Subtle left-right asymmetry of gene expression profiles in embryonic and foetal human brains

AbstractLeft-right laterality is an important aspect of human brain organization for which the genetic basis is poorly understood. Using RNA sequencing data we contrasted gene expression in left- and right-sided samples from several structures of the anterior central nervous systems of post mortem human embryos and fetuses. While few individual genes stood out as significantly lateralized, most structures showed evidence of laterality of their overall transcriptomic profiles. These left-right differences showed overlap with age-dependent changes in expression, indicating lateralized maturation rates, but not consistently in left-right orientation over all structures. Brain asymmetry may therefore originate in multiple locations, or if there is a single origin, it is earlier than 5 weeks post conception, with structure-specific lateralized processes already underway by this age. This pattern is broadly consistent with the weak correlations reported between various aspects of adult brain laterality, such as language dominance and handedness.

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Subtle left-right asymmetry of gene expression profiles in embryonic and foetal human brains

Scientific Reports ◽

10.1038/s41598-018-29496-2 ◽

2018 ◽

Vol 8 (1) ◽

Cited By ~ 19

Author(s):

Carolien G. F. de Kovel ◽

Steven N. Lisgo ◽

Simon E. Fisher ◽

Clyde Francks

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Left Right Asymmetry ◽

Human Brains

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LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data

BMC Genomics ◽

10.1186/s12864-021-07900-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yanan Ren ◽

Ting-You Wang ◽

Leah C. Anderton ◽

Qi Cao ◽

Rendong Yang

Keyword(s):

Gene Expression ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Clinical Samples ◽

Sequencing Data ◽

Multiple Cancer ◽

Regulatory Pathways ◽

Cancer Transcriptome ◽

Versatile Tool

Abstract Background Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs. Results As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancer patient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types. Conclusions LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA.

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Gene expression profiles in anatomically and functionally distinct regions of the normal aged human brain

Physiological Genomics ◽

10.1152/physiolgenomics.00208.2006 ◽

2007 ◽

Vol 28 (3) ◽

pp. 311-322 ◽

Cited By ~ 137

Author(s):

Winnie S. Liang ◽

Travis Dunckley ◽

Thomas G. Beach ◽

Andrew Grover ◽

Diego Mastroeni ◽

...

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Neurofibrillary Tangle ◽

Gene Expression Profiles ◽

Posterior Cingulate Cortex ◽

Neuritic Plaque ◽

Reference Information ◽

Plaque Pathology ◽

Human Brains ◽

Laser Capture

In this article, we have characterized and compared gene expression profiles from laser capture microdissected neurons in six functionally and anatomically distinct regions from clinically and histopathologically normal aged human brains. These regions, which are also known to be differentially vulnerable to the histopathological and metabolic features of Alzheimer’s disease (AD), include the entorhinal cortex and hippocampus (limbic and paralimbic areas vulnerable to early neurofibrillary tangle pathology in AD), posterior cingulate cortex (a paralimbic area vulnerable to early metabolic abnormalities in AD), temporal and prefrontal cortex (unimodal and heteromodal sensory association areas vulnerable to early neuritic plaque pathology in AD), and primary visual cortex (a primary sensory area relatively spared in early AD). These neuronal profiles will provide valuable reference information for future studies of the brain, in normal aging, AD and other neurological and psychiatric disorders.

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The effects of a globin blocker on the resolution of 3’mRNA sequencing data in porcine blood

BMC Genomics ◽

10.1186/s12864-019-6122-2 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 1

Author(s):

Kyu-Sang Lim ◽

Qian Dong ◽

Pamela Moll ◽

Jana Vitkovska ◽

Gregor Wiktorin ◽

...

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Data Sets ◽

Globin Genes ◽

Sequencing Data ◽

Globin Mrna ◽

Data Set ◽

Mrna Sequencing ◽

Porcine Blood

Abstract Background Gene expression profiling in blood is a potential source of biomarkers to evaluate or predict phenotypic differences between pigs but is expensive and inefficient because of the high abundance of globin mRNA in porcine blood. These limitations can be overcome by the use of QuantSeq 3’mRNA sequencing (QuantSeq) combined with a method to deplete or block the processing of globin mRNA prior to or during library construction. Here, we validated the effectiveness of QuantSeq using a novel specific globin blocker (GB) that is included in the library preparation step of QuantSeq. Results In data set 1, four concentrations of the GB were applied to RNA samples from two pigs. The GB significantly reduced the proportion of globin reads compared to non-GB (NGB) samples (P = 0.005) and increased the number of detectable non-globin genes. The highest evaluated concentration (C1) of the GB resulted in the largest reduction of globin reads compared to the NGB (from 56.4 to 10.1%). The second highest concentration C2, which showed very similar globin depletion rates (12%) as C1 but a better correlation of the expression of non-globin genes between NGB and GB (r = 0.98), allowed the expression of an additional 1295 non-globin genes to be detected, although 40 genes that were detected in the NGB sample (at a low level) were not present in the GB library. Concentration C2 was applied in the rest of the study. In data set 2, the distribution of the percentage of globin reads for NGB (n = 184) and GB (n = 189) samples clearly showed the effects of the GB on reducing globin reads, in particular for HBB, similar to results from data set 1. Data set 3 (n = 84) revealed that the proportion of globin reads that remained in GB samples was significantly and positively correlated with the reticulocyte count in the original blood sample (P < 0.001). Conclusions The effect of the GB on reducing the proportion of globin reads in porcine blood QuantSeq was demonstrated in three data sets. In addition to increasing the efficiency of sequencing non-globin mRNA, the GB for QuantSeq has an advantage that it does not require an additional step prior to or during library creation. Therefore, the GB is a useful tool in the quantification of whole gene expression profiles in porcine blood.

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G2S3: a gene graph-based imputation method for single-cell RNA sequencing data

10.1101/2020.04.01.020586 ◽

2020 ◽

Author(s):

Weimiao Wu ◽

Qile Dai ◽

Yunqing Liu ◽

Xiting Yan ◽

Zuoheng Wang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Sequencing Data ◽

High Data ◽

Study Gene Expression ◽

Single Cell Rna Sequencing ◽

Novel Method

AbstractSingle-cell RNA sequencing provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses. We propose a novel method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and other existing methods to seven single-cell datasets to compare their performance. Our results demonstrated that G2S3 is superior in recovering true expression levels, identifying cell subtypes, improving differential expression analyses, and recovering gene regulatory relationships, especially for mildly expressed genes.

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Automated identification of the mouse brain’s spatial compartments from in situ sequencing data

BMC Biology ◽

10.1186/s12915-020-00874-5 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Gabriele Partel ◽

Markus M. Hilscher ◽

Giorgia Milli ◽

Leslie Solorzano ◽

Anna H. Klemm ◽

...

Keyword(s):

Gene Expression ◽

Mouse Brain ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cell Types ◽

Sequencing Data ◽

Tissue Samples ◽

Gene Markers ◽

Smooth Transitions

Abstract Background Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape and requires manual annotation. With the advent of in situ sequencing technologies and automated approaches, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments. Results Here, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that a fully unsupervised approach can computationally define anatomical compartments, which are highly reproducible across individuals, using as few as 18 gene markers. We also show that morphological variation does not always follow gene expression, and different spatial compartments can be defined by various cell types with common morphological features but distinct gene expression profiles. Conclusion We show that spatial gene expression data can be used for unsupervised and unbiased annotations of mouse brain spatial compartments based only on molecular markers, without the need of subjective manual annotations based on tissue and cell morphology or matching reference atlases.

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G2S3: A gene graph-based imputation method for single-cell RNA sequencing data

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009029 ◽

2021 ◽

Vol 17 (5) ◽

pp. e1009029

Author(s):

Weimiao Wu ◽

Yunqing Liu ◽

Qile Dai ◽

Xiting Yan ◽

Zuoheng Wang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Sequencing Data ◽

High Data ◽

Study Gene Expression ◽

Single Cell Rna Sequencing

Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing imputation methods to eight single-cell transcriptomic datasets and compared their performance. Our results demonstrated that G2S3 has superior overall performance in recovering gene expression, identifying cell subtypes, reconstructing cell trajectories, identifying differentially expressed genes, and recovering gene regulatory and correlation relationships. Moreover, G2S3 is computationally efficient for imputation in large-scale single-cell transcriptomic datasets.

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Dynamic Characterization of Structural, Molecular, and Electrophysiological Phenotypes of Human-Induced Pluripotent Stem Cell-Derived Cerebral Organoids, and Comparison with Fetal and Adult Gene Profiles

Cells ◽

10.3390/cells9051301 ◽

2020 ◽

Vol 9 (5) ◽

pp. 1301 ◽

Cited By ~ 5

Author(s):

Sarah Logan ◽

Thiago Arzua ◽

Yasheng Yan ◽

Congshan Jiang ◽

Xiaojie Liu ◽

...

Keyword(s):

Gene Expression ◽

Human Brain ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Adult Brain ◽

Brain Diseases ◽

Gene Profiles ◽

Health And Disease ◽

Induced Pluripotent ◽

First Time

Background: The development of 3D cerebral organoid technology using human-induced pluripotent stem cells (iPSCs) provides a promising platform to study how brain diseases are appropriately modeled and treated. So far, understanding of the characteristics of organoids is still in its infancy. The current study profiled, for the first time, the electrophysiological properties of organoids at molecular and cellular levels and dissected the potential age equivalency of 2-month-old organoids to human ones by a comparison of gene expression profiles among cerebral organoids, human fetal and adult brains. Results: Cerebral organoids exhibit heterogeneous gene and protein markers of various brain cells, such as neurons, astrocytes, and vascular cells (endothelial cells and smooth muscle cells) at 2 months, and increases in neural, glial, vascular, and channel-related gene expression over a 2-month differentiation course. Two-month organoids exhibited action potentials, multiple channel activities, and functional electrophysiological responses to the anesthetic agent propofol. A bioinformatics analysis of 20,723 gene expression profiles showed the similar distance of gene profiles in cerebral organoids to fetal and adult brain tissues. The subsequent Ingenuity Pathway Analysis (IPA) of select canonical pathways related to neural development, network formation, and electrophysiological signaling, revealed that only calcium signaling, cyclic adenosine monophosphate (cAMP) response element-binding protein (CREB) signaling in neurons, glutamate receptor signaling, and synaptogenesis signaling were predicted to be downregulated in cerebral organoids relative to fetal samples. Nearly all cerebral organoid and fetal pathway phenotypes were predicted to be downregulated compared with adult tissue. Conclusions: This novel study highlights dynamic development, cellular heterogeneity and electrophysiological activity. In particular, for the first time, electrophysiological drug response recapitulates what occurs in vivo, and neural characteristics are predicted to be highly similar to the human brain, further supporting the promising application of the cerebral organoid system for the modeling of the human brain in health and disease. Additionally, the studies from these characterizations of cerebral organoids in multiple levels and the findings from gene comparisons between cerebral organoids and humans (fetuses and adults) help us better understand this cerebral organoid-based cutting-edge platform and its wide uses in modeling human brain in terms of health and disease, development, and testing drug efficacy and toxicity.

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Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments

BMC Medical Genomics ◽

10.1186/s12920-020-00759-0 ◽

2020 ◽

Vol 13 (S8) ◽

Author(s):

Nicolas Borisov ◽

Maxim Sorokin ◽

Victor Tkachev ◽

Andrew Garazha ◽

Anton Buzdin

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Low Grade ◽

Sequencing Data ◽

Kidney Tumors ◽

Pediatric Leukemia ◽

Adult Leukemia

Abstract Background Machine learning (ML) methods still have limited applicability in personalized oncology due to low numbers of available clinically annotated molecular profiles. This doesn’t allow sufficient training of ML classifiers that could be used for improving molecular diagnostics. Methods We reviewed published datasets of high throughput gene expression profiles corresponding to cancer patients with known responses on chemotherapy treatments. We browsed Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Tumor Alterations Relevant for GEnomics-driven Therapy (TARGET) repositories. Results We identified data collections suitable to build ML models for predicting responses on certain chemotherapeutic schemes. We identified 26 datasets, ranging from 41 till 508 cases per dataset. All the datasets identified were checked for ML applicability and robustness with leave-one-out cross validation. Twenty-three datasets were found suitable for using ML that had balanced numbers of treatment responder and non-responder cases. Conclusions We collected a database of gene expression profiles associated with clinical responses on chemotherapy for 2786 individual cancer cases. Among them seven datasets included RNA sequencing data (for 645 cases) and the others – microarray expression profiles. The cases represented breast cancer, lung cancer, low-grade glioma, endothelial carcinoma, multiple myeloma, adult leukemia, pediatric leukemia and kidney tumors. Chemotherapeutics included taxanes, bortezomib, vincristine, trastuzumab, letrozole, tipifarnib, temozolomide, busulfan and cyclophosphamide.

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The effects of a globin blocker on the resolution of 3’mRNA sequencing data in porcine blood

10.21203/rs.2.9873/v1 ◽

2019 ◽

Author(s):

Kyu-Sang Lim ◽

Qian Dong ◽

Pamela Renate Moll ◽

Jana Vitkovska ◽

Gregor Wiktorin ◽

...

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Data Sets ◽

Sequencing Data ◽

Data Set ◽

Phenotypic Differences ◽

Mrna Sequencing ◽

Porcine Blood ◽

Preparation Step

Abstract Background Gene expression profiling in blood is a potential source of biomarkers to evaluate or predict phenotypic differences between pigs but is expensive and inefficient because of the high abundance of hemoglobin (HB) mRNA in porcine blood. These limitations can be overcome by the use of QuantSeq 3’mRNA sequencing (QuantSeq) combined with a method to deplete or block the processing of HB mRNA prior to or during library construction. Here, we validated the effectiveness of QuantSeq using a novel specific globin blocker (GB) that is included in the library preparation step of QuantSeq. Results In data set 1, four concentrations of the GB were applied to RNA samples from two pigs. The GB significantly reduced the proportion of HB reads compared to non-GB (NGB) samples (P = 0.005) and increased the number of detectable non-HB genes. The second highest concentration C2, which showed very similar globin depletion rates (from 56.4 to 12%) as C1 but a better correlation of the expression of non-HB genes between NGB and GB (r = 0.98), allowed the expression of an additional 1,295 non-HB genes to be detected, although 40 genes that were detected in the NGB sample (at a low level) were not present in the GB library. Concentration C2 was applied in the rest of the study. In data set 2, the distribution of the percentage of HB reads for NGB (n=184) and GB (n=189) samples clearly showed the effects of the GB on reducing HB reads. Data set 3 (n=84) revealed that the proportion of HB reads that remained in GB samples was significantly and positively correlated with the reticulocyte count in the original blood sample (P < 0.001). Conclusions The effect of the GB on reducing the proportion of HB reads in porcine blood QuantSeq was demonstrated in three data sets. In addition to increasing the efficiency of sequencing non-HB mRNA, the GB for QuantSeq has as advantage that it does not require an additional step prior to or during library creation. Therefore, the GB is a useful tool in the quantification of whole gene expression profiles in porcine blood.

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