Localizing Genes to Cerebellar Layers by Classifying ISH Images

Lior, Kirsch; Liscovitch, Noa; Chechik, Gal

doi:10.1371/journal.pcbi.1002790

Cited by 28 publications

(21 citation statements)

References 30 publications

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“…Brain regions are well separated in this projection, in a way that matches the anterior-posterior axis and the BRO. The isolated cluster of samples on the left belongs to the cerebellum, which is well known to exhibit a unique molecular and cellular organization [ 21 , 51 , 52 ]. This analysis shows that the BRO is a major determinant of variability in human brain transcriptome.…”

Section: Resultsmentioning

confidence: 99%

On Expression Patterns and Developmental Origin of Human Brain Regions

Lior

Chechik

2016

PLoS Comput Biol

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Anatomical substructures of the human brain have characteristic cell-types, connectivity and local circuitry, which are reflected in area-specific transcriptome signatures, but the principles governing area-specific transcription and their relation to brain development are still being studied. In adult rodents, areal transcriptome patterns agree with the embryonic origin of brain regions, but the processes and genes that preserve an embryonic signature in regional expression profiles were not quantified. Furthermore, it is not clear how embryonic-origin signatures of adult-brain expression interplay with changes in expression patterns during development. Here we first quantify which genes have regional expression-patterns related to the developmental origin of brain regions, using genome-wide mRNA expression from post-mortem adult human brains. We find that almost all human genes (92%) exhibit an expression pattern that agrees with developmental brain-region ontology, but that this agreement changes at multiple phases during development. Agreement is particularly strong in neuron-specific genes, but also in genes that are not spatially correlated with neuron-specific or glia-specific markers. Surprisingly, agreement is also stronger in early-evolved genes. We further find that pairs of similar genes having high agreement to developmental region ontology tend to be more strongly correlated or anti-correlated, and that the strength of spatial correlation changes more strongly in gene pairs with stronger embryonic signatures. These results suggest that transcription regulation of most genes in the adult human brain is spatially tuned in a way that changes through life, but in agreement with development-determined brain regions.

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Section: Resultsmentioning

confidence: 99%

On Expression Patterns and Developmental Origin of Human Brain Regions

Lior

Chechik

2016

PLoS Comput Biol

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“…Gene networks were formulated based on the correlation of gene modules to one another. Upon post-clustering annotation of the larger gene networks with predefined markers for Purkinje neurons and granular neurons (30, 31, 35, 36), the turquoise gene network block, labeled as Purkinje neuron cluster, was enriched for Purkinje neuron gene markers, while the coral gene network labeled granular neuron cluster was enriched mainly of granular neuron markers (Figure 4A, Supplementary Table S4). The height threshold was then increased to 0.65 to obtain larger gene modules that might be correlated with the CA-phenotype (Modules 1 through 5), of which only Module 5 was significantly correlated with the CA-phenotype (Figure 4B, Supplementary Table S5).…”

Section: Resultsmentioning

confidence: 99%

“…ROAST was used in conjunction with EdgeR and its output of normalized read counts (CPM). The gene sets provided were Purkinje neuron and granular neuron markers from Kuhn et al (30) as well as Allen Brain Atlas derived markers from Kirsch et al (31), with 236 and 1684 transcripts, respectively. All Purkinje neuron markers had visually confirmed localization to Purkinje neurons according to the Allen Brain Atlas ISH images.…”

Section: Methodsmentioning

confidence: 99%

Defining Trends in Global Gene Expression in Arabian Horses with Cerebellar Abiotrophy

et al. 2016

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Equine cerebellar abiotrophy (CA) is a hereditary neurodegenerative disease that affects the Purkinje neurons of the cerebellum and causes ataxia in Arabian foals. Signs of CA are typically first recognized either at birth to any time up to 6 months of age. CA is inherited as an autosomal recessive trait and is associated with a single nucleotide polymorphism (SNP) on equine chromosome 2 (13074277G>A), located in the fourth exon of TOE1 and in proximity to MUTYH on the antisense strand. We hypothesize that unraveling the functional consequences of the CA SNP using RNA-seq will elucidate the molecular pathways underlying the CA phenotype. RNA-seq (100 bp PE strand-specific) was performed in cerebellar tissue from four CA-affected and five age-matched unaffected horses. Three pipelines for differential gene expression (DE) analysis were used (Tophat2/Cuffdiff2, Kallisto/EdgeR and Kallisto/Sleuth) with 151 significant DE genes identified by all three pipelines in CA-affected horses. TOE1 (Log2(foldchange)=0.92, p=0.66) and MUTYH (Log2(foldchange)=1.13, p=0.66) were not differentially expressed. Among the major pathways that were differentially expressed, genes associated with calcium homeostasis and specifically expressed in Purkinje neurons, CALB1 (Log2(foldchange)= −1.7, p<0.01) and CA8 (Log2(foldchange)= −0.97, p<0.01), were significantly down-regulated, confirming loss of Purkinje neurons. There was also a significant up-regulation of markers for microglial phagocytosis, TYROBP (Log2(foldchange)= 1.99, p<0.01) and TREM2 (Log2(foldchange)= 2.02, p<0.01). These findings reaffirm a loss of Purkinje neurons in CA-affected horses along with a potential secondary loss of granular neurons and activation of microglial cells.

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“…This step aims to address the problem that ISH brain images of the same gene vary significantly in shape and size when measured in different brains (Kirsch et al , 2012). SIFT features are histograms of oriented gradients on a small grid.…”

Section: Methodsmentioning

confidence: 99%

FuncISH: learning a functional representation of neural ISH images

Liscovitch¹,

Shalit

Chechik

2013

Bioinformatics

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Motivation: High-spatial resolution imaging datasets of mammalian brains have recently become available in unprecedented amounts. Images now reveal highly complex patterns of gene expression varying on multiple scales. The challenge in analyzing these images is both in extracting the patterns that are most relevant functionally and in providing a meaningful representation that allows neuroscientists to interpret the extracted patterns.Results: Here, we present FuncISH—a method to learn functional representations of neural in situ hybridization (ISH) images. We represent images using a histogram of local descriptors in several scales, and we use this representation to learn detectors of functional (GO) categories for every image. As a result, each image is represented as a point in a low-dimensional space whose axes correspond to meaningful functional annotations. The resulting representations define similarities between ISH images that can be easily explained by functional categories. We applied our method to the genomic set of mouse neural ISH images available at the Allen Brain Atlas, finding that most neural biological processes can be inferred from spatial expression patterns with high accuracy. Using functional representations, we predict several gene interaction properties, such as protein–protein interactions and cell-type specificity, more accurately than competing methods based on global correlations. We used FuncISH to identify similar expression patterns of GABAergic neuronal markers that were not previously identified and to infer new gene function based on image–image similarities.Contact: noalis@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.

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Localizing Genes to Cerebellar Layers by Classifying ISH Images

Cited by 28 publications

References 30 publications

On Expression Patterns and Developmental Origin of Human Brain Regions

On Expression Patterns and Developmental Origin of Human Brain Regions

Defining Trends in Global Gene Expression in Arabian Horses with Cerebellar Abiotrophy

FuncISH: learning a functional representation of neural ISH images

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