ProjectMapping functional changes to retinal output neurons in photoreceptor degeneration using two-photon imaging and…

Basic data

Title:
Mapping functional changes to retinal output neurons in photoreceptor degeneration using two-photon imaging and spatial transcriptomics
Duration:
01/01/2025 to 31/12/2027
Abstract / short description:
Classification of neurons into types – that is, defining the “building blocks” of complex neural networks – is an important approach towards understanding brain function. Cell types in the brain have been defined based on their functional responses, morphology, and transcriptomes. However, to date, such cell type classifications have rarely integrated data across modalities. In addition, cell types are not static: Neurons can change (or even lose) their function during aging and disease. This is a problem when clustering relies on a single modality, such as neural responses: Is a “new” functional type in a disease model indeed “new” or a known type with altered function? Do cell types change when they age and if so, how? To address both issues, a dynamic, multimodal cell type concept is needed: Dynamic, to capture changes in cell type function, and multimodal, to leverage information from different modalities for robust cell type identification.
Here, we propose to classify mouse retinal ganglion cells (RGCs) based on aligned functional and genetic data from the same tissue, generated with two-photon calcium imaging and spatial transcriptomics. Our central hypothesis is that transcriptomic RGC types can vary in their function over time, e.g., during normal aging or a progres¬sive disease. To test this hypothesis, we will develop novel computational methods for multimodal cell type classification. We will then use this classification to follow RGC types over the disease progression in rd10 mice. In these mutant mice, photoreceptors degenerate over the time course of 6 months, while the rest of the retina remains structurally intact and is partially remodeled, rendering it a suitable model for retinal degeneration in patients with Retinitis Pigmentosa.
We will start by collecting a multimodal dataset in young adult wildtype mice. We will record RGC light responses in the explan¬¬ted retina and perform spatial tran¬scriptomics in the same tissue to deter¬mine the mRNA finger¬prints for the recorded cells. We will develop a Using anew multimodal clustering approach and seeded it by established cell atlases, we will to integrate functional and genetic finger¬prints of the RGC types. Next, we will complement the wildtype dataset by collecting data for different ages. Accordingly, we will extend our clustering model to gain insights into poten¬tial age-de¬pen¬dent functional changes in RGC types and determine whether those are accom¬panied by genetic changes. Finally, we will acquire a dataset in the rd10 mouse retina at different time points during the dege¬neration. Here, type-specific genetic mar¬kers in the transcriptomic fingerprint will serve as tags to follow the trajectory of distinct RGC types over degeneration progression.
From the new multimodal RGC classification developed in this project we expect new insights into the changes (and stabi¬lity) of RGC types in healthy vs. degenerating retina over time. These insights are important ¬to understand the brain’s “building blocks”, and the susceptibility and resilience of retinal circuits to insults such as loss of photo¬receptor input.
Keywords:
Retina
retina, Netzhaut
hereditary retinal diseases
erbliche Netzhauterkrankungen
transcriptome
Transkriptom
machine learning
maschinelles Lernen

Involved staff

Managers

Hertie Institute for Artificial Intelligence in Brain Health (HIAI)
Non-clinical institutes, Faculty of Medicine
Institute for Bioinformatics and Medical Informatics (IBMI)
Interfaculty Institutes
Cluster of Excellence: Machine Learning: New Perspectives for Science (CML)
Centers or interfaculty scientific institutions
Tübingen AI Center
Department of Informatics, Faculty of Science

Contact persons

Center for Ophthalmology
Hospitals and clinical institutes, Faculty of Medicine
Research Center for Ophthalmology
Center for Ophthalmology, Hospitals and clinical institutes, Faculty of Medicine
Werner Reichardt Center for Integrative Neuroscience (CIN)
Centers or interfaculty scientific institutions

Local organizational units

Hertie Institute for Artificial Intelligence in Brain Health (HIAI)
Non-clinical institutes
Faculty of Medicine
Research Center for Ophthalmology
Center for Ophthalmology
Hospitals and clinical institutes, Faculty of Medicine

Funders

Bonn, Nordrhein-Westfalen, Germany

Cooperations

Ramat Gan, Israel
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