XYZeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment

Lee, Youjin; Bogdanoff, Derek; Wang, Yutong; Hartoularos, George C.; Woo, Jonathan M.; Mowery, Cody T.; Nisonoff, Hunter; Lee, David S.; Sun, Yang; Lee, James; Mehdizadeh, Sadaf; Cantlon, Joshua; Shifrut, Eric; Ngyuen, David N.; Roth, Theodore L.; Song, Yun S.; Marson, Alexander; Chow, Eric D.; Ye, Chun Jimmie

doi:10.1126/sciadv.abg4755

Cited by 82 publications

(60 citation statements)

References 55 publications

(66 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another polony-based method, PIXEL-seq, achieves a spot diameter of about 1.22 μm, but unlike in the flow cell, polony (or DNA cluster)-indexed library-sequencing (PIXEL-seq) does not have much spacing around each polony 72 . Techniques such as XYZeq 73 and sci-Space 74 have been developed to dissociate the single cell or nuclei in spatially barcoded spots for scRNA-seq, so the data have single-cell transcriptomic, but not spatial, resolution (Fig. 2f).…”

Section: Data Collectionmentioning

confidence: 99%

Museum of spatial transcriptomics

2022

View full text Add to dashboard Cite

The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors, depends on the spatial organization of their cells. In the past decade, high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To comprehensively document spatial gene expression technologies and data-analysis methods, we present a curated review of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field, such as usage of experimental techniques, species, tissues studied, and computational approaches used. Our Review places current methods in a historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/.

show abstract

Section: Data Collectionmentioning

confidence: 99%

Museum of spatial transcriptomics

2022

View full text Add to dashboard Cite

show abstract

“…S3). (Stereo-seq( 6 ); SeqScope( 20 ); DBiT-seq( 19 ); Slide-seq( 4, 5 ); Visium( 3 ); sci-Space( 7 ); XYZeq( 31 ))…”

Section: Resultsmentioning

confidence: 99%

The DNA-based global positioning system—a theoretical framework for large-scale spatial genomics

Greenstreet

Afanassiev

Kijima

et al. 2022

Preprint

View full text Add to dashboard Cite

We present GPS-seq, a theoretical framework that enables massively scalable, optics-free spatial transcriptomics. GPS-seq combines data from high-throughput sequencing with manifold learning to obtain the spatial transcriptomic landscape of a given tissue section without optical microscopy. In this framework, similar to technologies like Slide-seq and 10X Visium, tissue samples are stamped on a surface of randomly-distributed DNA-barcoded spots (or beads). The transcriptomic sequences of proximal cells are fused to DNA barcodes, enabling the recovery of a transcriptomic pixel image by high-throughput sequencing. The barcode spots serve as "anchors" which also capture spatially diffused "satellite" barcodes, and therefore allow computational reconstruction of spot positions without optical sequencing or depositing barcodes to pre-specified positions. In theory, it could generate 100 mm × 100 mm spatial transcriptomic images with 10-20 μm resolution by localizing 108 DNA-barcoded pixels with a single Illumina NovaSeq run. The general framework of GPS-seq is also compatible with standard single-cell (or single-nucleus) capture methods, and any modality of single-cell genomics, such as sci-ATAC-seq, could be transformed into spatial genomics in this strategy. We envision that GPS-seq will lead to breakthrough discoveries in diverse areas of biology by enabling organ-scale imaging of multiple genomic statuses at single-cell resolution for the first time.

show abstract

“…The rapid progress in spatially resolved transcriptomics is enabling the discovery and characterization of transcriptionally heterogenous cells in diverse tissue contexts (Lee et al, 2021; Ståhl et al, 2016). However, these methods do not capture the dynamics of cell behaviors that often define the unique biological processes that occur in the tissues.…”

Section: Discussionmentioning

confidence: 99%

Single-cell transcriptomics of dynamic cell behaviors

Vinarsky

Haber

Kwok

et al. 2022

Preprint

View full text Add to dashboard Cite

Despite advances in spatial transcriptomics, the molecular profiling of dynamic behaviors of cells in their native environment remains a major challenge. We present a method, termed behavioral transcriptomics, that allows us to couple physiological behaviors of single cells in an intact tissue to deep molecular profiling of individual cells. This method enabled us to establish a novel molecular signature for a striking migratory cellular behavior following tissue injury.

show abstract

XYZeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment

Cited by 82 publications

References 55 publications

Museum of spatial transcriptomics

Museum of spatial transcriptomics

The DNA-based global positioning system—a theoretical framework for large-scale spatial genomics

Single-cell transcriptomics of dynamic cell behaviors

Contact Info

Product

Resources

About