scholarly journals From whole-mount to single-cell spatial assessment of gene expression in 3D

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Lisa N. Waylen ◽  
Hieu T. Nim ◽  
Luciano G. Martelotto ◽  
Mirana Ramialison
Keyword(s):  
Gene Expression ◽  
Cell Types ◽  
Medical Diagnostics ◽  
Three Dimensions ◽  
Expression Data ◽  
Developmental Defects ◽  
Ongoing Research ◽  
Spatially Resolved ◽  
Spatio Temporal ◽  
Gene Expression Levels

Abstract Unravelling spatio-temporal patterns of gene expression is crucial to understanding core biological principles from embryogenesis to disease. Here we review emerging technologies, providing automated, high-throughput, spatially resolved quantitative gene expression data. Novel techniques expand on current benchmark protocols, expediting their incorporation into ongoing research. These approaches digitally reconstruct patterns of embryonic expression in three dimensions, and have successfully identified novel domains of expression, cell types, and tissue features. Such technologies pave the way for unbiased and exhaustive recapitulation of gene expression levels in spatial and quantitative terms, promoting understanding of the molecular origin of developmental defects, and improving medical diagnostics.

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 Advances in mixed cell deconvolution enable quantification of cell types in spatially-resolved gene expression data

2020 ◽  
Author(s):  
Patrick Danaher ◽  
Youngmi Kim ◽  
Brenn Nelson ◽  
Maddy Griswold ◽  
Zhi Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Gene Expression Data ◽  
Lung Tumor ◽  
Cell Types ◽  
Expression Data ◽  
Mixed Cell ◽  
Cell Abundance ◽  
Spatially Resolved ◽  
Abundance Estimates

AbstractWe introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell RNA sequencing within the regions of spatially-resolved gene expression studies. It obtains cell abundance estimates that are spatially-resolved, granular, and paired with highly multiplexed gene expression data.SpatialDecon incorporates several advancements in the field of gene expression deconvolution. We propose an algorithm based in log-normal regression, attaining sometimes dramatic performance improvements over classical least-squares methods. We compile cell profile matrices for 27 tissue types. We identify genes whose minimal expression by cancer cells makes them suitable for immune deconvolution in tumors. And we provide a lung tumor dataset for benchmarking immune deconvolution methods.In a lung tumor GeoMx DSP experiment, we observe a spatially heterogeneous immune response in intricate detail and identify 7 distinct phenotypes of the localized immune response. We then demonstrate how cell abundance estimates give crucial context for interpreting gene expression results.

Identification of stable reference genes in differentiating human pluripotent stem cells

2015 ◽  
Vol 47 (6) ◽  
pp. 232-239 ◽  
Author(s):  
Gustav Holmgren ◽  
Nidal Ghosheh ◽  
Xianmin Zeng ◽  
Yalda Bogestål ◽  
Peter Sartipy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Gene Expression Data ◽  
Pluripotent Stem Cells ◽  
Reference Genes ◽  
Cell Types ◽  
Human Pluripotent Stem Cells ◽  
Expression Data ◽  
Large Variability ◽  
The Stability

Reference genes, often referred to as housekeeping genes (HKGs), are frequently used to normalize gene expression data based on the assumption that they are expressed at a constant level in the cells. However, several studies have shown that there may be a large variability in the gene expression levels of HKGs in various cell types. In a previous study, employing human embryonic stem cells (hESCs) subjected to spontaneous differentiation, we observed that the expression of commonly used HKG varied to a degree that rendered them inappropriate to use as reference genes under those experimental settings. Here we present a substantially extended study of the HKG signature in human pluripotent stem cells (hPSC), including nine global gene expression datasets from both hESC and human induced pluripotent stem cells, obtained during directed differentiation toward endoderm-, mesoderm-, and ectoderm derivatives. Sets of stably expressed genes were compiled, and a handful of genes (e.g., EID2, ZNF324B, CAPN10, and RABEP2) were identified as generally applicable reference genes in hPSCs across all cell lines and experimental conditions. The stability in gene expression profiles was confirmed by reverse transcription quantitative PCR analysis. Taken together, the current results suggest that differentiating hPSCs have a distinct HKG signature, which in some aspects is different from somatic cell types, and underscore the necessity to validate the stability of reference genes under the actual experimental setup used. In addition, the novel putative HKGs identified in this study can preferentially be used for normalization of gene expression data obtained from differentiating hPSCs.

scholarly journals Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types

2017 ◽  
Author(s):  
Hilary K. Finucane ◽  
Yakir A. Reshef ◽  
Verneri Anttila ◽  
Kamil Slowikowski ◽  
Alexander Gusev ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Complex Disease ◽  
Genome Wide Association Study ◽  
Ex Vivo ◽  
Cell Types ◽  
Inhibitory Neurons ◽  
Biliary Cirrhosis ◽  
Expression Data ◽  
Specific Expression

ABSTRACTGenetics can provide a systematic approach to discovering the tissues and cell types relevant for a complex disease or trait. Identifying these tissues and cell types is critical for following up on non-coding allelic function, developing ex-vivo models, and identifying therapeutic targets. Here, we analyze gene expression data from several sources, including the GTEx and PsychENCODE consortia, together with genome-wide association study (GWAS) summary statistics for 48 diseases and traits with an average sample size of 169,331, to identify disease-relevant tissues and cell types. We develop and apply an approach that uses stratified LD score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We detect tissue-specific enrichments at FDR < 5% for 34 diseases and traits across a broad range of tissues that recapitulate known biology. In our analysis of traits with observed central nervous system enrichment, we detect an enrichment of neurons over other brain cell types for several brain-related traits, enrichment of inhibitory over excitatory neurons for bipolar disorder but excitatory over inhibitory neurons for schizophrenia and body mass index, and enrichments in the cortex for schizophrenia and in the striatum for migraine. In our analysis of traits with observed immunological enrichment, we identify enrichments of T cells for asthma and eczema, B cells for primary biliary cirrhosis, and myeloid cells for Alzheimer's disease, which we validated with independent chromatin data. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signal.

scholarly journals Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Julien Racle ◽  
Kaat de Jonge ◽  
Petra Baumgaertner ◽  
Daniel E Speiser ◽  
David Gfeller
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Immune Cell ◽  
Expression Profiles ◽  
Cell Types ◽  
Response To Therapy ◽  
Expression Data ◽  
Cell Type ◽  
Tumor Gene Expression ◽  
Tumor Gene

Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org).

scholarly journals ImmuneRegulation: a web-based tool for identifying human immune regulatory elements

2019 ◽  
Vol 47 (W1) ◽  
pp. W142-W150 ◽  
Author(s):  
Selim Kalayci ◽  
Myvizhi Esai Selvan ◽  
Irene Ramos ◽  
Chris Cotsapas ◽  
Eva Harris ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Types ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Web Based ◽  
Transcriptome Regulation ◽  
Rich Data ◽  
User Friendly ◽  
Gene Expression Levels

Abstract Humans vary considerably both in their baseline and activated immune phenotypes. We developed a user-friendly open-access web portal, ImmuneRegulation, that enables users to interactively explore immune regulatory elements that drive cell-type or cohort-specific gene expression levels. ImmuneRegulation currently provides the largest centrally integrated resource on human transcriptome regulation across whole blood and blood cell types, including (i) ∼43,000 genotyped individuals with associated gene expression data from ∼51,000 experiments, yielding genetic variant-gene expression associations on ∼220 million eQTLs; (ii) 14 million transcription factor (TF)-binding region hits extracted from 1945 ChIP-seq studies; and (iii) the latest GWAS catalog with 67,230 published variant-trait associations. Users can interactively explore associations between queried gene(s) and their regulators (cis-eQTLs, trans-eQTLs or TFs) across multiple cohorts and studies. These regulators may explain genotype-dependent gene expression variations and be critical in selecting the ideal cohorts or cell types for follow-up studies or in developing predictive models. Overall, ImmuneRegulation significantly lowers the barriers between complex immune regulation data and researchers who want rapid, intuitive and high-quality access to the effects of regulatory elements on gene expression in multiple studies to empower investigators in translating these rich data into biological insights and clinical applications, and is freely available at https://immuneregulation.mssm.edu.

scholarly journals High-throughput mRNA sequencing of stromal cells from endometriomas and endometrium

Reproduction ◽  
2017 ◽  
Vol 154 (1) ◽  
pp. 93-100 ◽  
Author(s):  
Kadri Rekker ◽  
Merli Saare ◽  
Elo Eriste ◽  
Tõnis Tasa ◽  
Viktorija Kukuškina ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Complement System ◽  
Stromal Cells ◽  
Cell Types ◽  
Coagulation Cascade ◽  
Expression Levels ◽  
Mrna Sequencing ◽  
Standard Design ◽  
Gene Expression Levels

The aetiology of endometriosis is still unclear and to find mechanisms behind the disease development, it is important to study each cell type from endometrium and ectopic lesions independently. The objective of this study was to uncover complete mRNA profiles in uncultured stromal cells from paired samples of endometriomas and eutopic endometrium. High-throughput mRNA sequencing revealed over 1300 dysregulated genes in stromal cells from ectopic lesions, including several novel genes in the context of endometriosis. Functional annotation analysis of differentially expressed genes highlighted pathways related to cell adhesion, extracellular matrix–receptor interaction and complement and coagulation cascade. Most importantly, we found a simultaneous upregulation of complement system components and inhibitors, indicating major imbalances in complement regulation in ectopic stromal cells. We also performed in vitro experiments to evaluate the effect of endometriosis patients’ peritoneal fluid (PF) on complement system gene expression levels, but no significant impact of PF on C3, CD55 and CFH levels was observed. In conclusion, the use of isolated stromal cells enables to determine gene expression levels without the background interference of other cell types. In the future, a new standard design studying all cell types from endometriotic lesions separately should be applied to reveal novel mechanisms behind endometriosis pathogenesis.

scholarly journals Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bárbara Andrade Barbosa ◽  
Saskia D. van Asten ◽  
Ji Won Oh ◽  
Arantza Farina-Sarasqueta ◽  
Joanne Verheij ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Expression Data ◽  
Cell Type ◽  
Expression Variability ◽  
Variable Nature ◽  
Log Normal

AbstractDeconvolution of bulk gene expression profiles into the cellular components is pivotal to portraying tissue’s complex cellular make-up, such as the tumor microenvironment. However, the inherently variable nature of gene expression requires a comprehensive statistical model and reliable prior knowledge of individual cell types that can be obtained from single-cell RNA sequencing. We introduce BLADE (Bayesian Log-normAl Deconvolution), a unified Bayesian framework to estimate both cellular composition and gene expression profiles for each cell type. Unlike previous comprehensive statistical approaches, BLADE can handle > 20 types of cells due to the efficient variational inference. Throughout an intensive evaluation with > 700 simulated and real datasets, BLADE demonstrated enhanced robustness against gene expression variability and better completeness than conventional methods, in particular, to reconstruct gene expression profiles of each cell type. In summary, BLADE is a powerful tool to unravel heterogeneous cellular activity in complex biological systems from standard bulk gene expression data.

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