Supervised Learning With Perceptual Similarity for Multimodal Gene Expression Registration of a Mouse Brain Atlas

The acquisition of high quality maps of gene expression in the rodent brain is of fundamental importance to the neuroscience community. The generation of such datasets relies on registering individual gene expression images to a reference volume, a task encumbered by the diversity of staining techniques employed, and by deformations and artifacts in the soft tissue. Recently, deep learning models have garnered particular interest as a viable alternative to traditional intensity-based algorithms for image registration. In this work, we propose a supervised learning model for general multimodal 2D registration tasks, trained with a perceptual similarity loss on a dataset labeled by a human expert and augmented by synthetic local deformations. We demonstrate the results of our approach on the Allen Mouse Brain Atlas (AMBA), comprising whole brain Nissl and gene expression stains. We show that our framework and design of the loss function result in accurate and smooth predictions. Our model is able to generalize to unseen gene expressions and coronal sections, outperforming traditional intensity-based approaches in aligning complex brain structures.

Download Full-text

Automated high-throughput registration for localizing 3D mouse brain gene expression using ITK

The Insight Journal ◽

10.54294/9nkczd ◽

2005 ◽

Author(s):

Lydia Ng ◽

Michael Hawrylycz ◽

David Haynor

Keyword(s):

Gene Expression ◽

Mouse Brain ◽

High Throughput ◽

Image Data ◽

Brain Structures ◽

Brain Atlas ◽

The Public ◽

Brain Gene Expression ◽

Genome Wide ◽

Registration Scheme

The Allen Brain Atlas (ABA) project aims to create a cellular-resolution, genome-wide map of gene expression in the adult mouse brain. The resulting in situ hybridization (ISH) image data will be available free-of-charge to the public. Additionally, we are developing an informatics pipeline to support searching of the data by anatomic region and expression level and/or pattern. This paper describes a robust, high-throughput registration scheme to automatically annotate hierarchical brain structures in the ISH imagery.

Download Full-text

Homologous laminar organization of the mouse and human subiculum

Scientific Reports ◽

10.1038/s41598-021-81362-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Michael S. Bienkowski ◽

Farshid Sepehrband ◽

Nyoman D. Kurniawan ◽

Jim Stanis ◽

Laura Korobkova ◽

...

Keyword(s):

Gene Expression ◽

Hippocampal Formation ◽

Expression Patterns ◽

Pyramidal Cells ◽

Brain Structures ◽

Longitudinal Axis ◽

Brain Atlas ◽

Fiber Density ◽

Laminar Organization ◽

Hybridization Data

AbstractThe subiculum is the major output component of the hippocampal formation and one of the major brain structures most affected by Alzheimer’s disease. Our previous work revealed a hidden laminar architecture within the mouse subiculum. However, the rotation of the hippocampal longitudinal axis across species makes it unclear how the laminar organization is represented in human subiculum. Using in situ hybridization data from the Allen Human Brain Atlas, we demonstrate that the human subiculum also contains complementary laminar gene expression patterns similar to the mouse. In addition, we provide evidence that the molecular domain boundaries in human subiculum correspond to microstructural differences observed in high resolution MRI and fiber density imaging. Finally, we show both similarities and differences in the gene expression profile of subiculum pyramidal cells within homologous lamina. Overall, we present a new 3D model of the anatomical organization of human subiculum and its evolution from the mouse.

Download Full-text

Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy

Scientific Reports ◽

10.1038/s41598-021-85807-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Norio Takata ◽

Nobuhiko Sato ◽

Yuji Komaki ◽

Hideyuki Okano ◽

Kenji F. Tanaka

Keyword(s):

Mouse Brain ◽

Brain Structures ◽

Brain Atlas ◽

Resting State Functional Connectivity ◽

Brain Science ◽

Step Procedure ◽

Public Resources ◽

Large Brain ◽

Magnetic Resonance Imaging Mri ◽

And Function

AbstractA brain atlas is necessary for analyzing structure and function in neuroimaging research. Although various annotation volumes (AVs) for the mouse brain have been proposed, it is common in magnetic resonance imaging (MRI) of the mouse brain that regions-of-interest (ROIs) for brain structures (nodes) are created arbitrarily according to each researcher’s necessity, leading to inconsistent ROIs among studies. One reason for such a situation is the fact that earlier AVs were fixed, i.e. combination and division of nodes were not implemented. This report presents a pipeline for constructing a flexible annotation atlas (FAA) of the mouse brain by leveraging public resources of the Allen Institute for Brain Science on brain structure, gene expression, and axonal projection. A mere two-step procedure with user-specified, text-based information and Python codes constructs FAA with nodes which can be combined or divided objectively while maintaining anatomical hierarchy of brain structures. Four FAAs with total node count of 4, 101, 866, and 1381 were demonstrated. Unique characteristics of FAA realized analysis of resting-state functional connectivity (FC) across the anatomical hierarchy and among cortical layers, which were thin but large brain structures. FAA can improve the consistency of whole brain ROI definition among laboratories by fulfilling various requests from researchers with its flexibility and reproducibility.

Download Full-text

Biological Image Analysis via Matrix Approximation

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch027 ◽

2011 ◽

pp. 166-170

Author(s):

Jieping Ye ◽

Ravi Janardan ◽

Sudhir Kumar

Keyword(s):

Gene Expression ◽

Expression Pattern ◽

Expression Patterns ◽

Gene Expression Pattern ◽

Controlled Vocabulary ◽

Gene Expression Patterns ◽

Image Content ◽

Gene Expressions ◽

Microarray Gene Expression ◽

Individual Gene

Understanding the roles of genes and their interactions is one of the central challenges in genome research. One popular approach is based on the analysis of microarray gene expression data (Golub et al., 1999; White, et al., 1999; Oshlack et al., 2007). By their very nature, these data often do not capture spatial patterns of individual gene expressions, which is accomplished by direct visualization of the presence or absence of gene products (mRNA or protein) (e.g., Tomancak et al., 2002; Christiansen et al., 2006). For instance, the gene expression pattern images of a Drosophila melanogaster embryo capture the spatial and temporal distribution of gene expression patterns at a given developmental stage (Bownes, 1975; Tsai et al., 1998; Myasnikova et al., 2002; Harmon et al., 2007). The identification of genes showing spatial overlaps in their expression patterns is fundamentally important to formulating and testing gene interaction hypotheses (Kumar et al., 2002; Tomancak et al., 2002; Gurunathan et al., 2004; Peng & Myers, 2004; Pan et al., 2006). Recent high-throughput experiments of Drosophila have produced over fifty thousand images (http://www. fruitfly.org/cgi-bin/ex/insitu.pl). It is thus desirable to design efficient computational approaches that can automatically retrieve images with overlapping expression patterns. There are two primary ways of accomplishing this task. In one approach, gene expression patterns are described using a controlled vocabulary, and images containing overlapping patterns are found based on the similarity of textual annotations. In the second approach, the most similar expression patterns are identified by a direct comparison of image content, emulating the visual inspection carried out by biologists [(Kumar et al., 2002); see also www.flyexpress.net]. The direct comparison of image content is expected to be complementary to, and more powerful than, the controlled vocabulary approach, because it is unlikely that all attributes of an expression pattern can be completely captured via textual descriptions. Hence, to facilitate the efficient and widespread use of such datasets, there is a significant need for sophisticated, high-performance, informatics-based solutions for the analysis of large collections of biological images.

Download Full-text

Retinal Genomic Fabrics Remodeling after Optic Nerve Injury

10.20944/preprints202004.0016.v2 ◽

2021 ◽

Author(s):

Pedro Henrique Victorino ◽

Camila Marra ◽

Dumitru Andrei Iacobas ◽

Sanda Iacobas ◽

David C Spray ◽

...

Keyword(s):

Gene Expression ◽

Optic Nerve ◽

Notch Signaling ◽

Signaling Pathway ◽

Molecular Mechanisms ◽

Notch Signaling Pathway ◽

Complement Cascade ◽

Gene Expressions ◽

Nerve Crush ◽

Individual Gene

Glaucoma is a multifactorial neurodegenerative disease, characterized by degeneration of the retinal ganglion cells (RGCs). There has been little progress in developing efficient strategies for neuroprotection in glaucoma. We profiled the retina transcriptome of Lister Hooded rats at 2 weeks after optic nerve crush (ONC) and analyzed the data from the Genomic Fabric Paradigm (GFP) to bring additional insights into the molecular mechanisms of the retinal remodeling after induction of RGC degeneration. GFP considers for the expression of each gene 3 independent characteristics: level, variability and correlation with each other gene. Thus, the 17,657 quantified genes our study generated a total of 155,911,310 values to analyze. This represents 8,830x more data per condition than a traditional transcriptomic analysis. ONC led to a 57% reduction in RGC numbers as detected by retrograde labeling with DiI. We observed a higher Relative Expression Variability after ONC. Gene expression stability was used as a measure of transcription control and disclosed a robust reduction in the number of very stably expressed genes. Predicted Protein-Protein interaction (PPI) analysis with STRING revealed axon and neuron projection as mostly decreased processes, consistent with RGC degeneration. Conversely, immune response PPIs were found among up-regulated genes. Enrichment analysis showed that Complement Cascade and Notch Signaling Pathway, as well as Oxidative Stress and Kit Receptor Pathway were affected after ONC. To expand our studies of altered molecular pathways, we examined the pair-wise coordination of gene expressions within each pathway and within the entire transcriptome using Pearson correlations. ONC increased the number of synergistically coordinated pairs of genes and the number of similar profiles mainly in Complement Cascade and Notch Signaling Pathway. This deep bioinformatic study provides novel insights beyond the regulation of individual gene expression and discloses changes in the control of expression of Complement Cascade and Notch Signaling functional pathways that may be relevant for both RGC degeneration and remodeling of the retinal tissue after ONC.

Download Full-text

Modulatory effect of olanzapine on SMIM20/phoenixin, NPQ/spexin and NUCB2/nesfatin-1 gene expressions in the rat brainstem

Pharmacological Reports ◽

10.1007/s43440-021-00267-7 ◽

2021 ◽

Author(s):

Artur Pałasz ◽

Piotr Żarczyński ◽

Katarzyna Bogus ◽

Kinga Mordecka-Chamera ◽

Alessandra Della Vecchia ◽

...

Keyword(s):

Gene Expression ◽

Mrna Expression ◽

Mrna Level ◽

Brain Structures ◽

Term Treatment ◽

Sprague Dawley ◽

Gene Expressions ◽

Alternative Mode ◽

Long Term Treatment ◽

Rat Brainstem

Abstract Background Phoenixin, spexin and nesfatin-1 belong to a family of newly discovered multifunctional neuropeptides that play regulatory roles in several brain structures and modulate the activity of important neural networks. However, little is known about their expression and action at the level of brainstem. The present work was, therefore, focused on gene expression of the aforementioned peptides in the brainstem of rats chronically treated with olanzapine, a second generation antipsychotic drug. Methods Studies were carried out on adult, male Sprague–Dawley rats that were divided into 2 groups: control and experimental animals treated with olanzapine (28-day-long intraperitoneal injection, at dose 5 mg/kg daily). All individuals were killed under anesthesia and the brainstem excised. Total mRNA was isolated from homogenized samples of both structures and the RT-PCR method was used for estimation of related SMIM20/phoenixin, NPQ/spexin and NUCB2/nesfatin-1 gene expression. Results Long-term treatment with olanzapine is reflected in qualitatively different changes in expression of examined neuropeptides mRNA in the rat brainstem. Olanzapine significantly decreased NPQ/spexin mRNA expression, but increased SMIM20/phoenixin mRNA level in the rat brainstem; while NUCB2/nesfatin-1 mRNA expression remained unchanged. Conclusions Olanzapine can affect novel peptidergic signaling in the rat brainstem. This may cautiously suggest the presence of an alternative mode of its action.

Download Full-text

Retinal Genomic Fabric Remodeling after Optic Nerve Injury

Genes ◽

10.3390/genes12030403 ◽

2021 ◽

Vol 12 (3) ◽

pp. 403

Author(s):

Pedro Henrique Victorino ◽

Camila Marra ◽

Dumitru Andrei Iacobas ◽

Sanda Iacobas ◽

David C. Spray ◽

...

Keyword(s):

Gene Expression ◽

Optic Nerve ◽

Notch Signaling ◽

Signaling Pathway ◽

Molecular Mechanisms ◽

Notch Signaling Pathway ◽

Complement Cascade ◽

Gene Expressions ◽

Nerve Crush ◽

Individual Gene

Glaucoma is a multifactorial neurodegenerative disease, characterized by degeneration of the retinal ganglion cells (RGCs). There has been little progress in developing efficient strategies for neuroprotection in glaucoma. We profiled the retina transcriptome of Lister Hooded rats at 2 weeks after optic nerve crush (ONC) and analyzed the data from the genomic fabric paradigm (GFP) to bring additional insights into the molecular mechanisms of the retinal remodeling after induction of RGC degeneration. GFP considers three independent characteristics for the expression of each gene: level, variability, and correlation with each other gene. Thus, the 17,657 quantified genes in our study generated a total of 155,911,310 values to analyze. This represents 8830x more data per condition than a traditional transcriptomic analysis. ONC led to a 57% reduction in RGC numbers as detected by retrograde labeling with 1,1′-dioctadecyl-3,3,3,3′-tetramethylindocarbocyanine perchlorate (DiI). We observed a higher relative expression variability after ONC. Gene expression stability was used as a measure of transcription control and disclosed a robust reduction in the number of very stably expressed genes. Predicted protein–protein interaction (PPI) analysis with STRING revealed axon and neuron projection as mostly decreased processes, consistent with RGC degeneration. Conversely, immune response PPIs were found among upregulated genes. Enrichment analysis showed that complement cascade and Notch signaling pathway, as well as oxidative stress and kit receptor pathway were affected after ONC. To expand our studies of altered molecular pathways, we examined the pairwise coordination of gene expressions within each pathway and within the entire transcriptome using Pearson correlations. ONC increased the number of synergistically coordinated pairs of genes and the number of similar profiles mainly in complement cascade and Notch signaling pathway. This deep bioinformatic study provided novel insights beyond the regulation of individual gene expression and disclosed changes in the control of expression of complement cascade and Notch signaling functional pathways that may be relevant for both RGC degeneration and remodeling of the retinal tissue after ONC.

Download Full-text