Patterns (Nov 2023)
MANGEM: A web app for multimodal analysis of neuronal gene expression, electrophysiology, and morphology
Abstract
Summary: Single-cell techniques like Patch-seq have enabled the acquisition of multimodal data from individual neuronal cells, offering systematic insights into neuronal functions. However, these data can be heterogeneous and noisy. To address this, machine learning methods have been used to align cells from different modalities onto a low-dimensional latent space, revealing multimodal cell clusters. The use of those methods can be challenging without computational expertise or suitable computing infrastructure for computationally expensive methods. To address this, we developed a cloud-based web application, MANGEM (multimodal analysis of neuronal gene expression, electrophysiology, and morphology). MANGEM provides a step-by-step accessible and user-friendly interface to machine learning alignment methods of neuronal multimodal data. It can run asynchronously for large-scale data alignment, provide users with various downstream analyses of aligned cells, and visualize the analytic results. We demonstrated the usage of MANGEM by aligning multimodal data of neuronal cells in the mouse visual cortex. The bigger picture: Recently, it has become possible to obtain multiple types of data (modalities) from individual neurons, like how genes are used (gene expression), how a neuron responds to electrical signals (electrophysiology), and what it looks like (morphology). These datasets can be used to group similar neurons together and learn their functions, but the complexity of the data can make this process difficult for researchers without sufficient computational skills. Various methods have been developed specifically for combining these modalities, and open-source software tools can alleviate the computational burden on biologists performing analyses of new data. Open-source tools performing modality combination (integration), clustering, and visualization have the potential to streamline the research process. It is our hope that intuitive and freely available software will advance neuroscience research by making advanced computational methods and visualizations more accessible.
