scholarly journals Intrinsic electrophysiological properties predict variability in morphology and connectivity among striatal Parvalbumin-expressing Pthlh-cells

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Carolina Bengtsson Gonzales ◽  
Steven Hunt ◽  
Ana B. Munoz-Manchado ◽  
Chris J. McBain ◽  
Jens Hjerling-Leffler
Keyword(s):  
Cell Types ◽  
Inhibitory Interneuron ◽  
Classification Schemes ◽  
Electrophysiological Properties ◽  
Neuronal Populations ◽  
Neuronal Classification ◽  
Single Cell Rna Sequencing ◽  
Different Types ◽  
Types Of Information ◽  
Activity Dependent

Abstract Determining the cellular content of the nervous system in terms of cell types and the rules of their connectivity represents a fundamental challenge to the neurosciences. The recent advent of high-throughput techniques, such as single-cell RNA-sequencing has allowed for greater resolution in the identification of cell types and/or states. Although most of the current neuronal classification schemes comprise discrete clusters, several recent studies have suggested that, perhaps especially, within the striatum, neuronal populations exist in continua, with regards to both their molecular and electrophysiological properties. Whether these continua are stable properties, established during development, or if they reflect acute differences in activity-dependent regulation of critical genes is currently unknown. We set out to determine whether gradient-like molecular differences in the recently described Pthlh-expressing inhibitory interneuron population, which contains the Pvalb-expressing cells, correlate with differences in morphological and connectivity properties. We show that morphology and long-range inputs correlate with a spatially organized molecular and electrophysiological gradient of Pthlh-interneurons, suggesting that the processing of different types of information (by distinct anatomical striatal regions) has different computational requirements.

scholarly journals A survey of human brain transcriptome diversity at the single cell level

2015 ◽  
Vol 112 (23) ◽  
pp. 7285-7290 ◽  
Author(s):  
Spyros Darmanis ◽  
Steven A. Sloan ◽  
Ye Zhang ◽  
Martin Enge ◽  
Christine Caneda ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cortical Neurons ◽  
Cell Types ◽  
Type I ◽  
Neuronal Populations ◽  
Brain Transcriptome ◽  
Single Cell Rna Sequencing ◽  
Whole Transcriptome

The human brain is a tissue of vast complexity in terms of the cell types it comprises. Conventional approaches to classifying cell types in the human brain at single cell resolution have been limited to exploring relatively few markers and therefore have provided a limited molecular characterization of any given cell type. We used single cell RNA sequencing on 466 cells to capture the cellular complexity of the adult and fetal human brain at a whole transcriptome level. Healthy adult temporal lobe tissue was obtained during surgical procedures where otherwise normal tissue was removed to gain access to deeper hippocampal pathology in patients with medical refractory seizures. We were able to classify individual cells into all of the major neuronal, glial, and vascular cell types in the brain. We were able to divide neurons into individual communities and show that these communities preserve the categorization of interneuron subtypes that is typically observed with the use of classic interneuron markers. We then used single cell RNA sequencing on fetal human cortical neurons to identify genes that are differentially expressed between fetal and adult neurons and those genes that display an expression gradient that reflects the transition between replicating and quiescent fetal neuronal populations. Finally, we observed the expression of major histocompatibility complex type I genes in a subset of adult neurons, but not fetal neurons. The work presented here demonstrates the applicability of single cell RNA sequencing on the study of the adult human brain and constitutes a first step toward a comprehensive cellular atlas of the human brain.

scholarly journals Q&A: using Patch-seq to profile single cells

BMC Biology ◽  
2017 ◽  
Vol 15 (1) ◽  
Author(s):  
Cathryn R. Cadwell ◽  
Rickard Sandberg ◽  
Xiaolong Jiang ◽  
Andreas S. Tolias
Keyword(s):  
Gene Expression ◽  
Nervous System ◽  
Patch Clamp ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Patch Clamp Recording ◽  
Electrophysiological Properties ◽  
Whole Cell Patch Clamp ◽  
Single Cell Rna Sequencing

Abstract Individual neurons vary widely in terms of their gene expression, morphology, and electrophysiological properties. While many techniques exist to study single-cell variability along one or two of these dimensions, very few techniques can assess all three features for a single cell. We recently developed Patch-seq, which combines whole-cell patch clamp recording with single-cell RNA-sequencing and immunohistochemistry to comprehensively profile the transcriptomic, morphologic, and physiologic features of individual neurons. Patch-seq can be broadly applied to characterize cell types in complex tissues such as the nervous system, and to study the transcriptional signatures underlying the multidimensional phenotypes of single cells.

scholarly journals Spatial mapping of cell types by integration of transcriptomics data

2019 ◽  
Author(s):  
Alma Andersson ◽  
Joseph Bergenstråhle ◽  
Michaela Asp ◽  
Ludvig Bergenstråhle ◽  
Aleksandra Jurek ◽  
...  
Keyword(s):  
Single Cell ◽  
Probabilistic Method ◽  
Cell Types ◽  
Spatial Mapping ◽  
Transcriptional Expression ◽  
Cell Type ◽  
Single Cell Rna Sequencing ◽  
Different Types ◽  
Transcriptomics Data ◽  
Spatial Cell

Spatial transcriptomics and single cell RNA-sequencing offer complementary insights into the transcriptional expression landscape. We here present a probabilistic method that integrates data from both techniques, leveraging their respective strengths in such a way that we are able to spatially map cell types to a tissue. The method is applied to several different types of tissue where the spatial cell type topographies are successfully delineated.

scholarly journals Phenotypic variation within and across transcriptomic cell types in mouse motor cortex

2020 ◽  
Author(s):  
Federico Scala ◽  
Dmitry Kobak ◽  
Matteo Bernabucci ◽  
Yves Bernaerts ◽  
Cathryn René Cadwell ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Cortical Neurons ◽  
Adult Mouse ◽  
Cell Types ◽  
Neuronal Diversity ◽  
Patch Clamp Recording ◽  
Electrophysiological Properties ◽  
Single Cell Rna Sequencing ◽  
Electric Space ◽  
Almost All

Cortical neurons exhibit astounding diversity in gene expression as well as in morphological and electrophysiological properties. Most existing neural taxonomies are based on either transcriptomic or morpho-electric criteria, as it has been technically challenging to study both aspects of neuronal diversity in the same set of cells. Here we used Patch-seq to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of over 1300 neurons in adult mouse motor cortex, providing a comprehensive morpho-electric annotation of almost all transcriptomically defined neural cell types. We found that, although broad families of transcriptomic types (Vip, Pvalb, Sst, etc.) had distinct and essentially non-overlapping morpho-electric phenotypes, individual transcriptomic types within the same family were not well-separated in the morpho-electric space. Instead, there was a continuum of variability in morphology and electrophysiology, with neighbouring transcriptomic cell types showing similar morpho-electric features, often without clear boundaries between them. Our results suggest that neural types in the neocortex do not always form discrete entities. Instead, neurons follow a hierarchy consisting of distinct non-overlapping branches at the level of families, but can form continuous and correlated transcriptomic and morpho-electrical landscapes within families.

scholarly journals Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments

2018 ◽  
Author(s):  
Jesse D. Bloom
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Common Method ◽  
Equal Proportion ◽  
Single Cell Rna Sequencing ◽  
Different Types ◽  
Multiple Cells ◽  
Human And Mouse

ABSTRACTIn single-cell RNA-sequencing, it is important to know the frequency at which the sequenced transcriptomes actually derive from multiple cells. A common method to estimate this multiplet frequency is to mix two different types of cells (e.g., human and mouse), and then determine how often the transcriptomes contain transcripts from both cell types. When the two cell types are mixed in equal proportion, the calculation of the multiplet frequency from the frequency of mixed transcriptomes is straightforward. But surprisingly, there are no published descriptions of how to calculate the multiplet frequency in the general case when the cell types are mixed unequally. Here I derive equations to analytically calculate the multiplet frequency from the numbers of observed pure and mixed transcriptomes when two cell types are mixed in arbitrary proportions, under the assumption that the loading of cells into droplets or wells is Poisson.

scholarly journals Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5578 ◽  
Author(s):  
Jesse D. Bloom
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Common Method ◽  
Equal Proportion ◽  
Single Cell Rna Sequencing ◽  
Different Types ◽  
Multiple Cells ◽  
Human And Mouse

In single-cell RNA-sequencing, it is important to know the frequency at which the sequenced transcriptomes actually derive from multiple cells. A common method to estimate this multiplet frequency is to mix two different types of cells (e.g., human and mouse), and then determine how often the transcriptomes contain transcripts from both cell types. When the two cell types are mixed in equal proportion, the calculation of the multiplet frequency from the frequency of mixed transcriptomes is straightforward. But surprisingly, there are no published descriptions of how to calculate the multiplet frequency in the general case when the cell types are mixed unequally. Here, I derive equations to analytically calculate the multiplet frequency from the numbers of observed pure and mixed transcriptomes when two cell types are mixed in arbitrary proportions, under the assumption that the loading of cells into droplets or wells is Poisson.

Study of the Molecular Architecture of Keratin Filaments by Electron Microscopy

1982 ◽  
Vol 40 ◽  
pp. 118-119
Author(s):  
U. Aebi ◽  
P. Rew ◽  
T.-T. Sun
Keyword(s):  
Electron Microscopy ◽  
Structural Organization ◽  
Cell Types ◽  
Structural Features ◽  
Molecular Architecture ◽  
The Past ◽  
Keratin Filaments ◽  
Different Types ◽  
10 Nm Filaments ◽  
Different Cell Types

Various types of intermediate-sized (10-nm) filaments have been found and described in many different cell types during the past few years. Despite the differences in the chemical composition among the different types of filaments, they all yield common structural features: they are usually up to several microns long and have a diameter of 7 to 10 nm; there is evidence that they are made of several 2 to 3.5 nm wide protofilaments which are helically wound around each other; the secondary structure of the polypeptides constituting the filaments is rich in ∞-helix. However a detailed description of their structural organization is lacking to date.

Effect of sleep on memory for binding different types of visual information

2020 ◽  
Author(s):  
John J Shaw ◽  
Zhisen Urgolites ◽  
Padraic Monaghan
Keyword(s):  
Visual Information ◽  
Storage Capacity ◽  
Declarative Memory ◽  
Recognition Task ◽  
Forced Choice ◽  
Long Term Memory ◽  
Term Memory ◽  
Different Types ◽  
Types Of Information

Visual long-term memory has a large and detailed storage capacity for individual scenes, objects, and actions. However, memory for combinations of actions and scenes is poorer, suggesting difficulty in binding this information together. Sleep can enhance declarative memory of information, but whether sleep can also boost memory for binding information and whether the effect is general across different types of information is not yet known. Experiments 1 to 3 tested effects of sleep on binding actions and scenes, and Experiments 4 and 5 tested binding of objects and scenes. Participants viewed composites and were tested 12-hours later after a delay consisting of sleep (9pm-9am) or wake (9am-9pm), on an alternative forced choice recognition task. For action-scene composites, memory was relatively poor with no significant effect of sleep. For object-scene composites sleep did improve memory. Sleep can promote binding in memory, depending on the type of information to be combined.

scholarly journals How the Multiplicity of Suggested Information Affects the Behavior of a User in a Recommender System

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 741
Author(s):  
Yuseok Ban ◽  
Kyungjae Lee
Keyword(s):  
High Precision ◽  
Recommender System ◽  
Visual Information ◽  
Experimental Results ◽  
Supplementary Information ◽  
Digital Platform ◽  
Different Types ◽  
Types Of Information

Many researchers have suggested improving the retention of a user in the digital platform using a recommender system. Recent studies show that there are many potential ways to assist users to find interesting items, other than high-precision rating predictions. In this paper, we study how the diverse types of information suggested to a user can influence their behavior. The types have been divided into visual information, evaluative information, categorial information, and narrational information. Based on our experimental results, we analyze how different types of supplementary information affect the performance of a recommender in terms of encouraging users to click more items or spend more time in the digital platform.

scholarly journals Transcriptional and morphological profiling of parvalbumin interneuron subpopulations in the mouse hippocampus

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lin Que ◽  
David Lukacsovich ◽  
Wenshu Luo ◽  
Csaba Földy
Keyword(s):  
Large Scale ◽  
Cell Types ◽  
Rna Seq ◽  
Neuronal Identity ◽  
Parvalbumin Interneurons ◽  
Different Types ◽  
Parvalbumin Interneuron ◽  
Cam Profile ◽  
Developmental Domains

AbstractThe diversity reflected by >100 different neural cell types fundamentally contributes to brain function and a central idea is that neuronal identity can be inferred from genetic information. Recent large-scale transcriptomic assays seem to confirm this hypothesis, but a lack of morphological information has limited the identification of several known cell types. In this study, we used single-cell RNA-seq in morphologically identified parvalbumin interneurons (PV-INs), and studied their transcriptomic states in the morphological, physiological, and developmental domains. Overall, we find high transcriptomic similarity among PV-INs, with few genes showing divergent expression between morphologically different types. Furthermore, PV-INs show a uniform synaptic cell adhesion molecule (CAM) profile, suggesting that CAM expression in mature PV cells does not reflect wiring specificity after development. Together, our results suggest that while PV-INs differ in anatomy and in vivo activity, their continuous transcriptomic and homogenous biophysical landscapes are not predictive of these distinct identities.

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