models. YOLOv11 provides the highest fidelity membrane segmentation for quantitative measurements and the Meta-based model SAM3 offers improved robustness under ultralow-dose conditions with competitive inference performance. This work provides practical guidance for model selection in cryoEM workflows, emphasizing that optimal choice depends on experimental priorities and downstream analysis requirements rather than metrics alone. These findings are broadly relevant to cryoEM workflows as AI-based analysis expands beyond the biological sciences. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/730486v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@f29df4org.highwire.dtl.DTLVardef@601d6eorg.highwire.dtl.DTLVardef@2c5023org.highwire.dtl.DTLVardef@1413f76_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2026

Facility-Scale Workflows for Data Acquisition, Standardization, Machine Learning Analysis, and Reproducible Science

Scientific user facilities routinely generate large-scale microscopy datasets across diverse instruments and vendors, differing substantially in file formats, dimensionality, and resolution. Beyond these inconsistencies, datasets are frequently fragmented living across isolated instruments and constrained by security policies and uneven metadata practices. Consequently, tracking, standardizing, processing, and visualizing these datasets in a manner compatible with modern machine learning and autonomous experimentation workflows remains a major challenge. While existing initiatives address data archiving, standardization, or analysis individually, few provide integrated solutions that bridge instrument-level acquisition and scalable ML workflows within heterogeneous, security-constrained user facilities. Here, we establish a deployable, facility-scale infrastructure that bridges instrument-level data generation with cloud-based ML analytics while remaining compliant with institutional network constraints. Our framework integrates on-premises cloud computing, the in-house Pycroscopy ecosystem, and an open-source metadata management platform to transform heterogeneous microscopy datasets into standardized, ML-ready representations. We demonstrate this approach across distinct microscopy modalities through end-to-end workflows encompassing metadata capture, format harmonization, automated database ingestion, segmentation-based ML inference, and interactive visualization. By structurally separating acquisition from cloud-based analysis services, the framework enables scalable model deployment and iterative refinement without direct connectivity to instrument computers. Together, this work provides a reproducible blueprint for facility-scale data and AI infrastructure, enabling ML-ready analytics, metadata traceability, and future autonomous experimentation workflows in microscopy-driven research.

microbiology↗

bioRxiv2026

Yin Yang Interactomes Drive the Metabolic Dysfunction-Associated Liver Disease Continuum to Hepatocellular Carcinoma

The risk for hepatocellular carcinoma (HCC) arising from metabolic dysfunction- associated steatotic liver disease (MASLD)-related cirrhosis exceeds the risk for cancer developing in the kidney, heart, or lung as a consequence of end-stage disease in those organs. Both experimental and clinical studies support the existence of circulating biomarkers whose elevated expression is associated with, and supports the diagnosis of, HCC. We posit the existence of oncogenic HCC biomarkers under regulation by tumor-suppressive microRNAs (miRs), and that reduced tumor-suppressive miR expression leads to increased oncogenic biomarker expression. By interrogating the MASLD-HCC miR interactome and miRs known to regulate HCC biomarkers, we identified hsa-miR-577, hsa-miR-9500, hsa-miR-101-3p, miR-206, hsa- miR-219a-1-3p, and hsa-miR-613 as tumor-suppressive miRs whose expression is reduced across multiple cancers. Their target genes, osteopontin, Golgi protein-73, and Dickkopf-related protein 1, exhibit increased expression in HCC and display oncogenic activity. Collectively, dysregulated expression of these miRs and their downstream gene products may contribute to progression along the MASLD continuum toward HCC. HighlightsWe identified tumor suppressive miRs, hsa-miR-577, hsa-miR-9500, hsa-miR-101-3p, miR-206, hsa-miR-219a-1-3p, and hsa-miR-613 with potentially reduced expression levels in HCC. Their gene products, osteopontin, Golgi protein-73, and Dickkopf-related protein 1, exhibit oncogenic properties and increased expression levels in HCC. These miR-gene product interactions may drive the MASLD continuum to HCC. Mechanistic insights should illuminate therapies. Identification of therapeutics that interrupt these interactions may represent a therapeutic pillar for patients with MASLD. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/698028v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@1892140org.highwire.dtl.DTLVardef@1e61677org.highwire.dtl.DTLVardef@cd0488org.highwire.dtl.DTLVardef@1e48d8a_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigureC_FLOATNO MASLD-related HCC. The intersection set between the MASLD-HCC miR interactome and the set of miRs regulating biomarkers diagnostic for HCC was interrogated and tumor suppressive miRs regulating HCC biomarkers with oncogenic activity were identified. Increased risk for HCC in patients with MASLD and MASLD-cirrhosis may arise from interactions between these dysregulated miRs and their downstream gene products. C_FIG PrologueMetabolic Dysfunction-Associated Liver Disease (MASLD), the leading cause of hepatocellular carcinoma (HCC) in Sweden, is now taking epidemic proportions in South Asia. In India, there is a relatively high prevalence of pre-diabetes, diabetes and insulin resistance; body mass indices > 35 are being observed. Each of these and/or their combinations represent risk factors for MASLD. Nevertheless, both patients and providers are largely oblivious to this disease, both from a lack of general awareness and the asymptomatic nature of MASLD, at least early within its continuum. Diagnosis is often made late, and by exclusion, when presentation may include fatigue, drowsiness, mild encephalopathy (MASLD-pituitary feedback loop), prominent hepatomegaly and asciites, and with liver-specific test demonstrating stiffness, scarring and even HCC. Societal awareness and effective management of this disease are urgent needs. A strategy akin to the 4 pillars for treatment of type 2 diabetes and chronic kidney disease (CKD) needs to be implemented for treatment of patients with MASLD. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=30 SRC="FIGDIR/small/698028v1_ufig2.gif" ALT="Figure 2"> View larger version (11K): org.highwire.dtl.DTLVardef@89a77org.highwire.dtl.DTLVardef@d50c15org.highwire.dtl.DTLVardef@10c6964org.highwire.dtl.DTLVardef@1db2cfa_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigureC_FLOATNO Four-pillar Strategy for MASLD. A strategy akin to the one in place for treatment of type 2 diabetes and CKD needs to be implemented for treatment of patients with MASLD. Aspects of this strategy, viz. SGLT2i and GLP-1{+/-}GIP agonists, are already approved in MASLD. C_FIG

bioinformatics↗

bioRxiv2025

Leveraging CryoEM and AI-Driven Morphological Feature Analysis for Insights on Bacterial Structures

Bacteria adapt by undergoing dynamic structural changes in response to environmental cues, which are often indicative of deeper phenotypic shifts in physiology and behavior. Understanding these changes across length scales is crucial for elucidating bacterial lifecycles, informing antifouling surface design, enhancing pathogen detection, and advancing renewable energy applications. Cryogenic electron microscopy (cryoEM) enables high-resolution imaging of bacterial structures in hydrated, biologically relevant conditions. However, current quantitative analysis strategies that extract structural information from bacterial samples remain labor-intensive. This work presents an AI-driven segmentation workflow tailored to low-dose cryoEM datasets to rapidly analyze bacterial ultrastructural features from Pantoea sp. YR343, a Gram-negative bacterium isolated from the rhizosphere of Populus deltoides that forms robust biofilms along plant roots. YOLOv11 image segmentation quantifies inner and outer membrane thickness and flagella length, enabling automated analysis of bacterial ultrastructure. The workflow reliably distinguishes membranes from carbon edges of the TEM grid and contaminant crystalline ice while matching manual measurements and increasing throughput. Flagella detection routines additionally quantify nearest-neighbor proximity between bacterial envelopes and flagella. A field-of-view module further detects bacteria at low magnification for rapid screening. Together, these tools provide a scalable and automated framework for high-throughput, quantitative analysis of bacterial ultrastructure in cryoEM data. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=39 SRC="FIGDIR/small/692658v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@18eac2org.highwire.dtl.DTLVardef@1dc67a1org.highwire.dtl.DTLVardef@1179499org.highwire.dtl.DTLVardef@11d08f8_HPS_FORMAT_FIGEXP M_FIG AI-based tools enable rapid characterization of bacterial ultrastructure in low-dose cryoEM. The envelope thickness tool quantifies membrane thickness and anisotropy. The flagella module analyzes filament morphology and detects cell-flagella contacts. The field-of-view (FOV) module C_FIG

microbiology↗

bioRxiv2025

Structure-Based Classification of CRISPR/Cas9 Proteins: A Machine Learning Approach to Elucidating Cas9 Allostery

The CRISPR/Cas9 system is a powerful gene-editing tool. Its specificity and stability rely on complex allosteric regulation. Understanding these allosteric regulations is essential for developing high-fidelity Cas9 variants with reduced off-target effects. Here, we introduce a novel structure-based machine learning (ML) approach to systematically identify long-range allosteric networks in Cas9. Our ML model was trained using all available Cas9 structures, ensuring a comprehensive representation of Cas9s structural landscape. We then applied this model to Streptococcus pyogenes Cas9 (SpCas9) to demonstrate the feature selection process. Using C-C inter-residue distances, we mapped key allosteric networks and refined them through a two-stage SHAP feature selection (FS) strategy, reducing a vast feature space to 28 critical Lysine-Arginine (Lys-Arg) residue pairs that mediate SpCas9 interdomain communication, stability, and specificity. These Lys-Arg pairs initially shared a 46.5 [A] inter-residue distance, but molecular dynamics simulations revealed distinct stabilization behaviors, indicating a hierarchical allosteric network. Further mutational analysis of R78A-K855A (M1) and R765A-K1246A (M2) identified an "electrostatic valley," a stabilizing network where positively charged residues interact with negatively charged DNA to maintain SpCas9s structural integrity. Disrupting this valley through direct (M2) or allosteric (M1) mutations destabilized SpCas9s DNA-bound conformation, leading to distinct pathways for improving SpCas9 specificity. This study provides a new framework for understanding allostery in Cas9, integrating ML-driven structural analysis with MD simulations. By identifying key allosteric residues and introducing the electrostatic valley as a central concept, we offer a rational strategy for engineering high-fidelity Cas9 variants. Beyond Cas9, our approach can be applied to uncover allosteric hotspots in other enzyme regulation and rational protein design.

pharmacology and toxicology↗

bioRxiv2025

CasGen: A Regularized Generative Model for CRISPR Cas Protein Design with Classification and Margin-Based Optimization

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated proteins (Cas) systems have revolutionized genome editing by providing high precision and versatility. However, most genome editing applications rely on a limited number of well-characterized Cas9 and Cas12 variants, constraining the potential for broader genome engineering applications. In this study, we extensively explored Cas9 and Cas12 proteins and developed CasGen, a novel transformer-based deep generative model with margin-based latent space regularization to enhance the quality of newly generative Cas9 and Cas12 proteins. Specifically, CasGen employs a strategies that combine classification to filter out non-Cas sequences, Bayesian optimization of the latent space to guide functionally relevant designs, and thorough structural validation using AlphaFold-based analyses to ensure robust protein generation. We collected a comprehensive dataset with 3,021 Cas9, 597 Cas12, and 597 Non-Cas protein sequences from reputable biological databases such as InterPro and PDB. To validate the generated proteins, we performed sequence alignment using the BLAST tool to ensure novelty and filter out highly similar sequences to existing Cas proteins. Structural prediction using AlphaFold2 and AlphaFold3 confirmed that the generated proteins exhibit high structural similarity to known Cas9 and Cas12 variants, with TM-scores between 0.70 and 0.85 and root-mean-square deviation (RMSD) values below 2.00 [A]. Sequence identity analysis further demonstrated that the generated Cas9 orthologs exhibited 28% to 55% identity with known variants, while Cas12a variants show up to 48% identity. Our results demonstrate that the proposed Cas generative model has significant potential to expand the genome editing toolkit by designing diverse Cas proteins that retain functional integrity. The developed deep generative approach offers a promising avenue for synthetic biology and therapeutic applications, enableling the development of more precise and versatile Cas-based genome editing tools.

genomics↗

bioRxiv2024

Transformer-Based Deep Learning Model with Latent Space Regularization for CRISPR-Cas Protein Sequence Classification

The discovery of the CRISPR-Cas system has significantly advanced genome editing, offering vast applications in medical treatments and life sciences research. Despite their immense potential, the existing CRISPR-Cas proteins still face challenges concerning size, delivery efficiency, and cleavage specificity. Addressing these challenges necessitates a deeper understanding of CRISPR-Cas proteins to enhance the design and discovery of novel Cas proteins for precision gene editing. In this study, we performed extensive deep-learning research on CRISPR-Cas proteins, aiming to develop a classification model capable of distinguishing CAS from non-CAS proteins, as well as discriminating sub-categories of CAS proteins, specifically CAS9 and CAS12. We developed two types of deep learning models: 1) a transformer encoder-based classification model, trained from scratch; and 2) a large protein language model fine-tuned on ProtBert, pre-trained on more than 200 million proteins. To boost learning efficiency for the model trained from scratch, we introduced a novel margin-based loss function to maximize inter-class separability and intra-class compactness in protein sequence embedding latent space of a transformer encoder. The experimental results show that the Fine-Tuned ProtBert-based (FTPB) classification model achieved accuracies of 99.06%, 94.42%, 96.80%, 97.57% for CAS9 vs. Non-CAS, CAS12 vs. Non-CAS, CAS9 vs. CAS12, and multi-class classification of CAS9 vs. CAS12 vs. Non-CAS, respectively. The Latent Space Regularized Max-Margin Transformer (LSRMT) model achieved classification accuracies of 99.81%, 99.81%, 99.06%, 99.27% for the same tasks, respectively. These results demonstrate the effectiveness of the proposed Max-Margin-based latent space regularization in enhancing model robustness and generalization capabilities. Remarkably, the LSRMT model, even when trained on a significantly smaller dataset, outperformed the fine-tuned state-of-the-art large protein model. The high classification accuracies achieved by the LSRMT model demonstrate its proficiency in identifying discriminative features of CAS proteins, marking a significant step towards advancing our understanding of CAS protein structures in future research endeavors.

bioinformatics↗

bioRxiv2024

Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum

The recent development of CRISPR-Cas technology holds promise to correct gene-level defects for genetic diseases. The key element of the CRISPR-Cas system is the Cas protein, a nuclease that can edit the gene of interest assisted by guide RNA. However, these Cas proteins suffer from inherent limitations like large size, low cleavage efficiency, and off-target effects, hindering their widespread application as a gene editing tool. Therefore, there is a need to identify novel Cas proteins with improved editing properties, for which it is necessary to understand the underlying features governing the Cas families. In the current study, we aim to elucidate the unique protein attributes associated with Cas9 and Cas12 families and identify the features that distinguish each family from the other. Here, we built Random Forest (RF) binary classifiers to distinguish Cas12 and Cas9 proteins from non-Cas proteins, respectively, using the complete protein feature spectrum (13,495 features) encoding various physiochemical, topological, constitutional, and coevolutionary information of Cas proteins. Furthermore, we built multiclass RF classifiers differentiating Cas9, Cas12, and Non-Cas proteins. All the models were evaluated rigorously on the test and independent datasets. The Cas12 and Cas9 binary models achieved a high overall accuracy of 95% and 97% on their respective independent datasets, while the multiclass classifier achieved a high F1 score of 0.97. We observed that Quasi-sequence-order descriptors like Schneider-lag descriptors and Composition descriptors like charge, volume, and polarizability are essential for the Cas12 family. More interestingly, we discovered that Amino Acid Composition descriptors, especially the Tripeptide Composition (TPC) descriptors, are important for the Cas9 family. Four of the identified important descriptors of Cas9 classification are tripeptides PWN, PYY, HHA, and DHI, which are seen to be conserved across all the Cas9 proteins and were located within different catalytically important domains of the Cas9 protein structure. Among these four tripeptides, tripeptides DHI and HHA are well-known to be involved in the DNA cleavage activity of the Cas9 protein. We therefore propose the the other two tripeptides, PWN and PYY, may also be essential for the Cas9 family. Our identified important descriptors enhanced the understanding of the catalytic mechanisms of Cas9 and Cas12 proteins and provide valuable insights into design of novel Cas systems to achieve enhanced gene-editing properties.

bioinformatics↗

bioRxiv2023

Decomposition of retinal ganglion cell electrical images for cell type and functional inference

ObjectiveIdentifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and for the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision. ApproachThe electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose electrical image into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments. ResultsThe decomposition was evaluated using large-scale multi-electrode recordings from the macaque retina. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells) substantially more accurately than previous approaches. SignificanceThese findings contribute to more accurate inference of RGC types and their original light responses based purely on their electrical features, with potential implications for vision restoration technology.

neuroscience↗

bioRxiv2022

NRPreTo: A Machine Learning Based Nuclear Receptor and Subfamily Prediction Tool

The Nuclear Receptor (NR) superfamily includes phylogenetically related ligand-activated proteins, which play a key role in various cellular activities. NR proteins are subdivided into seven subfamilies based on their function, mechanism, and nature of the interacting ligand. Developing robust tools to identify NR could give insights into their functional relationships and involvement in disease pathways. Existing NR prediction tools only use a few types of sequence-based features and are tested on relatively similar independent datasets; thus, they may suffer from overfitting when extended to new genera of sequences. To address this problem, we developed Nuclear Receptor Prediction Tool (NRPreTo); a two-level NR prediction tool with a unique training approach where in addition to the sequence-based features used by existing NR prediction tools, six additional feature groups depicting various physiochemical, structural and evolutionary features of proteins were utilized. The first level of NRPreTo allows for the successful prediction of a query protein as NR or non-NR, and further subclassifies the protein into one of the seven NR subfamilies in the second level. We developed Random Forest classifiers to test on benchmark datasets, as well as the entire human protein datasets from RefSeq and Human Protein Reference Database (HPRD). We observed that using additional feature groups improved performance. We also observed that NRPreTo achieved high performance on the external datasets and predicted 59 novel NRs in the human proteome. The source code of NRPreTo is publicly available at https://github.com/bozdaglab/NRPreTo.

systems biology↗

bioRxiv2022

Inferring light responses of primate retinal ganglion cells using intrinsic electrical signatures

Reproducing high-acuity vision with an epiretinal implant will likely require inferring the natural light responses of diverse RGC types in the implanted retina, without measuring them directly. Here we demonstrate an approach that exploits intrinsic electrical features of primate RGCs. First, ON-parasol and OFF-parasol RGCs were identified with 95% accuracy using electrical features. Then, the somatic electrical footprint, predicted cell type, and average linear-nonlinear-Poisson model parameters of each cell type were used to infer a light response model for each cell. Across five retinas, these models achieved an average correlation with measured firing rates of 0.49 for white noise visual stimuli and 0.50 for natural scenes stimuli, compared to 0.65 and 0.58 respectively for models fitted to recorded light responses, an upper bound. This finding, and linear decoding of images from predicted RGC activity, suggested that the inference approach may be useful for high-fidelity sight restoration.

neuroscience↗

bioRxiv2022

High-fidelity restoration of visual signals by electrical stimulation in the central primate retina

Electrical stimulation of retinal ganglion cells (RGCs) with electronic implants provides rudimentary artificial vision to people blinded by retinal degeneration. However, current devices stimulate indiscriminately and therefore cannot reproduce the intricate neural code of the retina. Recent work has demonstrated more precise activation of RGCs using focal electrical stimulation with multi-electrode arrays in the peripheral macaque retina, but it is unclear how effective this can be in the central retina, which is required for high-resolution vision. This work probes the neural code and effectiveness of focal epiretinal stimulation in the central macaque retina, using large-scale electrical recording and stimulation ex vivo. The functional organization, light response properties, and electrical properties of the major RGC types in the central retina were mostly similar to the peripheral retina, with some notable differences in density, kinetics, linearity, spiking statistics and correlations. The major RGC types could be distinguished by their intrinsic electrical properties. Electrical stimulation targeting parasol cells revealed similar activation thresholds and reduced axon bundle activation in the central retina, but lower stimulation selectivity. Quantitative evaluation of the potential for image reconstruction from electrically-evoked parasol cell signals revealed higher overall expected image quality in the central retina. An exploration of inadvertent midget cell activation suggested that it could contribute high spatial frequency noise to the visual signal carried by parasol cells. These results support the possibility of reproducing high-acuity visual signals in the central retina with an epiretinal implant. Significance StatementArtificial restoration of vision with retinal implants is a major treatment for blindness. However, present-day implants do not provide high-resolution visual perception, in part because they do not reproduce the natural neural code of the retina. Here we demonstrate the level of visual signal reproduction that is possible with a future implant by examining how accurately responses to electrical stimulation of parasol retinal ganglion cells (RGCs) can convey visual signals. Although the precision of electrical stimulation in the central retina was diminished relative to the peripheral retina, the quality of expected visual signal reconstruction in parasol cells was greater. These findings suggest that visual signals could be restored with high fidelity in the central retina using a future retinal implant.

neuroscience↗

bioRxiv2021

Efficient Inference of Sensitivity to Electrical Stimulation from Intrinsic Electrical Properties of Primate Retinal Ganglion Cells

High-fidelity electronic implants can in principle restore the function of neural circuits by precisely activating neurons via extracellular stimulation. However, direct characterization of the individual electrical responses of a large population of target neurons, in order to precisely control their activity, is often difficult or impossible. A potential solution is to leverage biophysical principles to infer sensitivity to electrical stimulation from features of spontaneous electrical activity, which can be recorded relatively easily. Here, this approach is developed and its potential value for vision restoration is tested quantitatively using large-scale high-density stimulation and recording from primate retinal ganglion cells (RGCs) ex vivo. Electrodes recording larger spikes from a given cell exhibited lower stimulation thresholds, with distinct trends for somas and axons, across cell types, retinas, and eccentricities. Thresholds for somatic stimulation increased with distance from the axon initial segment. The dependence of spike probability on injected current was inversely related to threshold, and was substantially steeper for axonal than somatic compartments, which could be identified by recorded electrical signatures. Dendritic stimulation was largely ineffective for eliciting spikes. These findings were quantitatively reproduced with biophysical simulations, and confirmed in tests on human RGCs. The inference of stimulation sensitivity from recorded electrical features was tested in simulated visual reconstruction, and revealed that the approach could significantly improve the function of future high-fidelity retinal implants.

neuroscience↗

bioRxiv2021

Spatially Patterned Bi-electrode Epiretinal Stimulation for Axon Avoidance at Cellular Resolution

ObjectiveEpiretinal prostheses are designed to restore vision to people blinded by photoreceptor degenerative diseases by stimulating surviving retinal ganglion cells (RGCs), which carry visual signals to the brain. However, inadvertent stimulation of RGCs at their axons can result in non-focal visual percepts, limiting the quality of artificial vision. Theoretical work has suggested that axon activation can be avoided with current stimulation designed to minimize the second spatial derivative of the induced extracellular voltage along the axon. However, this approach has not been verified experimentally at the resolution of single cells. Approach. In this work, a custom multi-electrode array (512 electrodes, 10 m diameter, 60 m pitch) was used to stimulate and record RGCs in macaque retina ex vivo at single-cell, single-spike resolution. RGC activation thresholds resulting from bi-electrode stimulation, which consisted of bipolar currents simultaneously delivered through two electrodes straddling an axon, were compared to activation thresholds from traditional single-electrode stimulation. ResultsOn average, across three retinal preparations, the bi-electrode stimulation strategy reduced somatic activation thresholds ([~]21%) while increasing axonal activation thresholds ([~]14%), thus favoring selective somatic activation. Furthermore, individual examples revealed rescued selective activation of somas that was not possible with any individual electrode. Significance. This work suggests that a bi-electrode epiretinal stimulation strategy can reduce inadvertent axonal activation at cellular resolution, for high-fidelity artificial vision. Novelty & SignificanceThe effectiveness of bi-electrode stimulation for enhancing the electrical activation of retinal neurons was tested using high-density multi-electrode recording and stimulation in isolated macaque retina. The results suggest that spatially patterned bi-electrode stimuli reduce unwanted axon activation and thus improve the selectivity of stimulation at cellular resolution. Similar patterns could be implemented in a future high-resolution prosthesis to permit a more faithful replication of normal retinal activity, at the resolution of single-cells and single-spikes, for the treatment of incurable blindness.

bioengineering↗

bioRxiv2018

Epiretinal stimulation with local return enhances selectivity at cellular resolution

ObjectiveEpiretinal prostheses are designed to restore vision in people blinded by photoreceptor degenerative diseases, by directly activating retinal ganglion cells (RGCs) using an electrode array implanted on the retina. In present-day clinical devices, current spread from the stimulating electrode to a distant return electrode often results in the activation of many cells, potentially limiting the quality of artificial vision. In the laboratory, epiretinal activation of RGCs with cellular resolution has been demonstrated with small electrodes, but distant returns may still cause undesirable current spread. Here, the ability of local return stimulation to improve the selective activation of RGCs at cellular resolution was evaluated.\n\nApproachA custom multi-electrode array (512 electrodes, 10 m diameter, 60 m pitch) was used to simultaneously stimulate and record from RGCs in isolated primate retina. Stimulation near the RGC soma with a single electrode and a distant return was compared to stimulation in which the return was provided by six neighboring electrodes.\n\nMain resultsLocal return stimulation enhanced the capability to activate cells near the central electrode (<30 m) while avoiding cells farther away (>30 m). This resulted in an improved ability to selectively activate ON and OFF cells, including cells encoding immediately adjacent regions in the visual field.\n\nSignificanceThese results suggest that a device that restricts the electric field through local returns could optimize activation of neurons at cellular resolution, improving the quality of artificial vision.\n\nNovelty & SignificanceThe effectiveness of local return stimulation for enhancing the electrical activation of retinal neurons was tested using high-density multi-electrode recording and stimulation in isolated macaque retina. The results suggest that local returns may reduce unwanted evoked activity and thus optimize the selectivity of stimulation at cellular resolution. Similar patterns could be implemented in a future high-resolution prosthesis to permit a more faithful replication of normal retinal activity for the treatment of incurable blindness.

neuroscience↗