Dataset Information

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution.

ABSTRACT: Spatial positions of cells in tissues strongly influence function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. We developed Slide-seq, a method for transferring RNA from tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the locations of the RNA to be inferred by sequencing. Using Slide-seq, we localized cell types identified by single-cell RNA sequencing datasets within the cerebellum and hippocampus, characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, and defined the temporal evolution of cell type-specific responses in a mouse model of traumatic brain injury. These studies highlight how Slide-seq provides a scalable method for obtaining spatially resolved gene expression data at resolutions comparable to the sizes of individual cells.

SUBMITTER: Rodriques SG

PROVIDER: S-EPMC6927209 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution.

Rodriques Samuel G SG Stickels Robert R RR Goeva Aleksandrina A Martin Carly A CA Murray Evan E Vanderburg Charles R CR Welch Joshua J Chen Linlin M LM Chen Fei F Macosko Evan Z EZ

Science (New York, N.Y.) 20190328 6434

Spatial positions of cells in tissues strongly influence function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. We developed Slide-seq, a method for transferring RNA from tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the locations of the RNA to be inferred by sequencing. Using Slide-seq, we localized cell types identified by single-cell RNA sequencing datasets within the cerebellum and hippocampus ...[more]

PMID: 30923225

Similar Datasets

Project description:To persist in their dynamic human host environments, fungal pathogens must sense and adapt by modulating their gene expression to fulfill their cellular needs. Understanding transcriptional regulation on a global scale would uncover cellular processes linked to persistence and virulence mechanisms that could be targeted for antifungal therapeutics. Infections associated with the yeast Candida albicans, a highly prevalent fungal pathogen, and the multiresistant related species Candida auris are becoming a serious public health threat. To define the set of a gene regulated by a transcriptional regulator in C. albicans, chromatin immunoprecipitation (ChIP)-based techniques, including ChIP with microarray technology (ChIP-chip) or ChIP-DNA sequencing (ChIP-seq), have been widely used. Here, we describe a new set of PCR-based micrococcal nuclease (MNase)-tagging plasmids for C. albicans and other Candida spp. to determine the genome-wide location of any transcriptional regulator of interest using chromatin endogenous cleavage (ChEC) coupled to high-throughput sequencing (ChEC-seq). The ChEC procedure does not require protein-DNA cross-linking or sonication, thus avoiding artifacts related to epitope masking or the hyper-ChIPable euchromatic phenomenon. In a proof-of-concept application of ChEC-seq, we provided a high-resolution binding map of the SWI/SNF chromatin remodeling complex, a master regulator of fungal fitness in C. albicans, in addition to the transcription factor Nsi1 that is an ortholog of the DNA-binding protein Reb1 for which genome-wide occupancy was previously established in Saccharomyces cerevisiae The ChEC-seq procedure described here will allow a high-resolution genomic location definition which will enable a better understanding of transcriptional regulatory circuits that govern fungal fitness and drug resistance in these medically important fungi.IMPORTANCE Systemic fungal infections caused by Candida albicans and the "superbug" Candida auris are becoming a serious public health threat. The ability of these yeasts to cause disease is linked to their faculty to modulate the expression of genes that mediate their escape from the immune surveillance and their persistence in the different unfavorable niches within the host. Comprehensive knowledge on gene expression control of fungal fitness is consequently an interesting framework for the identification of essential infection processes that could be hindered by chemicals as potential therapeutics. Here, we expanded the use of ChEC-seq, a technique that was initially developed in the yeast model Saccharomyces cerevisiae to identify genes that are modulated by a transcriptional regulator, in pathogenic yeasts from the genus Candida This robust technique will allow a better characterization of key gene expression regulators and their contribution to virulence and antifungal resistance in these pathogenic yeasts.

Project description:An essential component of genome function is the syntax of genomic regulatory elements that determine how diverse transcription factors interact to orchestrate a program of regulatory control. A precise characterization of in vivo spacing constraints between key transcription factors would reveal key aspects of this genomic regulatory language. To discover novel transcription factor spatial binding constraints in vivo, we developed a new integrative computational method, genome wide event finding and motif discovery (GEM). GEM resolves ChIP data into explanatory motifs and binding events at high spatial resolution by linking binding event discovery and motif discovery with positional priors in the context of a generative probabilistic model of ChIP data and genome sequence. GEM analysis of 63 transcription factors in 214 ENCODE human ChIP-Seq experiments recovers more known factor motifs than other contemporary methods, and discovers six new motifs for factors with unknown binding specificity. GEM's adaptive learning of binding-event read distributions allows it to further improve upon previous methods for processing ChIP-Seq and ChIP-exo data to yield unsurpassed spatial resolution and discovery of closely spaced binding events of the same factor. In a systematic analysis of in vivo sequence-specific transcription factor binding using GEM, we have found hundreds of spatial binding constraints between factors. GEM found 37 examples of factor binding constraints in mouse ES cells, including strong distance-specific constraints between Klf4 and other key regulatory factors. In human ENCODE data, GEM found 390 examples of spatially constrained pair-wise binding, including such novel pairs as c-Fos:c-Jun/USF1, CTCF/Egr1, and HNF4A/FOXA1. The discovery of new factor-factor spatial constraints in ChIP data is significant because it proposes testable models for regulatory factor interactions that will help elucidate genome function and the implementation of combinatorial control.

Dataset Information

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution.

Publications

Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets