Consistent cross-modal identification of cortical neurons with coupled autoencoders

Gala, Rohan; Budzillo, Agata; Baftizadeh, Fahimeh; Miller, Jeremy A.; Gouwens, Nathan W.; Arkhipov, Anton; Tasic, Bosiljka; Murphy, Gabe J.; Zeng, Hongkui; Hawrylycz, Michael; Sümbül, Uygar

doi:10.1101/2020.06.30.181065

Cited by 11 publications

(15 citation statements)

References 32 publications

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“…Early classification of these neurons was based on their morphology [most notably of the axon (Kawaguchi and Kubota, 1997;Toledo-Rodriguez et al, 2005)], firing pattern, PV expression, and gene expression profile determined by PCR (Toledo-Rodriguez et al, 2004). More recent classifications based on single-cell or single-nucleus RNA-sequencing (RNA-seq) (Hodge et al, 2019), patch sequencing (Patch-seq) yielding morphoelectric (met-type) and transcriptomic (t-type) datasets (Gouwens et al, 2020), or machine learning applied to a large patch-seq dataset (Gala et al, 2020) have identified 7 clusters (RNA-seq) as well as 10 t-type and 5 met-type (patch-seq) Pvalb neurons. Given the different algorithms used to stratify the datasets, it is not surprising that the reported number of Pvalb neuron subtypes varies across studies.…”

Section: Pv Downregulationmentioning

confidence: 99%

The Parvalbumin Hypothesis of Autism Spectrum Disorder

Filice

Janickova

Henzi

et al. 2020

Front. Cell. Neurosci.

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The prevalence of autism spectrum disorder (ASD)—a type of neurodevelopmental disorder—is increasing and is around 2% in North America, Asia, and Europe. Besides the known genetic link, environmental, epigenetic, and metabolic factors have been implicated in ASD etiology. Although highly heterogeneous at the behavioral level, ASD comprises a set of core symptoms including impaired communication and social interaction skills as well as stereotyped and repetitive behaviors. This has led to the suggestion that a large part of the ASD phenotype is caused by changes in a few and common set of signaling pathways, the identification of which is a fundamental aim of autism research. Using advanced bioinformatics tools and the abundantly available genetic data, it is possible to classify the large number of ASD-associated genes according to cellular function and pathways. Cellular processes known to be impaired in ASD include gene regulation, synaptic transmission affecting the excitation/inhibition balance, neuronal Ca2+ signaling, development of short-/long-range connectivity (circuits and networks), and mitochondrial function. Such alterations often occur during early postnatal neurodevelopment. Among the neurons most affected in ASD as well as in schizophrenia are those expressing the Ca2+-binding protein parvalbumin (PV). These mainly inhibitory interneurons present in many different brain regions in humans and rodents are characterized by rapid, non-adaptive firing and have a high energy requirement. PV expression is often reduced at both messenger RNA (mRNA) and protein levels in human ASD brain samples and mouse ASD (and schizophrenia) models. Although the human PVALB gene is not a high-ranking susceptibility/risk gene for either disorder and is currently only listed in the SFARI Gene Archive, we propose and present supporting evidence for the Parvalbumin Hypothesis, which posits that decreased PV level is causally related to the etiology of ASD (and possibly schizophrenia).

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Section: Pv Downregulationmentioning

confidence: 99%

The Parvalbumin Hypothesis of Autism Spectrum Disorder

Filice

Janickova

Henzi

et al. 2020

Front. Cell. Neurosci.

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“…Broadly, GABAergic neurons can be subdivided according to the expression of marker genes Parvalbumin (PV +), Somatostatin (SST +), and Serotonin Receptor 3a (5-HT3aR +) [329,330]. Within each of those three large groups, further subdivisions can be made according to gene expression [331,332], morphology, and electrophysiological firing parameters [333,334], with current estimates ranging from 20 to 60 subdivisions [335].…”

Section: Epigenetic Modulation In Gabaergic Neuron Developmentmentioning

confidence: 99%

“…Furthermore, studies measuring multiple modalities on the same neurons promise a unification of classifications from single-cell electrophysiology, morphology and RNA sequencing (Patch-Seq), and have delivered insights in glutamatergic [436] and GABAergic neuron populations [333]. Especially when coupled with advanced analysis techniques [334], those large datasets might soon be available as a "reference classifier" that experimental data can be compared with, similarly to reference atlases in neuroanatomy or reference genomes in genomics. 5) Identification of converging molecular pathways for therapeutic interventions: Functional interactions between several NDD related chromatin remodelers and their reg-ulatory proteins has been shown to converge on a shared transcriptional axis [156,287,[437][438][439].…”

Section: Future Perspectivesmentioning

confidence: 99%

The emerging role of chromatin remodelers in neurodevelopmental disorders: a developmental perspective

Mossink

Negwer

Schubert

et al. 2020

Cell. Mol. Life Sci.

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Neurodevelopmental disorders (NDDs), including intellectual disability (ID) and autism spectrum disorders (ASD), are a large group of disorders in which early insults during brain development result in a wide and heterogeneous spectrum of clinical diagnoses. Mutations in genes coding for chromatin remodelers are overrepresented in NDD cohorts, pointing towards epigenetics as a convergent pathogenic pathway between these disorders. In this review we detail the role of NDD-associated chromatin remodelers during the developmental continuum of progenitor expansion, differentiation, cell-type specification, migration and maturation. We discuss how defects in chromatin remodelling during these early developmental time points compound over time and result in impaired brain circuit establishment. In particular, we focus on their role in the three largest cell populations: glutamatergic neurons, GABAergic neurons, and glia cells. An in-depth understanding of the spatiotemporal role of chromatin remodelers during neurodevelopment can contribute to the identification of molecular targets for treatment strategies.

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“…Alignment has proven effective for matching cell type sequence data collected on different platforms, across multiple data modalities, and even between species where few homologous marker genes show conserved patterns ( Bakken et al, 2020a ; Bakken et al, 2020b ; Hodge et al, 2020 ; Hodge et al, 2019 ; Yao et al, 2020a ). When combined with experimental methods such as Patch-seq ( Cadwell et al, 2016 ; Fuzik et al, 2016 ; Gouwens et al, 2020 ; Scala et al, 2020 ), which involves application of electrophysiological recording and morphological analysis of single patch-clamped neurons followed by scRNA-seq of cell contents, autoencoder-based dimensionality reduction ( Gala et al, 2019 ) can extend these alignments to bridge distinct modalities. Such analysis strategies provide a mechanism for classifying cell types using data from disparate data sources, allow for annotation transfer between experiments, and are a critical step toward unifying data-driven cell type definitions.…”

Section: Introductionmentioning

confidence: 99%

Common cell type nomenclature for the mammalian brain

Miller

Gouwens

Tasic

et al. 2020

eLife

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The advancement of single-cell RNA-sequencing technologies has led to an explosion of cell type definitions across multiple organs and organisms. While standards for data and metadata intake are arising, organization of cell types has largely been left to individual investigators, resulting in widely varying nomenclature and limited alignment between taxonomies. To facilitate cross-dataset comparison, the Allen Institute created the common cell type nomenclature (CCN) for matching and tracking cell types across studies that is qualitatively similar to gene transcript management across different genome builds. The CCN can be readily applied to new or established taxonomies and was applied herein to diverse cell type datasets derived from multiple quantifiable modalities. The CCN facilitates assigning accurate yet flexible cell type names in the mammalian cortex as a step toward community-wide efforts to organize multi-source, data-driven information related to cell type taxonomies from any organism.

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Consistent cross-modal identification of cortical neurons with coupled autoencoders

Cited by 11 publications

References 32 publications

The Parvalbumin Hypothesis of Autism Spectrum Disorder

The Parvalbumin Hypothesis of Autism Spectrum Disorder

The emerging role of chromatin remodelers in neurodevelopmental disorders: a developmental perspective

Common cell type nomenclature for the mammalian brain

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