Autonomous combinatorial color barcoding for multiplexing single molecule RNA visualization

Cheng, Yong; Zhuo, Yehong; Hartmann, Katharina; Zou, Ping; Bekki, Gözde; Alter, Heike; Liu, Hai-Kun

doi:10.1101/127373

Cited by 6 publications

(8 citation statements)

References 17 publications

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“…To reduce tissue autofluorescence, sections were embedded in polyacrylamide gel, RNAs were anchored to the gel by LabelX treatment, and cellular proteins and lipids were cleared as previously described 89,90 , with modifications. LabelX solution was prepared by reacting Label-IT (Mirus Bio) with Acryloyl X – SE (Thermo Fischer Scientific) as described by Chen and colleagues 90 . Specifically, sections were air dried for 15-20 min and fixed in 3.7% paraformaldehyde in PBS for 10-15 min, followed by a 2 min incubation in 4% SDS in PBS and washes with PBS.…”

Section: Methodsmentioning

confidence: 99%

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Reconstructing the ancestral vertebrate brain using a lamprey neural cell type atlas

Lamanna

Hervas-Sotomayor

Oel

et al. 2022

Preprint

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The vertebrate brain emerged more than ~500 million years ago in common evolutionary ancestors. To systematically trace its cellular and molecular origins, we established a spatially resolved cell type atlas of the entire brain of the sea lamprey - a jawless species whose phylogenetic position affords the reconstruction of ancestral vertebrate traits - based on extensive single-cell RNA-seq and in situ sequencing data. Comparisons of this atlas to neural data from the mouse and other jawed vertebrates unveiled various shared features that enabled the reconstruction of the core cell type composition, tissue structures, and gene expression programs of the ancestral brain. However, our analyses also revealed key tissues and cell types that arose later in evolution. For example, the ancestral vertebrate brain was likely devoid of cerebellar cell types and oligodendrocytes (myelinating cells); our data suggest that the latter emerged from astrocyte-like evolutionary precursors on the jawed vertebrate lineage. Our work illuminates the cellular and molecular architecture of the ancestral vertebrate brain and provides a foundation for exploring its diversification during evolution.

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Section: Methodsmentioning

confidence: 99%

Section: Single Molecule Rna-fishmentioning

confidence: 99%

Reconstructing the ancestral vertebrate brain using a lamprey neural cell type atlas

Lamanna

Hervas-Sotomayor

Oel

et al. 2022

Preprint

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“…who used a panel of zonated landmark genes with smFISH to remap the single-cell transcriptomes of mouse liver cells to the zonation profile. Other approaches are osmFISH or huluFISH 81 , 82 . Techniques for direct in situ transcriptomics have also been described (e.g.…”

Section: Future Technical Improvementsmentioning

confidence: 99%

Heterogeneity and plasticity in healthy and atherosclerotic vasculature explored by single-cell sequencing

Kuijk¹,

Kuppe

Betsholtz

et al. 2019

Cardiovascular Research

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Cellular characteristics and their adjustment to a state of disease have become more evident due to recent advances in imaging, fluorescent reporter mice, and whole genome RNA sequencing. The uncovered cellular heterogeneity and/or plasticity potentially complicates experimental studies and clinical applications, as markers derived from whole tissue ‘bulk’ sequencing is unable to yield a subtype transcriptome and specific markers. Here, we propose definitions on heterogeneity and plasticity, discuss current knowledge thereof in the vasculature and how this may be improved by single-cell sequencing (SCS). SCS is emerging as an emerging technique, enabling researchers to investigate different cell populations in more depth than ever before. Cell selection methods, e.g. flow assisted cell sorting, and the quantity of cells can influence the choice of SCS method. Smart-Seq2 offers sequencing of the complete mRNA molecule on a low quantity of cells, while Drop-seq is possible on large numbers of cells on a more superficial level. SCS has given more insight in heterogeneity in healthy vasculature, where it revealed that zonation is crucial in gene expression profiles among the anatomical axis. In diseased vasculature, this heterogeneity seems even more prominent with discovery of new immune subsets in atherosclerosis as proof. Vascular smooth muscle cells and mesenchymal cells also share these plastic characteristics with the ability to up-regulate markers linked to stem cells, such as Sca-1 or CD34. Current SCS studies show some limitations to the number of replicates, quantity of cells used, or the loss of spatial information. Bioinformatical tools could give some more insight in current datasets, making use of pseudo-time analysis or RNA velocity to investigate cell differentiation or polarization. In this review, we discuss the use of SCS in unravelling heterogeneity in the vasculature, its current limitations and promising future applications.

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“… 10 Recently, a multitude of methods for single‐mRNA visualization and quantification (smFISH) are available. 11 , 12 , 13 A consistent problem for inter‐sample comparability is that smFISH highly depends on preparation‐dependent RNA integrity. This is due to delays between sampling and fixation, fixation time, quality of the fixative and paraffin‐embedding.…”

Section: Introductionmentioning

confidence: 99%

ScoMorphoFISH: A deep learning enabled toolbox for single‐cell single‐mRNA quantification and correlative (ultra‐)morphometry

Siegerist

Hay

Dikou

et al. 2022

J Cellular Molecular Medi

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Increasing the information depth of single kidney biopsies can improve diagnostic precision, personalized medicine and accelerate basic kidney research. Until now, information on mRNA abundance and morphologic analysis has been obtained from different samples, missing out on the spatial context and single‐cell correlation of findings. Herein, we present scoMorphoFISH, a modular toolbox to obtain spatial single‐cell single‐mRNA expression data from routinely generated kidney biopsies. Deep learning was used to virtually dissect tissue sections in tissue compartments and cell types to which single‐cell expression data were assigned. Furthermore, we show correlative and spatial single‐cell expression quantification with super‐resolved podocyte foot process morphometry. In contrast to bulk analysis methods, this approach will help to identify local transcription changes even in less frequent kidney cell types on a spatial single‐cell level with single‐mRNA resolution. Using this method, we demonstrate that ACE2 can be locally upregulated in podocytes upon injury. In a patient suffering from COVID‐19‐associated collapsing FSGS, ACE2 expression levels were correlated with intracellular SARS‐CoV‐2 abundance. As this method performs well with standard formalin‐fixed paraffin‐embedded samples and we provide pretrained deep learning networks embedded in a comprehensive image analysis workflow, this method can be applied immediately in a variety of settings.

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Autonomous combinatorial color barcoding for multiplexing single molecule RNA visualization

Cited by 6 publications

References 17 publications

Reconstructing the ancestral vertebrate brain using a lamprey neural cell type atlas

Reconstructing the ancestral vertebrate brain using a lamprey neural cell type atlas

Heterogeneity and plasticity in healthy and atherosclerotic vasculature explored by single-cell sequencing

ScoMorphoFISH: A deep learning enabled toolbox for single‐cell single‐mRNA quantification and correlative (ultra‐)morphometry

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