scholarly journals A spatially resolved brain region- and cell type-specific isoform atlas of the postnatal mouse brain

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Anoushka Joglekar ◽  
Andrey Prjibelski ◽  
Ahmed Mahfouz ◽  
Paul Collier ◽  
Susan Lin ◽  
...  
Keyword(s):  
Single Cell ◽  
Brain Region ◽  
Cell Types ◽  
Brain Regions ◽  
Protein Isoforms ◽  
Cell Type ◽  
Anatomic Structure ◽  
Spatially Resolved ◽  
Long Read ◽  
Isoform Expression

AbstractSplicing varies across brain regions, but the single-cell resolution of regional variation is unclear. We present a single-cell investigation of differential isoform expression (DIE) between brain regions using single-cell long-read sequencing in mouse hippocampus and prefrontal cortex in 45 cell types at postnatal day 7 (www.isoformAtlas.com). Isoform tests for DIE show better performance than exon tests. We detect hundreds of DIE events traceable to cell types, often corresponding to functionally distinct protein isoforms. Mostly, one cell type is responsible for brain-region specific DIE. However, for fewer genes, multiple cell types influence DIE. Thus, regional identity can, although rarely, override cell-type specificity. Cell types indigenous to one anatomic structure display distinctive DIE, e.g. the choroid plexus epithelium manifests distinct transcription-start-site usage. Spatial transcriptomics and long-read sequencing yield a spatially resolved splicing map. Our methods quantify isoform expression with cell-type and spatial resolution and it contributes to further our understanding of how the brain integrates molecular and cellular complexity.

scholarly journals Cell-type, single-cell, and spatial signatures of brain-region specific splicing in postnatal development

2020 ◽  
Author(s):  
Anoushka Joglekar ◽  
Andrey Prjibelski ◽  
Ahmed Mahfouz ◽  
Paul Collier ◽  
Susan Lin ◽  
...  
Keyword(s):  
Single Cell ◽  
Brain Region ◽  
Cell Types ◽  
Brain Regions ◽  
Specific Cell ◽  
Cell Type ◽  
Anatomic Structure ◽  
Link Type ◽  
Long Read ◽  
Isoform Expression

AbstractAlternative RNA splicing varies across brain regions, but the single-cell resolution of such regional variation is unknown. Here we present the first single-cell investigation of differential isoform expression (DIE) between brain regions, by performing single cell long-read transcriptome sequencing in the mouse hippocampus and prefrontal cortex in 45 cell types at postnatal day 7 (www.isoformAtlas.com). Using isoform tests for brain-region specific DIE, which outperform exon-based tests, we detect hundreds of brain-region specific DIE events traceable to specific cell-types. Many DIE events correspond to functionally distinct protein isoforms, some with just a 6-nucleotide exon variant. In most instances, one cell type is responsible for brain-region specific DIE. Cell types indigenous to only one anatomic structure display distinctive DIE, where for example, the choroid plexus epithelium manifest unique transcription start sites. However, for some genes, multiple cell-types are responsible for DIE in bulk data, indicating that regional identity can, although less frequently, override cell-type specificity. We validated our findings with spatial transcriptomics and long-read sequencing, yielding the first spatially resolved splicing map in the postnatal mouse brain (www.isoformAtlas.com). Our methods are highly generalizable. They provide a robust means of quantifying isoform expression with cell-type and spatial resolution, and reveal how the brain integrates molecular and cellular complexity to serve function.

scholarly journals A multimodal cell census and atlas of the mammalian primary motor cortex

2020 ◽  
Author(s):  
◽  
Ricky S. Adkins ◽  
Andrew I. Aldridge ◽  
Shona Allen ◽  
Seth A. Ament ◽  
...  
Keyword(s):  
Motor Cortex ◽  
Single Cell ◽  
Primary Motor Cortex ◽  
Spatial Information ◽  
Molecular Genetic ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Open Chromatin ◽  
Cell Type ◽  
Spatially Resolved

ABSTRACTWe report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.

scholarly journals FR-Match: Robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test

2020 ◽  
Author(s):  
Yun Zhang ◽  
Brian D. Aevermann ◽  
Trygve E. Bakken ◽  
Jeremy A. Miller ◽  
Rebecca D. Hodge ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
R Package ◽  
Brain Regions ◽  
Cortical Layer ◽  
Middle Temporal Gyrus ◽  
Cell Type ◽  
Sequencing Data

AbstractSingle cell/nucleus RNA sequencing (scRNAseq) is emerging as an essential tool to unravel the phenotypic heterogeneity of cells in complex biological systems. While computational methods for scRNAseq cell type clustering have advanced, the ability to integrate datasets to identify common and novel cell types across experiments remains a challenge. Here, we introduce a cluster-to-cluster cell type matching method – FR-Match – that utilizes supervised feature selection for dimensionality reduction and incorporates shared information among cells to determine whether two cell type clusters share the same underlying multivariate gene expression distribution. FR-Match is benchmarked with existing cell-to-cell and cell-to-cluster cell type matching methods using both simulated and real scRNAseq data. FR-Match proved to be a stringent method that produced fewer erroneous matches of distinct cell subtypes and had the unique ability to identify novel cell phenotypes in new datasets. In silico validation demonstrated that the proposed workflow is the only self-contained algorithm that was robust to increasing numbers of true negatives (i.e. non-represented cell types). FR-Match was applied to two human brain scRNAseq datasets sampled from cortical layer 1 and full thickness middle temporal gyrus. When mapping cell types identified in specimens isolated from these overlapping human brain regions, FR-Match precisely recapitulated the laminar characteristics of matched cell type clusters, reflecting their distinct neuroanatomical distributions. An R package and Shiny application are provided at https://github.com/JCVenterInstitute/FRmatch for users to interactively explore and match scRNAseq cell type clusters with complementary visualization tools.

scholarly journals Mapping and modeling the genomic basis of differential RNA isoform expression at single-cell resolution with LR-Split-seq

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Elisabeth Rebboah ◽  
Fairlie Reese ◽  
Katherine Williams ◽  
Gabriela Balderrama-Gutierrez ◽  
Cassandra McGill ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Transcription Start Sites ◽  
Design Flexibility ◽  
Long Reads ◽  
Internal Exon ◽  
Long Read ◽  
Isoform Expression ◽  
Full Length Transcript

AbstractThe rise in throughput and quality of long-read sequencing should allow unambiguous identification of full-length transcript isoforms. However, its application to single-cell RNA-seq has been limited by throughput and expense. Here we develop and characterize long-read Split-seq (LR-Split-seq), which uses combinatorial barcoding to sequence single cells with long reads. Applied to the C2C12 myogenic system, LR-split-seq associates isoforms to cell types with relative economy and design flexibility. We find widespread evidence of changing isoform expression during differentiation including alternative transcription start sites (TSS) and/or alternative internal exon usage. LR-Split-seq provides an affordable method for identifying cluster-specific isoforms in single cells.

scholarly journals Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain by scGESTALT

2017 ◽  
Author(s):  
Bushra Raj ◽  
Daniel E. Wagner ◽  
Aaron McKenna ◽  
Shristi Pandey ◽  
Allon M. Klein ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Brain Regions ◽  
Marker Genes ◽  
Cell Type ◽  
Zebrafish Brain ◽  
Vertebrate Brain ◽  
Single Cell Rna Sequencing ◽  
Single Cell Profiling ◽  
Lineage Trees

ABSTRACTHundreds of cell types are generated during development, but their lineage relationships are largely elusive. Here we report a technology, scGESTALT, which combines cell type identification by single-cell RNA sequencing with lineage recording by cumulative barcode editing. We sequenced ~60,000 transcriptomes from the juvenile zebrafish brain and identified more than 100 cell types and marker genes. We engineered an inducible system that combines early and late barcode editing and isolated thousands of single-cell transcriptomes and their associated barcodes. The large diversity of edited barcodes and cell types enabled the generation of lineage trees with hundreds of branches. Inspection of lineage trajectories identified restrictions at the level of cell types and brain regions and helped uncover gene expression cascades during differentiation. These results establish scGESTALT as a new and widely applicable tool to simultaneously characterize the molecular identities and lineage histories of thousands of cells during development and disease.

scholarly journals Single-cell deconvolution of 3,000 post-mortem brain samples for eQTL and GWAS dissection in mental disorders

2021 ◽  
Author(s):  
Yongjin Park ◽  
Liang He ◽  
Jose Davila-Velderrain ◽  
Lei Hou ◽  
Shahin Mohammadi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Genetic Variants ◽  
Cell Types ◽  
Brain Regions ◽  
Post Mortem ◽  
Rna Seq ◽  
Cell Type ◽  
Post Mortem Brain ◽  
Cell Type Specific

AbstractThousands of genetic variants acting in multiple cell types underlie complex disorders, yet most gene expression studies profile only bulk tissues, making it hard to resolve where genetic and non-genetic contributors act. This is particularly important for psychiatric and neurodegenerative disorders that impact multiple brain cell types with highly-distinct gene expression patterns and proportions. To address this challenge, we develop a new framework, SPLITR, that integrates single-nucleus and bulk RNA-seq data, enabling phenotype-aware deconvolution and correcting for systematic discrepancies between bulk and single-cell data. We deconvolved 3,387 post-mortem brain samples across 1,127 individuals and in multiple brain regions. We find that cell proportion varies across brain regions, individuals, disease status, and genotype, including genetic variants in TMEM106B that impact inhibitory neuron fraction and 4,757 cell-type-specific eQTLs. Our results demonstrate the power of jointly analyzing bulk and single-cell RNA-seq to provide insights into cell-type-specific mechanisms for complex brain disorders.

scholarly journals Single-cell isoform RNA sequencing (ScISOr-Seq) across thousands of cells reveals isoforms of cerebellar cell types

2018 ◽  
Author(s):  
Ishaan Gupta ◽  
Paul G Collier ◽  
Bettina Haase ◽  
Ahmed Mahfouz ◽  
Anoushka Joglekar ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
Full Length ◽  
Cell Of Origin ◽  
Cell Type ◽  
Long Reads ◽  
Long Read ◽  
The Individual ◽  
Bulk Tissue

AbstractFull-length isoform sequencing has advanced our knowledge of isoform biology1–11. However, apart from applying full-length isoform sequencing to very few single cells12,13, isoform sequencing has been limited to bulk tissue, cell lines, or sorted cells. Single splicing events have been described for <=200 single cells with great statistical success14,15, but these methods do not describe full-length mRNAs. Single cell short-read 3’ sequencing has allowed identification of many cell sub-types16–23, but full-length isoforms for these cell types have not been profiled. Using our new method of single-cell-isoform-RNA-sequencing (ScISOr-Seq) we determine isoform-expression in thousands of individual cells from a heterogeneous bulk tissue (cerebellum), without specific antibody-fluorescence activated cell sorting. We elucidate isoform usage in high-level cell types such as neurons, astrocytes and microglia and finer sub-types, such as Purkinje cells and Granule cells, including the combination patterns of distant splice sites6–9,24,25, which for individual molecules requires long reads. We produce an enhanced genome annotation revealing cell-type specific expression of known and 16,872 novel (with respect to mouse Gencode version 10) isoforms (see isoformatlas.com).ScISOr-Seq describes isoforms from >1,000 single cells from bulk tissue without cell sorting by leveraging two technologies in three steps: In step one, we employ microfluidics to produce amplified full-length cDNAs barcoded for their cell of origin. This cDNA is split into two pools: one pool for 3’ sequencing to measure gene expression (step 2) and another pool for long-read sequencing and isoform expression (step 3). In step two, short-read 3’-sequencing provides molecular counts for each gene and cell, which allows clustering cells and assigning a cell type using cell-type specific markers. In step three, an aliquot of the same cDNAs (each barcoded for the individual cell of origin) is sequenced using Pacific Biosciences (“PacBio”)1,2,4,5,26 or Oxford Nanopore3. Since these long reads carry the single-cell barcodes identified in step two, one can determine the individual cell from which each long read originates. Since most single cells are assigned to a named cluster, we can also assign the cell’s cluster name (e.g. “Purkinje cell” or “astrocyte”) to the long read in question (Fig 1A) – without losing the cell of origin of each long read.

scholarly journals Spatially mapped single-cell chromatin accessibility

2019 ◽  
Author(s):  
Casey A. Thornton ◽  
Ryan M. Mulqueen ◽  
Andrew Nishida ◽  
Kristof A. Torkenczy ◽  
Eve G. Lowenstein ◽  
...  
Keyword(s):  
Single Cell ◽  
Spatial Information ◽  
Three Dimensional ◽  
Cell Types ◽  
Chromatin Accessibility ◽  
Artery Occlusion ◽  
Cell Type ◽  
Cell Type Specificity ◽  
Spatially Resolved ◽  
Typical Experiment

AbstractHigh-throughput single-cell epigenomic assays can resolve the heterogeneity of cell types and states in complex tissues, however, spatial orientation within the network of interconnected cells is lost. Here, we present a novel method for highly scalable, spatially resolved, single-cell profiling of chromatin states. We use high-density multiregional sampling to perform single-cell combinatorial indexing on Microbiopsies Assigned to Positions for the Assay for Transposase Accessible Chromatin (sciMAP-ATAC) to produce single-cell data of an equivalent quality to non-spatially resolved single-cell ATAC-seq, where each cell is localized to a three-dimensional position within the tissue. A typical experiment comprises between 96 and 384 spatially mapped tissue positions, each producing 10s to over 100 individual single-cell ATAC-seq profiles, and a typical resolution of 214 cubic microns; with the ability to tune the resolution and cell throughput to suit each target application. We apply sciMAP-ATAC to the adult mouse primary somatosensory cortex, where we profile cortical lamination and demonstrate the ability to analyze data from a single tissue position or compare a single cell type in adjacent positions. We also profile the human primary visual cortex, where we produce spatial trajectories through the cortex. Finally, we characterize the spatially progressive nature of cerebral ischemic infarct in the mouse brain using a model of transient middle cerebral artery occlusion. We leverage the spatial information to identify novel and known transcription factor activities that vary by proximity to the ischemic infarction core with cell type specificity.

scholarly journals Single-cell analysis of Foxp1-driven mechanisms essential for striatal development

2019 ◽  
Author(s):  
Ashley G. Anderson ◽  
Ashwinikumar Kulkarni ◽  
Matthew Harper ◽  
Genevieve Konopka
Keyword(s):  
Single Cell ◽  
Target Genes ◽  
Single Cell Analysis ◽  
Cell Types ◽  
Brain Regions ◽  
Projection Neurons ◽  
Loss Of Function ◽  
Cell Type ◽  
Motor Information ◽  
Cell Type Specific

AbstractThe striatum is a critical forebrain structure for integrating cognitive, sensory, and motor information from diverse brain regions into meaningful behavioral output. However, the transcriptional mechanisms that underlie striatal development and organization at single-cell resolution remain unknown. Here, we show that Foxp1, a transcription factor strongly linked to autism and intellectual disability, regulates organizational features of striatal circuitry in a cell-type-dependent fashion. Using single-cell RNA-sequencing, we examine the cellular diversity of the early postnatal striatum and find that cell-type-specific deletion ofFoxp1in striatal projection neurons alters the cellular composition and neurochemical architecture of the striatum. Importantly, using this approach, we identify the non-cell autonomous effects produced by disruptingFoxp1in one cell-type and the molecular compensation that occurs in other populations. Finally, we identify Foxp1-regulated target genes within distinct cell-types and connect these molecular changes to functional and behavioral deficits relevant to phenotypes described in patients withFOXP1loss-of-function mutations. These data reveal cell-type-specific transcriptional mechanisms underlying distinct features of striatal circuitry and identify Foxp1 as a key regulator of striatal development.

scholarly journals Comprehensive single-cell gene and isoform expression analysis reveals signatures of ageing in haematopoietic stem and progenitor cells

2021 ◽  
Author(s):  
Laura Mincarelli ◽  
Vladimir Uzun ◽  
David Wright ◽  
Stuart Rushworth ◽  
Wilfried Haerty ◽  
...  
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Progenitor Cells ◽  
Cell Type ◽  
Aberrant Expression ◽  
Hematopoietic Stem ◽  
Long Read ◽  
Stem And Progenitor Cells ◽  
Cell Type Specific ◽  
Isoform Expression

Abstract Single-cell approaches have revealed that the haematopoietic hierarchy is a continuum of differentiation, from stem cell to committed progenitor, marked by changes in gene expression. However, many of these approaches neglect isoform level information, and thus do not capture the extent and effect of alternative splicing within the system. Here, we present the first integrated short- and long-read single-cell RNA-seq of haematopoietic stem and progenitor cells. We demonstrate that over half of genes detected in standard short-read single-cell analyses are expressed as multiple, often functionally distinct, isoforms. This includes many transcription factors and key cytokine receptors, and in particular the Thrombopoietin receptor Mpl, which displays complex isoform expression patterns between individual hematopoietic stem cells. The dataset further reveals novel signatures of hematopoietic ageing, including a global increase in lncRNA expression. Strikingly, the long-read sequencing enables us to observe aberrant expression of full-length VJ-rearranged immunoglobulin kappa transcripts in aged haematopoietic stem cells, prior to lymphoid commitment. Integrating single cell and cell-type specific isoform landscape in normal and aged hematopoiesis provides a new reference for accurate molecular profiling of heterogeneous tissues, as well as novel insights into transcriptional complexity, cell-type specific splicing events and effects of ageing.

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