Spatially mapped single-cell chromatin accessibility

Thornton, Casey; Mulqueen, Ryan M.; Torkenczy, Kristof A.; Nishida, Andrew; Lowenstein, Eve G.; Fields, Andrew J.; Steemers, Frank J.; Zhang, Wenri; McConnell, Heather L.; Woltjer, Randy; Mishra, Anusha; Wright, Kevin M.; Adey, Andrew

doi:10.1038/s41467-021-21515-7

Cited by 60 publications

(44 citation statements)

References 78 publications

(72 reference statements)

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“…This is particularity intriguing in cases for which there is no assay for spatially resolving data of a certain modality. For example, chromatin accessibility could not be spatially resolved at the single-cell level until very recently 44 .…”

Section: Discussionmentioning

confidence: 99%

Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram

et al. 2021

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Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. Tangram can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.

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Section: Discussionmentioning

confidence: 99%

Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram

et al. 2021

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“…Clear separation of cell types was observed using marker gene signal and differential accessibility profiles ( Figure 1h , Supplementary File 2 , Supplementary Figure 1a ) 15 , 20 . Finally, we assessed any systematic bias that may affect biological interpretation by integrating our data sets with snATAC, 10X Genomics scATAC, dscATAC and sciMAP-ATAC ( Figure 2i ) 13 – 15 , 21 . We observed that our libraries readily integrate across platforms, maintaining cell type discrimination between clusters.…”

Section: Resultsmentioning

confidence: 99%

“…Welch’s two-sample T test comparisons between unique reads per cell were calculated with the t.test function in base R for a one-sided alternative hypothesis. For peak overlap comparison, we added an additional sci-ATAC low sequencing effort data set 21 performed on adult mouse flash frozen brain tissue. We then counted unique peak overlaps between data sets and plotted as stacked bar plots via ggplot geom_bar function ( Figure 2g ).…”

Section: Methodsmentioning

confidence: 99%

“…For assessment on sequencing effort necessary to reach a median unique reads/cell threshold, we compared our s3ATAC mouse data to the publically available sci-ATAC matched sample data 21 . We randomly subsampled the bam files pre-deduplication and calculated per cellID library complexity, as described above 10 , 21 . The resulting model was plotted with geom_line .…”

Section: Methodsmentioning

confidence: 99%

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High-content single-cell combinatorial indexing

Mulqueen

Pokholok²,

O’Connell

et al. 2021

Nat Biotechnol

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Single-cell combinatorial indexing (sci) with transposase-based library construction increases the throughput of single-cell genomics assays but produces sparse coverage in terms of usable reads per cell. We develop symmetrical strand sci (‘s3’), a uracil-based adapter switching approach that improves the rate of conversion of source DNA into viable sequencing library fragments following tagmentation. We apply this chemistry to assay chromatin accessibility (s3-ATAC) in human cortical and mouse whole brain tissues, with mouse datasets demonstrating a 6-to-13-fold improvement in usable reads per cell compared with other available methods. Application of s3 to single-cell whole genome sequencing (s3-WGS) and to whole genome plus chromatin conformation (s3-GCC) yields 148 and 14.8 fold improvements, respectively, in usable reads per cell compared with sci-DNA-seq and sci-HiC. We show that s3-WGS and s3-GCC resolve subclonal genomic alterations in patient-derived pancreatic cancer cell lines. We expect that the s3 platform will be compatible with other transposase-based techniques, including sci-MET or CUT&Tag.

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“…As this technology has grown more widespread, efforts to understand the genetic underpinnings of cell state differences have begun and continue to grow. The future of single-cell technologies in studying human disease appears promising, as new single-cell technologies to capture additional modalities such as chromatin accessibility, proteins, and spatial location have matured (Moffitt et al 2018;Chen et al 2019;Eng et al 2019;Zhu et al 2019;Ma et al 2020;Specht et al 2021;Takei et al 2021;Thornton et al 2021) and will enable researchers to detail factors underlying cell state differences not described by mRNA alone. Moreover, these technologies may help researchers further our understanding of the interaction between these factors in cell regulation.…”

Section: Discussionmentioning

confidence: 99%

Applications of single-cell genomics and computational strategies to study common disease and population-level variation

Auerbach

Reilly

et al. 2021

Genome Res.

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The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of the disease molecular mechanism. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but how to link cellular heterogeneity with disease phenotypes requires careful computational analysis. In this article, we will review the current applications of single-cell methods in human disease studies and describe what we have learned so far from existing studies about human genetic variation. As single-cell technologies are becoming widely applicable in human disease studies, population-level studies have become a reality. We will describe how we should go about pursuing and designing these studies, particularly how to select study subjects, how to determine the number of cells to sequence per subject, and the needed sequencing depth per cell. We also discuss computational strategies for the analysis of single-cell data and describe how single-cell data can be integrated with bulk tissue data and data generated from genome-wide association studies. Finally, we point out open problems and future research directions.

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Spatially mapped single-cell chromatin accessibility

Cited by 60 publications

References 78 publications

Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram

Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram

High-content single-cell combinatorial indexing

Applications of single-cell genomics and computational strategies to study common disease and population-level variation

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