Single-cell profiling of the human primary motor cortex in ALS and FTLD

Abstract

Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are two devastating and fatal neurodegenerative conditions. While distinct, they share many clinical, genetic, and pathological characteristics, and both show selective vulnerability of layer 5b extratelencephalic-projecting cortical populations, including Betz cells in ALS and von Economo neurons (VENs) in FTLD. Here, we report the first high resolution single-cell atlas of the human primary motor cortex (MCX) and its transcriptional alterations in ALS and FTLD across ~380,000 nuclei from 64 individuals, including 17 control samples and 47 sporadic and C9orf72-associated ALS and FTLD patient samples. We identify 46 transcriptionally distinct cellular subtypes including two Betz-cell subtypes, and we observe a previously unappreciated molecular similarity between Betz cells and VENs of the prefrontal cortex (PFC) and frontal insula. Many of the dysregulated genes and pathways are shared across excitatory neurons, including stress response, ribosome function, oxidative phosphorylation, synaptic vesicle cycle, endoplasmic reticulum protein processing, and autophagy. Betz cells and SCN4B+ long-range projecting L3/L5 cells are the most transcriptionally affected in both ALS and FTLD. Lastly, we find that the VEN/Betz cell-enriched transcription factor, POU3F1, has altered subcellular localization, co-localizes with TDP-43 aggregates, and may represent a cell type-specific vulnerability factor in the Betz cells of ALS and FTLD patient tissues.

Singling out motor neurons in the age of single-cell transcriptomics

“…Emerging technologies are poised to reshape how neurodegenerative disease is classified and investigated (Figure 4) [23,119,120]. However, arguably the most impactful development in…”

Huntington’s Disease Produces Multiplexed Transcriptional Vulnerabilities of Striatal D1-D2 and Striosome-Matrix Neurons

“…3), which seems a class of striatal interneuron according to our preliminary data (Pineda et al, in preparation). These cell types, identified by ACTIONet [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] , co-clustered perfectly atop each other between controls and the HD samples (magenta and cyan dots, Fig. 1A-C), affirming the consistency of cell-type annotations across phenotypes.…”

“…We analyzed snRNA-seq data by using ACTIONet [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] (Extended Data Fig. 1), from striatal samples harvested from human striatum and from R6/2 and zQ175 HD model mice, and originally reported in an initial study without attention to the coordinated compartmental transcriptomics examined here (Fig.…”

“…We used the ACTIONet and scran R packages to normalize, batch correct, and cluster single-nucleus gene counts. Batch-corrected data were used as input to the archetypal analysis for cell type identification (ACTION) algorithm [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] to identify a set of landmark cells or 'archetypes', each representing a potential underlying cell state. Using ACTION-decompositions with varying numbers of archetypes, we employed the ACTION-based network (ACTIONet) framework [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] to create a multi-resolution nearest neighbor graph.…”

“…Batch-corrected data were used as input to the archetypal analysis for cell type identification (ACTION) algorithm [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] to identify a set of landmark cells or 'archetypes', each representing a potential underlying cell state. Using ACTION-decompositions with varying numbers of archetypes, we employed the ACTION-based network (ACTIONet) framework [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] to create a multi-resolution nearest neighbor graph. A modified version of the stochastic gradient descent-based layout method was used in the uniform manifold approximation and projection (UMAP) algorithm 59 , to visualize the ACTIONet graph.…”