scholarly journals Slide-seq: A Scalable Technology for Measuring Genome-Wide Expression at High Spatial Resolution

2019 ◽  
Author(s):  
Samuel G. Rodriques ◽  
Robert R. Stickels ◽  
Aleksandrina Goeva ◽  
Carly A. Martin ◽  
Evan Murray ◽  
...  
Keyword(s):  
Gene Expression ◽  
Spatial Resolution ◽  
Large Scale ◽  
Spatial Organization ◽  
Expression Patterns ◽  
Cell Types ◽  
Genome Wide ◽  
Biological Discovery ◽  
Spatial Locations ◽  
Genome Wide Expression

AbstractThe spatial organization of cells in tissue has a profound influence on their function, yet a high-throughput, genome-wide readout of gene expression with cellular resolution is lacking. Here, we introduce Slide-seq, a highly scalable method that enables facile generation of large volumes of unbiased spatial transcriptomes with 10 µm spatial resolution, comparable to the size of individual cells. In Slide-seq, RNA is transferred from freshly frozen tissue sections onto a surface covered in DNA-barcoded beads with known positions, allowing the spatial locations of the RNA to be inferred by sequencing. To demonstrate Slide-seq’s utility, we localized cell types identified by large-scale scRNA-seq datasets within the cerebellum and hippocampus. We next systematically characterized spatial gene expression patterns in the Purkinje layer of mouse cerebellum, identifying new axes of variation across Purkinje cell compartments. Finally, we used Slide-seq to define the temporal evolution of cell-type-specific responses in a mouse model of traumatic brain injury. Slide-seq will accelerate biological discovery by enabling routine, high-resolution spatial mapping of gene expression.One Sentence SummarySlide-seq measures genome-wide expression in complex tissues at 10-micron resolution.

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

Science ◽  
2019 ◽  
Vol 363 (6434) ◽  
pp. 1463-1467 ◽  
Author(s):  
Samuel G. Rodriques ◽  
Robert R. Stickels ◽  
Aleksandrina Goeva ◽  
Carly A. Martin ◽  
Evan Murray ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Spatial Resolution ◽  
Expression Patterns ◽  
Cell Types ◽  
Expression Data ◽  
Tissue Sections ◽  
Spatially Resolved ◽  
Genome Wide ◽  
Cell Type Specific ◽  
Genome Wide Expression

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.

scholarly journals Gene expression imputation across multiple brain regions reveals schizophrenia risk throughout development

2017 ◽  
Author(s):  
Laura M. Huckins ◽  
Amanda Dobbyn ◽  
Douglas M. Ruderfer ◽  
Gabriel Hoffman ◽  
Weiqing Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Patterns ◽  
Brain Regions ◽  
Developmental Expression ◽  
Genotype Data ◽  
Genome Wide ◽  
Hexosaminidase A ◽  
Gene Expression Levels

AbstractTranscriptomic imputation approaches offer an opportunity to test associations between disease and gene expression in otherwise inaccessible tissues, such as brain, by combining eQTL reference panels with large-scale genotype data. These genic associations could elucidate signals in complex GWAS loci and may disentangle the role of different tissues in disease development. Here, we use the largest eQTL reference panel for the dorso-lateral pre-frontal cortex (DLPFC), collected by the CommonMind Consortium, to create a set of gene expression predictors and demonstrate their utility. We applied these predictors to 40,299 schizophrenia cases and 65,264 matched controls, constituting the largest transcriptomic imputation study of schizophrenia to date. We also computed predicted gene expression levels for 12 additional brain regions, using publicly available predictor models from GTEx. We identified 413 genic associations across 13 brain regions. Stepwise conditioning across the genes and tissues identified 71 associated genes (67 outside the MHC), with the majority of associations found in the DLPFC, and of which 14/67 genes did not fall within previously genome-wide significant loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple pathways associated with porphyric disorders. We investigated developmental expression patterns for all 67 non-MHC associated genes using BRAINSPAN, and identified groups of genes expressed specifically pre-natally or post-natally.

scholarly journals Microfluidic Leukocyte Isolation for Gene Expression Analysis in Critically Ill Hospitalized Patients

2008 ◽  
Vol 54 (5) ◽  
pp. 891-900 ◽  
Author(s):  
Aman Russom ◽  
Palaniappan Sethu ◽  
Daniel Irimia ◽  
Michael N Mindrinos ◽  
Steve E Calvano ◽  
...  
Keyword(s):  
Gene Expression ◽  
Critically Ill ◽  
Expression Analysis ◽  
Clinical Setting ◽  
Whole Blood ◽  
Gene Expression Analysis ◽  
Expression Patterns ◽  
Hospitalized Patients ◽  
Genome Wide ◽  
Genome Wide Expression

Abstract Background: Microarray technology is becoming a powerful tool for diagnostic, therapeutic, and prognostic applications. There is at present no consensus regarding the optimal technique to isolate nucleic acids from blood leukocyte populations for subsequent expression analyses. Current collection and processing techniques pose significant challenges in the clinical setting. Here, we report the clinical validation of a novel microfluidic leukocyte nucleic acid isolation technique for gene expression analysis from critically ill, hospitalized patients that can be readily used on small volumes of blood. Methods: We processed whole blood from hospitalized patients after burn injury and severe blunt trauma according to the microfluidic and standard macroscale leukocyte isolation protocol. Side-by-side comparison of RNA quantity, quality, and genome-wide expression patterns was used to clinically validate the microfluidic technique. Results: When the microfluidic protocol was used for processing, sufficient amounts of total RNA were obtained for genome-wide expression analysis from 0.5 mL whole blood. We found that the leukocyte expression patterns from samples processed using the 2 protocols were concordant, and there was less variability introduced as a result of harvesting method than there existed between individuals. Conclusions: The novel microfluidic approach achieves leukocyte isolation in <25 min, and the quality of nucleic acids and genome expression analysis is equivalent to or surpasses that obtained from macroscale approaches. Microfluidics can significantly improve the isolation of blood leukocytes for genomic analyses in the clinical setting.

scholarly journals An unsupervised learning method for reconstructing cell spatial organization with application to the DREAM Single Cell Transcriptomics Challenge

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 124
Author(s):  
Yang Chen ◽  
Disheng Mao ◽  
Yuping Zhang ◽  
Zhengqing Ouyang
Keyword(s):  
Gene Expression ◽  
Unsupervised Learning ◽  
Single Cell ◽  
Spatial Organization ◽  
Single Cells ◽  
Expression Patterns ◽  
Cell Types ◽  
Learning Method ◽  
Driver Genes ◽  
Clustering Approach

Single cell RNA sequencing (scRNA-seq) data analysis is important for building a global transcription landscape of all cell types in tissues, tracing cell lineages, and reconstructing cell spatial organizations. In this article, we propose an unsupervised learning method to predict spatial positions and gene expression of individual cells in Drosophila embryos using a small number of driver genes. Specifically, we develop a two-stage clustering approach, and compute a probability matrix of the spatial positions of single cells. This method is applied to dataset in the DREAM Single Cell Transcriptomics Challenge. The comparison with the “gold standard” suggests that our method is effective in reconstructing the cell positions and gene expression patterns in spatial tissues.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals Histone modifications form a cell-type-specific chromosomal bar code that persists through the cell cycle

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
John A. Halsall ◽  
Simon Andrews ◽  
Felix Krueger ◽  
Charlotte E. Rutledge ◽  
Gabriella Ficz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Histone Modifications ◽  
Expression Patterns ◽  
Cell Types ◽  
Cell Type ◽  
Bar Code ◽  
Genes Encoding ◽  
Cell Type Specific ◽  
Rolling Windows

AbstractChromatin configuration influences gene expression in eukaryotes at multiple levels, from individual nucleosomes to chromatin domains several Mb long. Post-translational modifications (PTM) of core histones seem to be involved in chromatin structural transitions, but how remains unclear. To explore this, we used ChIP-seq and two cell types, HeLa and lymphoblastoid (LCL), to define how changes in chromatin packaging through the cell cycle influence the distributions of three transcription-associated histone modifications, H3K9ac, H3K4me3 and H3K27me3. We show that chromosome regions (bands) of 10–50 Mb, detectable by immunofluorescence microscopy of metaphase (M) chromosomes, are also present in G1 and G2. They comprise 1–5 Mb sub-bands that differ between HeLa and LCL but remain consistent through the cell cycle. The same sub-bands are defined by H3K9ac and H3K4me3, while H3K27me3 spreads more widely. We found little change between cell cycle phases, whether compared by 5 Kb rolling windows or when analysis was restricted to functional elements such as transcription start sites and topologically associating domains. Only a small number of genes showed cell-cycle related changes: at genes encoding proteins involved in mitosis, H3K9 became highly acetylated in G2M, possibly because of ongoing transcription. In conclusion, modified histone isoforms H3K9ac, H3K4me3 and H3K27me3 exhibit a characteristic genomic distribution at resolutions of 1 Mb and below that differs between HeLa and lymphoblastoid cells but remains remarkably consistent through the cell cycle. We suggest that this cell-type-specific chromosomal bar-code is part of a homeostatic mechanism by which cells retain their characteristic gene expression patterns, and hence their identity, through multiple mitoses.

scholarly journals Discovering Distinct Patterns in Gene Expression Profiles

2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Clustering Methods ◽  
Gene Expressions ◽  
Real Gene ◽  
Large Scale Dataset ◽  
Coexpressed Genes

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.

scholarly journals Use of serial analysis of gene expression to generate kidney expression libraries

2000 ◽  
Vol 279 (2) ◽  
pp. F383-F392 ◽  
Author(s):  
M. Ashraf El-Meanawy ◽  
Jeffrey R. Schelling ◽  
Fatima Pozuelo ◽  
Matthew M. Churpek ◽  
Eckhard K. Ficker ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Disease ◽  
Ribosomal Proteins ◽  
Expressed Sequence Tag ◽  
Chronic Renal Disease ◽  
Expression Patterns ◽  
Expression Library ◽  
Parathyroid Hormone Receptor ◽  
Homology Searching ◽  
Genome Wide

Chronic renal disease initiation and progression remain incompletely understood. Genome-wide expression monitoring should clarify mechanisms that cause progressive renal disease by determining how clusters of genes coordinately change their activity. Serial analysis of gene expression (SAGE) is a technique of expression profiling, which permits simultaneous, comparative, and quantitative analysis of gene-specific, 9- to 13-bp sequence tags. Using SAGE, we have constructed a tag expression library from ROP-+/+ mouse kidney. Tag sequences were sorted by abundance, and identity was determined by sequence homology searching. Analyses of 3,868 tags yielded 1,453 unique kidney transcripts. Forty-two percent of these transcripts matched mRNA sequence entries with known function, 35% of the transcripts corresponded to expressed sequence tag (EST) entries or cloned genes, whose function has not been established, and 23% represented unidentified genes. Previously characterized transcripts were clustered into functional groups, and those encoding metabolic enzymes, plasma membrane proteins (transporters/receptors), and ribosomal proteins were most abundant (39, 14, and 12% of known transcripts, respectively). The most common, kidney-specific transcripts were kidney androgen-regulated protein (4% of all transcripts), sodium-phosphate cotransporter (0.3%), renal cytochrome P-450 (0.3%), parathyroid hormone receptor (0.1%), and kidney-specific cadherin (0.1%). Comprehensively characterizing and contrasting gene expression patterns in normal and diseased kidneys will provide an alternative strategy to identify candidate pathways, which regulate nephropathy susceptibility and progression, and novel targets for therapeutic intervention.

FRI0309 The genome-wide expression of human osteoarthritic cartilage shows different GENE-expression profiles OA-related

2013 ◽  
Vol 71 (Suppl 3) ◽  
pp. 418.3-418
Author(s):  
J. Fernandez-Tajes ◽  
A. Soto-Hermida ◽  
M. Fernandez-Moreno ◽  
M.E. Vazquez-Mosquera ◽  
N. Oreiro ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Osteoarthritic Cartilage ◽  
Genome Wide ◽  
Genome Wide Expression
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