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698 results for “Retina”
Neural Retina Atlas
<p>This repository contains intermediate files used in analysis for the manuscript 'A proteogenomic atlas of the human neural retina' by Riepe et al. (2024). The code is available at https://github.com/cmbi/Neural-Retina-Atlas.</p>
Reference data for neural retina atlas
<p>This repository contains reference files used in analysis for the manuscript 'A proteogenomic atlas of the human neural retina' by Riepe et al. (2024). The code is available at https://github.com/cmbi/Neural-Retina-Atlas.</p>
scGPT: End-to-End Protocol for Fine-tuned Retina Cell Type Annotation
<h1>Abstract</h1> <p>Single-cell research faces challenges in accurately annotating cell types at high resolution, especially when dealing with large-scale datasets and rare cell populations. To address this, foundation models like scGPT offer flexible, scalable solutions by leveraging transformer-based architectures. This protocol provides a comprehensive guide to fine-tuning scGPT for cell-type classification in single-cell RNA sequencing (scRNA-seq) data. We demonstrate how to fine-tune scGPT on a custom retina dataset, highlighting the model’s efficiency in handling complex data and improving annotation accuracy achieving 99.5% F1-score. This protocol automates key steps, including data preprocessing, model fine-tuning, and evaluation. This protocol enables researchers to efficiently deploy scGPT for their own datasets. The provided tools, including a command-line script and Jupyter Notebook, simplify the customization and exploration of the model, proposing an accessible workflow for users with minimal Python and Linux knowledge. The protocol offers an off-the-shell solution of high-precision cell-type annotation using scGPT for researchers with intermediate bioinformatics.</p>
Closed-loop experiment in the retina
<p><strong>Data from closed-loop experiment in the retina</strong></p> <p>See the related code on github:</p> <p>https://github.com/ChrisGll/RBM_TRBM</p> <p>This file explains the organization of data recorded in the closed-loop experiment performed by Christophe Gardella and used in the following articles:<br> - Closed-loop estimation of retinal network sensitivity reveals signature of efficient coding, Ferrari, Gardella, Marre and Mora, eNeuro, 2017: http://www.eneuro.org/content/early/2018/01/16/ENEURO.0166-17.2017<br> - Blindfold learning of an accurate neural metric, Gardella, Marre and Mora, PNAS, 2018: http://www.pnas.org/content/early/2018/03/09/1718710115.long<br> The stimulus consists in a series of 0.9 s snippets of bar trajectory. Each snippet is called a sequence. The bar has a smooth random motion, with each sequence trajectory beginning and ending at position 0, the center of the screen.</p> <p>In parentheses are the values specific to our data.</p> <p><strong>Notations :</strong><br> Scalar variables:<br> stim_rate: (=50) binning rate for the stimulus, in Hz</p> <p>Nne: (=60) number of neurons<br> Nseq: (=17034) number of sequences<br> Nb_seq: (=45) number of time bins per sequence</p> <p>Nreftj: (=2) number of reference trajectories<br> Npertdir: (=16) number of perturbation directions per reference trajectory</p> <p><br> Indices:<br> ne_i : index of neuron<br> b_i : index of time bin seq_i : index of sequence (from 1 to Nseq) reftj_i : index of reference trajectory (1 or Nreftj)<br> pertdir_i : index of perturbation direction (1 to Npertdir)</p> <p>In general, in the code:<br> ..._i stands for index:<br> ..._l stands for list<br> ..._il stands for list of indices</p> <p><strong>Stimulus:</strong><br> trajs: cell of size (Nseq,1) with<br> trajs{seq_i}: vector of size (1, Nb_seq) with trajs{seq_i}(b_i) the bar position in time bin b_i, in µm.</p> <p>rand_seq_il: list of indices of sequences corresponding to random bar trajectories</p> <p>Example: rand_seq_il(1) is the index of the first sequence corresponding to a random trajectory. trajs{rand_seq_il(1)} is the corresponding random trajectory.</p> <p>ref_seq_il: cell of size (1, Nreftj) with ref_seq_il{reftj_i} the list of indices of sequences corresponding to repetitions of reference trajectory reftj_i.</p> <p>pert_seq_il: cell of size (Npertdir, Nreftj) with<br> pert_seq_il{pertdir_i, reftj_i}: list of indices of sequences corresponding to a perturbation of reference trajectory reftj_i in direction pertdir_i. Indices are sorted by increasing perturbation amplitude.</p> <p>Example: All sequence trajectories are either random, a trajectory, or a perturbation. So the intersection between rand_seq_il, any ref_seq_il{reftj_i} or any pert_seq_il{pertdir_i, reftj_i} is always be empty.</p> <p>Example: The union between rand_seq_il, all ref_seq_il{reftj_i} and all pert_seq_il{pertdir_i, reftj_i} is the complete list of indices 1:Nseq.</p> <p>pert_amp_l: cell of size (Npertdir, Nreftj) with<br> pert_amp_l{pertdir_i, reftj_i}: list of amplitudes of corresponding perturbations. pert_amp_l{pertdir_i, reftj_i}(n) is the amplitude of the perturbation in sequence pert_seq_il{pertdir_i, reftj_i}(n).</p> <p><strong>Responses:</strong><br> spkb_rate : (=50) binning rate for the responses, in Hz sparse_spkb_all: cell of size (Nseq,1) with<br> sparse_spkb_all{seq_i}: sparse matrix of size (Nne, Nb_seq): binned response during sequence seq_i. sparse_spkb_all{seq_i}(ne_i,b_i)=1 if neuron ne_i spiked at least once in time bin b_i.</p> <p>sparse_spkb_all is the representation of responses used for computing the linear discriminability and distances with the RBM and TRBM metrics.</p> <p>In order to compute distances, one usually only considers a subset of the Nb_seq time bins of the sequence. We note:<br> b_il_s: (=23:37) list of indices of time bins used for distance computation.</p> <p>If one needs un-binned responses (for some metrics such as the van Rossum metric), spike times can be found in the variable spkt_all:</p> <p>spkt_all: cell of size (Nseq,1) with<br> spkt_all{seq_i}: cell of size (Nne, 1) with<br> spkt_all{seq_i}{ne_i}: list of spike times (in UNIT) of neuron ne_i in sequence seq_i. The list is empty if there is no spike. These times are in s, and are relative, with t=0 the beginning of the sequence.</p> <p>pert_lindiscrim_l: cell of size (Npertdir, Nreftj) with<br> pert_lindiscrim_l{pertdir_i, reftj_i} the linear discriminability of responses to the perturbation of reference trajectory reftj_i in direction pertdir_i. pert_lindiscrim_l{pertdir_i, reftj_i}(n) is the linear discriminability of responses in sequence pert_seq_il{pertdir_i, reftj_i}(n).</p> <p> </p>
Blood Vessels Dataset obtained from Retina Images of Healthy and Diabetic Retinopathy Individual
<p>This dataset contains blood vessels image files extracted from publicly available fundus retina images</p>
Biochemical Characterization of Mouse Retina of an Alzheimer's Disease Model by Raman Spectroscopy
<p>Raman raw data for the paper "Biochemical Characterization of Mouse Retina of an Alzheimer’s Disease Model by Raman Spectroscopy"</p> <ul> <li>two datasets of Raman images from cross-sectional and en face mouse retinas without processing</li> </ul>
РИС. 1. Схематичное иЗображение глаЗа наЗемного лёгочного моллюска. СокраЩениЯ: c – роговица; ec – глаЗнаЯ капсула; r – сетчатка; p – краЯ Зрачка; l – хрусталик, окруженный слоем стекловидного тела; L abs – абсолютное расстоЯние между Зрачком и наружной поверхностью хрусталика; D l – продольный диаметр хрусталика; А – абсолютный диаметр Зрачabs ка; D – поперечный диаметр глаЗа. FIG. 1. Schematic drawing of the eye of a terrestrial pulmonate mollusk. Abbreviation: c – cornea; ec – eye capsule; r – retina; p – edges of the pupil; l – lens, surrounded by a layer of the vitreous body; L abs – the absolute distance between the pupil and the outer surface of the lens; D l – the longitudinal diameter of the lens; А abs – the absolute diameter of the pupil; D e – the transverse diameter of the eye. in Зрачок камерных глаЗ наЗемных брюхоногих моллюсков (Heterobranchia, Stylommatophora)
РИС. 1. Схематичное иЗображение глаЗа наЗемного лёгочного моллюска. СокраЩениЯ: c – роговица; ec – глаЗнаЯ капсула; r – сетчатка; p – краЯ Зрачка; l – хрусталик, окруженный слоем стекловидного тела; L abs – абсолютное расстоЯние между Зрачком и наружной поверхностью хрусталика; D l – продольный диаметр хрусталика; А – абсолютный диаметр Зрачabs ка; D – поперечный диаметр глаЗа. FIG. 1. Schematic drawing of the eye of a terrestrial pulmonate mollusk. Abbreviation: c – cornea; ec – eye capsule; r – retina; p – edges of the pupil; l – lens, surrounded by a layer of the vitreous body; L abs – the absolute distance between the pupil and the outer surface of the lens; D l – the longitudinal diameter of the lens; А abs – the absolute diameter of the pupil; D e – the transverse diameter of the eye.
РИС. 2. ГлаЗ Monachoides incarnata. A. ФотографиЯ препарата иЗолированного глаЗа. B. ФотографиЯ продольного полутонкого среЗа глаЗа. СокраЩениЯ: c – роговица; ec – глаЗнаЯ капсула; r – сетчатка; p – краЯ Зрачка; l – хрусталик, окруженный слоем стекловидного тела. FIG. 2. The eye of Monachoides incarnata. A. Photograph of the preparation of the isolated eye. B. Photograph of the longitudinal semithin section of the eye. Abbreviation: c – cornea; ec – eye capsule; r – retina; p – edges of the pupil; l – lens, surrounded by a layer of the vitreous body. in Зрачок камерных глаЗ наЗемных брюхоногих моллюсков (Heterobranchia, Stylommatophora)
РИС. 2. ГлаЗ Monachoides incarnata. A. ФотографиЯ препарата иЗолированного глаЗа. B. ФотографиЯ продольного полутонкого среЗа глаЗа. СокраЩениЯ: c – роговица; ec – глаЗнаЯ капсула; r – сетчатка; p – краЯ Зрачка; l – хрусталик, окруженный слоем стекловидного тела. FIG. 2. The eye of Monachoides incarnata. A. Photograph of the preparation of the isolated eye. B. Photograph of the longitudinal semithin section of the eye. Abbreviation: c – cornea; ec – eye capsule; r – retina; p – edges of the pupil; l – lens, surrounded by a layer of the vitreous body.
Feedback from retinal ganglion cells to the inner retina
<p>This repository contains the data files and codes used in the following paper:</p> <p>Feedback from retinal ganglion cells to the inner retina<br> Anastasiia Vlasiuk, Hiroki Asari<br> PLOS One (accepted)</p> <p>The preprint is also available from bioRxiv 2020.08.30.274514.</p>
Annotation results of two human retina organoids
<p>Cell annotation was based on i) marker genes which were extracted from literature and expert knowledge, or (ii) via a transfer learning tool CaSTLe (Lieberman Y, Rokach L, Shay T, 2018) using the Cowan <em>et al</em>. (2020) organoid reference data set.</p> <p>Data preprocessing of both scRNA-seq data sets was done in scanpy (Wolf, F., Angerer, P. & Theis, 2018), and are included as h5ad-files.</p> <p>The annotation results are included in the .csv files</p>
Spatial organization of the mouse retina at single cell resolution by MERFISH
<p><strong>Abstract:</strong><br> <br> The visual signal processing in the retina requires the precise organization of diverse neuronal types working in concert. While single-cell omics studies have identified more than 120 different neuronal subtypes in the mouse retina1, little is known about their spatial organization. Here, we generated the first single-cell spatial atlas of the mouse retina using multiplexed error-robust fluorescence in situ hybridization (MERFISH). We profiled over 390,000 cells and identified all major cell types and nearly all subtypes through the integration with reference single-cell RNA sequencing (scRNA-seq) data. Our spatial atlas allowed simultaneous examination of nearly all cell subtypes in the retina, revealing 8 previously unknown displaced amacrine cell subtypes and establishing the first connection between the molecular classification of many cell subtypes and their spatial arrangement. Furthermore, we identified spatially dependent differential gene expression between subtypes, suggesting the possibility of functional tuning of neuronal types based on location.<br> <br> <strong>Data description:</strong><br> 1. VZA105a_integrated_368genes.h5ad<br> This file contains the raw MERFISH count matrix for four samples with 368 gene features. The "sampleid" column represents the unique sample ID, while the "region" column corresponds to the tissue section ID. The "majorclass" and "subclass" columns indicate annotated retinal cell types. Finally, the "center_x" and "center_y" columns provide the coordinates of the cell centers.</p> <p><br> 2. VA45_integrated.h5ad<br> This file contains the raw MERFISH count matrix for six samples with 500 gene features. The "sampleid" column represents the unique sample ID, while the "region" column corresponds to the tissue section ID. The "majorclass" and "subclass" columns indicate annotated retinal cell types. Finally, the "center_x" and "center_y" columns provide the coordinates of the cell centers.<br> <br> 3. merfish_impute.h5ad<br> This file contains the imputed count matrix for ten samples. The "sampleid" column represents the unique sample ID, while the "region" column corresponds to the tissue section ID. The "majorclass" and "subclass" columns indicate annotated retinal cell types. Finally, the "center_x" and "center_y" columns provide the coordinates of the cell centers.<br> </p>
Data for Center-surround interactions underlie bipolar cell motion sensitivity in the mouse retina
<p>Data from the publication "Center-surround interactions underlie bipolar cell motion sensitivity in the mouse retina" published in <em>Nature Communications</em> (2022) https://doi.org/10.1038/s41467-022-32762-7.</p> <p>Code for working with this dataset may be found at https://github.com/eulerlab/bc-motion</p>
An ON-type direction selective ganglion cell in primate retina
Open the record for dataset details and reuse information.
Restoration of cone circuit functionality in the regenerating adult zebrafish retina
Open the record for dataset details and reuse information.
Data from: Pushing the limits of photoreception in twilight conditions: The rod-like cone retina of the deep-sea pearlsides
Most vertebrates have a duplex retina comprising two photoreceptor types, rods for dim-light (scotopic) vision and cones for bright-light (photopic) and color vision. However, deep-sea fishes are only active in dim-light conditions; hence, most species have lost their cones in favor of a simplex retina composed exclusively of rods. Although the pearlsides, Maurolicus spp., have such a pure rod retina, their behavior is at odds with this simplex visual system. Contrary to other deep-sea fishes, pearlsides are mostly active during dusk and dawn close to the surface, where light levels are intermediate (twilight or mesopic) and require the use of both rod and cone photoreceptors. This study elucidates this paradox by demonstrating that the pearlside retina does not have rod photoreceptors only; instead, it is composed almost exclusively of transmuted cone photoreceptors. These transmuted cells combine the morphological characteristics of a rod photoreceptor with a cone opsin and a cone phototransduction cascade to form a unique photoreceptor type, a rod-like cone, specifically tuned to the light conditions of the pearlsides' habitat (blue-shifted light at mesopic intensities). Combining properties of both rods and cones into a single cell type, instead of using two photoreceptor types that do not function at their full potential under mesopic conditions, is likely to be the most efficient and economical solution to optimize visual performance. These results challenge the standing paradigm of the function and evolution of the vertebrate duplex retina and emphasize the need for a more comprehensive evaluation of visual systems in general.
Drosophila pupal retina
<p>Confocal imaging data associated with paper:</p> <p> <a href="https://pubmed.ncbi.nlm.nih.gov/38533736/">Inter-plane feedback coordinates cell morphogenesis and maintains 3D tissue organization in the Drosophila pupal retina. </a></p> <div><span>Sun X, Decker J, Sanchez-Luege N, Rebay I.</span> <span>Development. 2024 Mar 15;151(6):dev201757. doi: 10.1242/dev.201757. Epub 2024 Mar 27.</span> <span>PMID: <span>38533736</span></span></div>
The Regulation of Microglia Activity and the Production of IL-1α and IL-6 in the Degenerated Retina by Mesenchymal Stem Cells
<p><span>Activation of immune response and production of proinflammatory factors plays an important role in the development and progression of retinal degenerative diseases (RDD). For this reason, focusing on immunomodulation is potential option for the efficient ophthalmological therapy. Mesenchymal stem cells (MSCs) have been study in the treatment of RDD mainly due to their regenerative and neuroprotective actions. Nevertheless, MSCs also possess several immunomodulatory properties. Our study shows that NaIO<sub>3</sub>-induced degeneration increased <em>in vitro</em> and <em>in vivo</em> expression of genes for Interleukin (IL)-1α and IL-6 in the mouse retinal tissue. In addition, intraperitoneal application of NaIO<sub>3</sub> increased expression of gene for Iba-1 and infiltration of CD45<sup>+</sup>CD11b<sup>+</sup> cells into the retina. CD45<sup>+</sup> population was also responsible for the production of IL-1α while IL-6 was produced by CD45<sup>-</sup> cells. Cocultivation of degenerated retina in the presence of MSCs decreased the expression and production of both studied cytokines and expression of gene for Iba-1 in the retinal tissue. On contrary, it was observed that MSCs treated with supernatant from degenerated retina increased the expression of genes for cyclooxygenase-2, transforming growth factor-β, programmed-death ligand 1 and nerve growth factor. These results show that MSCs are able to regulate immune reaction in the degenerated retinal environment.</span></p>
Human Retina Cell Atlas reference model
<p>This dataset hosts files needed to reproduce the Human Retina Cell Atlas (HRCA) reference model using scArches. The HRCA data can be accessed through several interactive browsers, including <a href="https://data.humancellatlas.org/hca-bio-networks/eye/atlases/retina-v1-0">HCA Data Portal</a>, <a href="https://cellxgene.cziscience.com/collections/4c6eaf5c-6d57-4c76-b1e9-60df8c655f1e">CELLxGENE</a>, <a href="https://retina.cells.ucsc.edu/">UCSC Cell Browser</a>, and <a href="https://singlecell.broadinstitute.org/single_cell/study/SCP2805">the Broad Single Cell Portal</a>. Please use these browsers for atlas exploration and visualization. For more information on HRCA, please refer to the HRCA paper (<a href="https://doi.org/10.1101/2023.11.07.566105" rel="nofollow">Li et al.</a>, bioRxiv 2023) and the Github repository at <a href="https://github.com/RCHENLAB/HRCA_reproducibility">https://github.com/RCHENLAB/HRCA_reproducibility</a>. This dataset has been used in the tutorial for the HRCA reference model at <a href="https://github.com/RCHENLAB/HRCA_reproducibility/tree/main/scArches">https://github.com/RCHENLAB/HRCA_reproducibility/tree/main/scArches</a>.<br><br></p> <p><strong>Data description:</strong></p> <p>1. HRCA_snRNA_allcells_rawcounts.h5ad</p> <p>This file contains the cell-by-gene count matrix for over 3.1 million single nuclei and more than 36,000 gene features of the HRCA. Gene features are represented by gene symbols. Please refer to the interactive browsers for atlas exploration, where gene features are mapped to Ensembl IDs. In the cell metadata, "sampleid" indicates sample batches of cells, and "celltype" specifies 123 retina cell types.</p> <p>2. model.pt</p> <p>This file is the trained reference model using scArches, incorporating 10,000 highly variable features from the full count matrix. It can be directly used for cell type annotation of new retina samples.</p> <p>3. HRCA_snRNA_allcells_rawcounts_latent.h5ad</p> <p>This file contains the embeddings of all 3.1 million reference single nuclei generated by the trained reference model using scArches. These embeddings can be used to compare with the embeddings of query data for exploration.</p> <p>4. HRCA_reference_model_gene_id_and_symbol.csv</p> <p>This file contains the mapping of Ensembl IDs to gene symbols for the 10,000 features used in the reference model. This mapping can be used to convert the gene features in a query .h5ad file from gene IDs to gene symbols, allowing cell type labels to be predicted using the trained reference model, which uses gene symbols as gene features.</p> <p>5. query.h5ad</p> <p>This file contains a cell-by-gene count matrix for a query dataset, designed to support reproducibility in the HRCA reference model tutorial. The "majorclass" column includes pre-annotated major cell classes. Additional details on the tutorial are available at <a href="https://github.com/RCHENLAB/HRCA_reproducibility/tree/main/scArches">https://github.com/RCHENLAB/HRCA_reproducibility/tree/main/scArches</a>.</p> <p>6. query_latent.h5ad</p> <p>This file contains the embeddings of the query data against the trained reference model. These embeddings can be compared with the reference data embeddings for exploration and visualization.</p>
Annotation of a human retina organoid before and after imputation
<p>Two Seurat-objects which include the unimputed and DCA-imputed retina organoid data sets, published by Kim <em>et al. </em>, stored in RDS-format.</p>
Amacrine cells differentially balance zebrafish colour circuits in the central and peripheral retina
<p>In vertebrate vision, the feature-extracting circuits of the inner retina are driven by photoreceptors whose outputs are already pre-processed. In zebrafish, for example, outer retinal circuits split "colour" from "greyscale" information across all four cone-photoreceptor types. How does the inner retina process this incoming spectral information while also combining cone signals to shape new greyscale functions?</p> <p>We address this question by imaging the light-driven responses of amacrine cells (ACs) and bipolar cells (BCs) in larval zebrafish, in the presence and pharmacological absence of inner retinal inhibition. We find that amacrine cells exert distinct effects on greyscale processing depending on retinal region, as well as contributing to the generation of colour opponency in the central retina. However, in the peripheral retina amacrine cells enhanced opponency in some bipolar cells while at the same time suppressing pre-existing opponency in others, such that the net change in the number of colour-opponent units was essentially zero. To achieve this 'dynamic balance' ACs counteracted intrinsic colour opponency of BCs via the On-channel. Consistent with these observations, Off-stratifying ACs were exclusively achromatic, while all colour opponent ACs stratified in the On-sublamina.</p> <p>This study reveals that the central and peripheral retina of larval zebrafish employ fundamentally distinct inhibitory circuits to control the interaction between greyscale- and colour-processing. Differential actions on the On- and Off-channels control the transmission of colour-opponent signals in the periphery.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.