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Probabilistic cell typing enables fine mapping of closely related cell types in situ.

ABSTRACT: Understanding the function of a tissue requires knowing the spatial organization of its constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patterns that define its many, closely related neuronal types, but cannot reveal their spatial arrangement. Here we introduce probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages previous scRNA-seq classification to identify cell types using multiplexed in situ RNA detection. We applied this method by mapping the inhibitory neurons of mouse hippocampal area CA1, for which ground truth is available from extensive previous work identifying their laminar organization. Our method identified these neuronal classes in a spatial arrangement matching ground truth, and further identified multiple classes of isocortical pyramidal cell in a pattern matching their known organization. This method will allow identifying the spatial organization of closely related cell types across the brain and other tissues.

SUBMITTER: Qian X

PROVIDER: S-EPMC6949128 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Probabilistic cell typing enables fine mapping of closely related cell types in situ.

Qian Xiaoyan X Harris Kenneth D KD Hauling Thomas T Nicoloutsopoulos Dimitris D Muñoz-Manchado Ana B AB Skene Nathan N Hjerling-Leffler Jens J Nilsson Mats M

Nature methods 20191118 1

Understanding the function of a tissue requires knowing the spatial organization of its constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patterns that define its many, closely related neuronal types, but cannot reveal their spatial arrangement. Here we introduce probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages previous scRNA-seq classification to identify cell types using multiplexed ...[more]

PMID: 31740815

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Project description:To identify evolutionarily conserved features of replication timing and their relationship to epigenetic properties, we profiled replication timing genome-wide in four human embryonic stem cell (hESC) lines, hESC-derived neural precursor cells (NPCs), lymphoblastoid cells, and two human induced pluripotent stem cell lines (hiPSCs), and compared them with related mouse cell types. Results confirm the conservation of coordinately replicated megabase-sized "replication domains" punctuated by origin-suppressed regions. Differentiation-induced replication timing changes in both species occur in 400- to 800-kb units and are similarly coordinated with transcription changes. A surprising degree of cell-type-specific conservation in replication timing was observed across regions of conserved synteny, despite considerable species variation in the alignment of replication timing to isochore GC/LINE-1 content. Notably, hESC replication timing profiles were significantly more aligned to mouse epiblast-derived stem cells (mEpiSCs) than to mouse ESCs. Comparison with epigenetic marks revealed a signature of chromatin modifications at the boundaries of early replicating domains and a remarkably strong link between replication timing and spatial proximity of chromatin as measured by Hi-C analysis. Thus, early and late initiation of replication occurs in spatially separate nuclear compartments, but rarely within the intervening chromatin. Moreover, cell-type-specific conservation of the replication program implies conserved developmental changes in spatial organization of chromatin. Together, our results reveal evolutionarily conserved aspects of developmentally regulated replication programs in mammals, demonstrate the power of replication profiling to distinguish closely related cell types, and strongly support the hypothesis that replication timing domains are spatially compartmentalized structural and functional units of three-dimensional chromosomal architecture.

Dataset Information

Probabilistic cell typing enables fine mapping of closely related cell types in situ.

Publications

Probabilistic cell typing enables fine mapping of closely related cell types in situ.

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