scholarly journals Alternative TSSs are co‐regulated in single cells in the mouse brain

2017 ◽  
Vol 13 (5) ◽  
pp. 930 ◽  
Author(s):  
Kasper Karlsson ◽  
Peter Lönnerberg ◽  
Sten Linnarsson
2005 ◽  
Vol 55 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Chris Heyn ◽  
John A. Ronald ◽  
Lisa T. Mackenzie ◽  
Ian C. MacDonald ◽  
Ann F. Chambers ◽  
...  

2020 ◽  
Vol 1 (3) ◽  
pp. 100215
Author(s):  
Susanne Laukoter ◽  
Nicole Amberg ◽  
Florian M. Pauler ◽  
Simon Hippenmeyer

2017 ◽  
Author(s):  
Aparna Bhaduri ◽  
Tomasz J. Nowakowski ◽  
Alex A. Pollen ◽  
Arnold R. Kriegstein

AbstractHigh throughput methods for profiling the transcriptomes of single cells have recently emerged as transformative approaches for large-scale population surveys of cellular diversity in heterogeneous primary tissues. Efficient generation of such an atlas will depend on sufficient sampling of the diverse cell types while remaining cost-effective to enable a comprehensive examination of organs, developmental stages, and individuals. To examine the relationship between cell number and transcriptional heterogeneity in the context of unbiased cell type classification, we explicitly explored the population structure of a publically available 1.3 million cell dataset from the E18.5 mouse brain. We propose a computational framework for inferring the saturation point of cluster discovery in a single cell mRNA-seq experiment, centered around cluster preservation in downsampled datasets. In addition, we introduce a “complexity index”, which characterizes the heterogeneity of cells in a given dataset. Using Cajal-Retzius cells as an example of a limited complexity dataset, we explored whether biological distinctions relate to technical clustering. Surprisingly, we found that clustering distinctions carrying biologically interpretable meaning are achieved with far fewer cells (20,000). Together, these findings suggest that most of the biologically interpretable insights from the 1.3 million cells can be recapitulated by analyzing 50,000 randomly selected cells, indicating that instead of profiling few individuals at high “cellular coverage”, the much anticipated cell atlasing studies may instead benefit from profiling more individuals, or many time points at lower cellular coverage.Recent efforts seek to create a comprehensive cell atlas of the human body1,2 Current technology, however, makes it precipitously expensive to perform analysis of every cell. Therefore, designing effective sampling strategies be critical to generate a working atlas in an efficient, cost-effective, and streamlined manner. The advent of single cell and single nucleus mRNA sequencing (RNAseq) in droplet format3,4 now enables large scale sampling of cells from any tissue, and a recently released publicly available dataset of 1.3 million single cells from the E18.5 mouse brain generated with the 10X Chromium5 provides an opportunity to explore the relationship between population structure and the number of sampled cells necessary to reveal the underlying diversity of cell types. Here, we present a framework for how researchers can evaluate whether a dataset has reached saturation, and we estimate how many cells would be required to generate an atlas of the sample analyzed here. This framework can be applied to any organ or cell type specific atlas for any organism.


2021 ◽  
Author(s):  
Margareth Nogueira ◽  
Daiane CF Golbert ◽  
Richardson Leão

Laser Capture Microdissection (LCM) is a method that allows to select and dissecting specific structures, cell populations, or even single cells from different types of tissue to extract DNA, RNA, or proteins. It is easy to perform and precise, avoiding unwanted signals from irrelevant cells, because gene expression may be affected by a bulk of heterogeneous material in a sample. However, despite its efficiency, several steps can affect the sample RNA integrity. In comparison to DNA, RNA is a much more unstable molecule and represents a challenge in the LCM method. Here we describe an optimized protocol to provide good concentration and high-quality RNA in specific structures, such as Dentate Gyrus and CA1 in the hippocampus, basolateral amygdala and anterior cingulate cortex of mouse brain tissue.


2020 ◽  
Author(s):  
Tommaso Biancalani ◽  
Gabriele Scalia ◽  
Lorenzo Buffoni ◽  
Raghav Avasthi ◽  
Ziqing Lu ◽  
...  

Charting a biological atlas of an organ, such as the brain, requires us to spatially-resolve whole transcriptomes of single cells, and to relate such cellular features to the histological and anatomical scales. Single-cell and single-nucleus RNA-Seq (sc/snRNA-seq) can map cells comprehensively5,6, but relating those to their histological and anatomical positions in the context of an organ’s common coordinate framework remains a major challenge and barrier to the construction of a cell atlas7–10. Conversely, Spatial Transcriptomics allows for in-situ measurements11–13 at the histological level, but at lower spatial resolution and with limited sensitivity. Targeted in situ technologies1–3 solve both issues, but are limited in gene throughput which impedes profiling of the entire transcriptome. Finally, as samples are collected for profiling, their registration to anatomical atlases often require human supervision, which is a major obstacle to build pipelines at scale. Here, we demonstrate spatial mapping of cells, histology, and anatomy in the somatomotor area and the visual area of the healthy adult mouse brain. We devise Tangram, a method that aligns snRNA-seq data to various forms of spatial data collected from the same brain region, including MERFISH1, STARmap2, smFISH3, and Spatial Transcriptomics4 (Visium), as well as histological images and public atlases. Tangram can map any type of sc/snRNA-seq data, including multi-modal data such as SHARE-seq data5, which we used to reveal spatial patterns of chromatin accessibility. We equipped Tangram with a deep learning computer vision pipeline, which allows for automatic identification of anatomical annotations on histological images of mouse brain. By doing so, Tangram reconstructs a genome-wide, anatomically-integrated, spatial map of the visual and somatomotor area with ∼30,000 genes at single-cell resolution, revealing spatial gene expression and chromatin accessibility patterning beyond current limitation of in-situ technologies.


2021 ◽  
Vol 18 (3) ◽  
pp. 283-292 ◽  
Author(s):  
Chenxu Zhu ◽  
Yanxiao Zhang ◽  
Yang Eric Li ◽  
Jacinta Lucero ◽  
M. Margarita Behrens ◽  
...  

2020 ◽  
Author(s):  
Jiawei Huang ◽  
Daifeng Wang

AbstractRecent single-cell multi-modal data reveal different characteristics of single cells, such as transcriptomics, morphology, and electrophysiology. However, our understanding of functional genomics and gene regulation leading to the cellular characteristics remains elusive. To address this, we used emerging manifold learning to align gene expression and electrophysiological data of single neuronal cells in the mouse brain. After manifold alignment, the cell clusters highly correspond to transcriptomic and morphological cell-types, suggesting a strong nonlinear linkage between gene expression and electrophysiology at the cell-type level. Additional functional enrichment and gene regulatory network analyses revealed potential novel molecular mechanistic insights from genes to electrophysiology at cellular resolution.


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