Non-linear Dimensionality Reduction on Extracellular Waveforms Reveals Physiological, Functional, and Laminar Diversity in Premotor Cortex

AbstractCortical circuits involved in decision-making are thought to contain a large number of cell types— each with different physiological, functional, and laminar distribution properties—that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features, such as trough to peak duration of extracellular spikes, to identify putative cell types, but these can only capture a small amount of variation. Here, we develop a new method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply WaveMAP to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using WaveMAP, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while also revealing undocumented diversity within these sub types. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics.SignificanceHow different cell types sculpt activity patterns in brain areas associated with decision-making is a fundamentally unresolved problem in neuroscience. In monkeys, and other species where transgenic access is not yet possible, identifying physiological types in vivo relies on only a few discrete user-specified features of extracellular waveforms to identify cell types. Here, we show that non-linear dimensionality reduction with graph clustering applied to the entire extracellular waveform can delineate many different putative cell types and does so in an interpretable manner. We show that this method reveals previously undocumented physiological, functional, and laminar diversity in the dorsal premotor cortex of monkeys, a key brain area implicated in decision-making.

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Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex

eLife ◽

10.7554/elife.67490 ◽

2021 ◽

Vol 10 ◽

Author(s):

Eric Kenji Lee ◽

Hymavathy Balasubramanian ◽

Alexandra Tsolias ◽

Stephanie Udochku Anakwe ◽

Maria Medalla ◽

...

Keyword(s):

Dimensionality Reduction ◽

Premotor Cortex ◽

Cell Types ◽

Cortical Circuits ◽

Cell Type ◽

Dorsal Premotor Cortex ◽

Non Linear ◽

Linear Dimensionality Reduction ◽

Nuanced Understanding

Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply WaveMAP to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using WaveMAP, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using featurebased approaches. WaveMAP therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.

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Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data

Scientific Reports ◽

10.1038/s41598-021-03613-0 ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Akram Vasighizaker ◽

Saiteja Danda ◽

Luis Rueda

Keyword(s):

Dimensionality Reduction ◽

Single Cell ◽

Cell Types ◽

Gene Set Enrichment Analysis ◽

Rna Seq ◽

Reduction Techniques ◽

Non Linear ◽

Dimensionality Reduction Techniques ◽

Linear Dimensionality Reduction ◽

The Impact

AbstractIdentifying relevant disease modules such as target cell types is a significant step for studying diseases. High-throughput single-cell RNA-Seq (scRNA-seq) technologies have advanced in recent years, enabling researchers to investigate cells individually and understand their biological mechanisms. Computational techniques such as clustering, are the most suitable approach in scRNA-seq data analysis when the cell types have not been well-characterized. These techniques can be used to identify a group of genes that belong to a specific cell type based on their similar gene expression patterns. However, due to the sparsity and high-dimensionality of scRNA-seq data, classical clustering methods are not efficient. Therefore, the use of non-linear dimensionality reduction techniques to improve clustering results is crucial. We introduce a method that is used to identify representative clusters of different cell types by combining non-linear dimensionality reduction techniques and clustering algorithms. We assess the impact of different dimensionality reduction techniques combined with the clustering of thirteen publicly available scRNA-seq datasets of different tissues, sizes, and technologies. We further performed gene set enrichment analysis to evaluate the proposed method’s performance. As such, our results show that modified locally linear embedding combined with independent component analysis yields overall the best performance relative to the existing unsupervised methods across different datasets.

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