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

eLife ◽  
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.

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

2021 ◽  
Author(s):  
Eric Kenji Lee ◽  
Hymavathy Balasubramanian ◽  
Alexandra Tsolias ◽  
Stephanie Anakwe ◽  
Maria Medalla ◽  
...  
Keyword(s):  
Decision Making ◽  
Dimensionality Reduction ◽  
Brain Area ◽  
Premotor Cortex ◽  
Cell Types ◽  
Graph Clustering ◽  
Dorsal Premotor Cortex ◽  
Non Linear ◽  
Linear Dimensionality Reduction

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.

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

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.

scholarly journals Cell-type specific analysis of physiological action of estrogen in mouse oviducts

2020 ◽  
Author(s):  
Emily A. McGlade ◽  
Gerardo G. Herrera ◽  
Kalli K. Stephens ◽  
Sierra L. W. Olsen ◽  
Sarayut Winuthayanon ◽  
...  
Keyword(s):  
Hormone Replacement ◽  
Cell Types ◽  
Normal Embryo ◽  
Developmental Defect ◽  
Cell Type ◽  
Specific Analysis ◽  
Cell Type Specific ◽  
17Β Estradiol ◽  
Oviductal Cell

AbstractOne of the endogenous estrogens, 17β-estradiol (E2) is a female steroid hormone secreted from the ovary. It is well established that E2 causes biochemical and histological changes in the uterus. The oviduct response to E2 is virtually unknown in an in vivo environment. In this study, we assessed the effect of E2 on each oviductal cell type, using an ovariectomized-hormone-replacement mouse model, single cell RNA-sequencing (scRNA-seq), in situ hybridization, and cell-type-specific deletion in mice. We found that each cell type in the oviduct responded to E2 distinctively, especially ciliated and secretory epithelial cells. The treatment of exogenous E2 did not drastically alter the transcriptomic profile from that of endogenous E2 produced during estrus. Moreover, we have identified and validated genes of interest in our datasets that may be used as cell- and region-specific markers in the oviduct. Insulin-like growth factor 1 (Igf1) was characterized as an E2-target gene in the mouse oviduct and was also expressed in human Fallopian tubes. Deletion of Igf1 in progesterone receptor (Pgr)-expressing cells resulted in female subfertility, partially due to an embryo developmental defect and embryo retention within the oviduct. In summary, we have shown that oviductal cell types are differentially regulated by E2 and support gene expression changes that are required for normal embryo development and transport in mouse models.

scholarly journals Profound functional and molecular diversity of mitochondria revealed by cell type-specific profiling in vivo

2018 ◽  
Author(s):  
Caroline Fecher ◽  
Laura Trovò ◽  
Stephan A. Müller ◽  
Nicolas Snaidero ◽  
Jennifer Wettmarshausen ◽  
...  
Keyword(s):  
Molecular Diversity ◽  
Molecular Level ◽  
Cell Types ◽  
Long Chain Fatty Acids ◽  
Cell Type ◽  
Mitochondrial Proteome ◽  
Novel Approach ◽  
Cell Type Specific ◽  
And Function

AbstractMitochondria vary in morphology and function in different tissues, however little is known about their molecular diversity among cell types. To investigate mitochondrial diversity in vivo, we developed an efficient protocol to isolate cell type-specific mitochondria based on a new MitoTag mouse. We profiled the mitochondrial proteome of three major neural cell types in cerebellum and identified a substantial number of differential mitochondrial markers for these cell types in mice and humans. Based on predictions from these proteomes, we demonstrate that astrocytic mitochondria metabolize long-chain fatty acids more efficiently than neurons. Moreover, we identified Rmdn3 as a major determinant of ER-mitochondria proximity in Purkinje cells. Our novel approach enables exploring mitochondrial diversity on the functional and molecular level in many in vivo contexts.

scholarly journals Renal cell markers: lighthouses for managing renal diseases

2021 ◽  
Author(s):  
Shivangi Agarwal ◽  
Yashwanth R Sudhini ◽  
Onur K Polat ◽  
Jochen Reiser ◽  
Mehmet Mete Altintas
Keyword(s):  
High Efficiency ◽  
Imaging Techniques ◽  
Unmet Need ◽  
Cell Types ◽  
Whole Body ◽  
Renal Diseases ◽  
Cell Type ◽  
Cell Markers ◽  
Urine Production

Kidneys, one of the vital organs in our body, are responsible for maintaining whole-body homeostasis. The complexity of renal function (e.g., filtration, reabsorption, fluid and electrolyte regulation, urine production) demands diversity not only at the level of cell types but also in their overall distribution and structural framework within the kidney. To gain an in-depth molecular-level understanding of the renal system, it is imperative to discern the components of kidney and the types of cells residing in each of the sub-regions. Recent developments in labeling, tracing, and imaging techniques enabled us to mark, monitor and identify these cells in vivo with high efficiency in a minimally invasive manner. In this review, we have summarized different cell types, specific markers that are uniquely associated with those cell types, and their distribution in kidney, which altogether make kidneys so special and different. Cellular sorting based on the presence of certain proteins on the cell surface allowed for assignment of multiple markers for each cell type. However, different studies using different techniques have found contradictions in the cell-type specific markers. Thus, the term "cell marker" might be imprecise and sub-optimal, leading to uncertainty when interpreting the data. Therefore, we strongly believe that there is an unmet need to define the best cell markers for a cell type. Although, the compendium of renal-selective marker proteins presented in this review is a resource that may be useful to the researchers, we acknowledge that the list may not be necessarily exhaustive.

scholarly journals Revisiting the role of IRF3 in inflammation and immunity by conditional and specifically targeted gene ablation in mice

2018 ◽  
Vol 115 (20) ◽  
pp. 5253-5258 ◽  
Author(s):  
Hideyuki Yanai ◽  
Shiho Chiba ◽  
Sho Hangai ◽  
Kohei Kometani ◽  
Asuka Inoue ◽  
...  
Keyword(s):  
Immune Cell ◽  
Cell Types ◽  
Type I ◽  
Type I Ifn ◽  
Cell Type ◽  
Gene Ablation ◽  
Cell Type Specific

IFN regulatory factor 3 (IRF3) is a transcription regulator of cellular responses in many cell types that is known to be essential for innate immunity. To confirm IRF3’s broad role in immunity and to more fully discern its role in various cellular subsets, we engineered Irf3-floxed mice to allow for the cell type-specific ablation of Irf3. Analysis of these mice confirmed the general requirement of IRF3 for the evocation of type I IFN responses in vitro and in vivo. Furthermore, immune cell ontogeny and frequencies of immune cell types were unaffected when Irf3 was selectively inactivated in either T cells or B cells in the mice. Interestingly, in a model of lipopolysaccharide-induced septic shock, selective Irf3 deficiency in myeloid cells led to reduced levels of type I IFN in the sera and increased survival of these mice, indicating the myeloid-specific, pathogenic role of the Toll-like receptor 4–IRF3 type I IFN axis in this model of sepsis. Thus, Irf3-floxed mice can serve as useful tool for further exploring the cell type-specific functions of this transcription factor.

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