MANGEM: a web app for Multimodal Analysis of Neuronal Gene expression, Electrophysiology and Morphology.

Single-cell techniques have enabled the acquisition of multi-modal data, particularly for neurons, to characterize cellular functions. Patch-seq, for example, combines patch-clamp recording, cell imaging, and single-cell RNA-seq to obtain electrophysiology, morphology, and gene expression data from a single neuron. While these multi-modal data offer potential insights into neuronal functions, they 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 multi-modal cell clusters. However, the use of those methods can be challenging for biologists and neuroscientists without computational expertise and also requires suitable computing infrastructure for computationally expensive methods. To address these issues, we developed a cloud-based web application, MANGEM (Multimodal Analysis of Neuronal Gene expression, Electrophysiology, and Morphology) at https://ctc.waisman.wisc.edu/mangem. MANGEM provides a step-by-step accessible and user-friendly interface to machine-learning alignment methods of neuronal multi-modal data while enabling real-time visualization of characteristics of raw and aligned cells. It can be run asynchronously for large-scale data alignment, provides users with various downstream analyses of aligned cells and visualizes the analytic results such as identifying multi-modal cell clusters of cells and detecting correlated genes with electrophysiological and morphological features. We demonstrated the usage of MANGEM by aligning Patch-seq multimodal data of neuronal cells in the mouse visual cortex.

BOMA, a machine-learning framework for comparative gene expression analysis across brains and organoids.

Our machine-learning framework, brain and organoid manifold alignment (BOMA), first performs a global alignment of developmental gene expression data between brains and organoids. It then applies manifold learning to locally refine the alignment, revealing conserved and specific developmental trajectories across brains and organoids. Using BOMA, we found that human cortical organoids better align with certain brain cortical regions than with other non-cortical regions, implying organoid-preserved developmental gene expression programs specific to brain regions. Additionally, our alignment of non-human primate and human brains reveals highly conserved gene expression around birth. Also, we integrated and analyzed developmental single-cell RNA sequencing (scRNA-seq) data of human brains and organoids, showing conserved and specific cell trajectories and clusters. Further identification of expressed genes of such clusters and enrichment analyses reveal brain- or organoid-specific developmental functions and pathways. Finally, we experimentally validated important specific expressed genes through the use of immunofluorescence. BOMA is open-source available as a web tool for community use.