Resources Hub

Vizgen is developing the next generation of genomics tools to expand on the capabilities of spatially resolved transcriptomics. Our technology will aide the acceleration of biological research and discovery to advance human health. Access supporting resources by selecting the category below.

Featured Resource

eBook: What Does High-Quality Spatial Genomics Data Look Like?

eBook

All

Application & Technical Notes

Publications

The emerging landscape of spatial profiling technologies

Abstract Improved scale, multiplexing and resolution are establishing spatial nucleic acid and protein profiling methods as a major pillar for cellular atlas building of complex samples, from tissues to full organisms. Emerging methods yield omics…

Sparcle: assigning transcripts to cells in multiplexed images

Abstract Imaging-based spatial transcriptomics has the power to reveal patterns of single-cell gene expression by detecting mRNA transcripts as individually resolved spots in multiplexed images. However, molecular quantification has been severely limited by the computational…

PROPOSE: Predictive and robust probe selection for spatial transcriptomics

Abstract Fluorescence in situ hybridization (FISH) is a widely used method for visualizing gene expression in cells and tissues. A key challenge is determining a small panel of genes (typically less than 1% of the genome) to…

Comparative analysis of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing

Spatial transcriptomics extends single cell RNA sequencing (scRNA-seq) technologies by providing spatial context for cell type identification and analysis. In particular, imaging-based spatial technologies such as Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH) can achieve…

AACR 2022 Poster: A single-cell spatially resolved map of colorectal cancer identifies novel spatial relationships between cancer cells and the microenvironment

BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis

Abstract Spatial omics data are clustered to define both cell types and tissue domains. We present Building Aggregates with a Neighborhood Kernel and Spatial Yardstick (BANKSY), an algorithm that unifies these two spatial clustering problems…

Reference-based cell type matching of spatial transcriptomics data

Abstract With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly. Spatial transcriptomics provides spatial location and pattern information about cells in tissue sections at single cell…

Cell type-specific inference of differential expression in spatial transcriptomics

Abstract Spatial transcriptomics enables spatially resolved gene expression measurements at near single-cell resolution. There is a pressing need for computational tools to enable the detection of genes that are differentially expressed (DE) within specific cell…

Benchmarking Computational Integration Methods for Spatial Transcriptomics Data

Abstract The increasing popularity of spatial transcriptomics has allowed researchers to analyze transcriptome data in its tissue sample’s spatial context. Various methods have been developed for detecting SV (spatially variable) genes, with distinct spatial expression…

EASI-FISH for thick tissue defines lateral hypothalamus spatio-molecular organization

Summary Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to survey gene expression…

MorphNet Predicts Cell Morphology from Single-Cell Gene Expression

Abstract Gene expression and morphology both play a key role in determining the types and functions of cells, but the relationship between molecular and morphological features is largely uncharacterized. We present MorphNet, a computational approach…

eBook: What Does High-Quality Spatial Genomics Data Look Like?

Like detective work, detailed understanding of the complexities of biological systems requires scientists to answer questions about details at every level: what, when, why, how…and where. Spatial genomics, Nature’s 2020 Method of the Year, can…

MERSCOPE Photobleacher User Guide

This guide outlines the instructions for use for the MERSCOPE Photobleacher. The MERSCOPE Photobleacher is used to remove autofluorescent background from tissue samples before performing MERFISH measurments on the MERSCOPETM Platform.

MERSCOPE Platform Grant Application Resource

Molecular and spatial signatures of mouse brain aging at single-cell resolution

Summary The diversity and complex organization of cells in the brain have hindered systematic characterization of age-related changes in its cellular and molecular architecture, limiting our ability to understand the mechanisms underlying its functional decline…

SFN 2022 Poster: Analyzing the molecular basis underlying anatomic and functional complexity of the mouse brain with MERSCOPE™

Single-cell sequencing has provided numerous novel insights into cell heterogeneity and cell-type-specific adaptation of the nervous system. However, most sequencing-based techniques require cell dissociation and consequently lose the spatial information of the analyzed cells, which…

SFN 2022 Poster: True multiomic in situ single-cell imaging revealed massive pathological changes in Alzheimer’s disease brain

Our understanding of molecular biology and pathology has been greatly facilitated by recent advances in multiple sequencing technologies, coupled with computational analysis, to detect and count proteins and transcripts. However, these tools lack spatial resolution…

Omics goes spatial epigenomics

Spatial omics techniques generate spatially resolved, comprehensive data about molecules that define the identity and function of cells in tissues. Epigenetic multiplexing approaches such as Multiplexed Error-robust FISH (MERFISH), introduced by Lu et al. in this…

Modeling intercellular communication in tissues using spatial graphs of cells

Abstract Models of intercellular communication in tissues are based on molecular profiles of dissociated cells, are limited to receptor–ligand signaling and ignore spatial proximity in situ. We present node-centric expression modeling, a method based on…

Spatially resolved epigenomic profiling of single cells in complex tissues

Abstract The recent development of spatial omics methods has enabled single-cell profiling of the transcriptome and 3D genome organization with high spatial resolution. Expanding the repertoire of spatial omics tools, a spatially resolved single-cell epigenomics…

Pages: 123456789101112131415

Resources Hub

Featured Resource

eBook: What Does High-Quality Spatial Genomics Data Look Like?

Featured Resource

Search Resources

Filter by Resource Type

Filter by Research Area

Filter by Products