Systems spatiotemporal dynamics of traumatic brain injury at single cell resolution reveals humanin as a therapeutic target

Abstract The etiology of mild traumatic brain injury (mTBI) remains elusive due to the tissue and cellular heterogeneity of the affected brain regions that underlie cognitive impairments and subsequent neurological disorders. This complexity is further exacerbated by disrupted circuits within and between cell populations across brain regions and the periphery, which occur at different timescales and in spatial domains. We profiled three tissues (hippocampus, frontal cortex, and blood leukocytes) at the acute (24hr) and chronic (7days) phases of mTBI at single cell resolution and demonstrated that the coordinated gene expression patterns across cell types were disrupted and re-organized by TBI at different timescales with distinct regional and cellular patterns. Gene expression-based network modeling identified astrocytes as a key regulator of the cell-cell coordination following mTBI in both hippocampus and frontal cortex across timepoints, and mt-Rnr2, which encodes the mitochondrial peptide humanin, as a potential target for intervention based on its broad regional and dynamic dysregulation following mTBI. Treatment of a murine mTBI model with humanin reversed cognitive impairment caused by mTBI through the restoration of metabolic pathways within astrocytes. Our results offer a systems-level understanding of the dynamic and spatial regulation of gene programs by mTBI and pinpoint key target genes, pathways, and cell circuits that are amenable to therapeutics.

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Beyond comparisons of means: understanding changes in gene expression at the single-cell level

10.1101/035949 ◽

2016 ◽

Author(s):

Catalina A Vallejos ◽

Sylvia Richardson ◽

John C Marioni

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Patterns ◽

Probabilistic Approach ◽

Embryonic Stem ◽

Cellular Heterogeneity ◽

Bayesian Hierarchical ◽

False Discovery ◽

Rich Information ◽

The Rich

Single-cell RNA sequencing (scRNA-seq) can be used to characterise differences in gene expression patterns between pre-specified populations of cells. Traditionally, differential expression tools are restricted to the study of changes in overall expression between cell populations. However, such analyses do not take full advantage of the rich information provided by scRNA-seq. In this article, we present a Bayesian hierarchical model which can be used to study changes in expression that lie beyond comparisons of means. In particular, our method can highlight genes that undergo changes in cell-to-cell heterogeneity between the populations but whose overall expression is preserved. Evidence supporting these changes is quantified using a probabilistic approach based on tail posterior probabilities, where a probability cut-off is calibrated through the expected false discovery rate. Our method incorporates a built-in normalisation strategy and quantifies technical artefacts by borrowing information from technical spike-in genes. Control experiments validate the performance of our approach. Finally, we compare expression patterns of mouse embryonic stem cells between different stages of the cell cycle, revealing substantial differences in cellular heterogeneity.

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A 3D gene expression atlas of the floral meristem based on spatial reconstruction of single nucleus RNA sequencing data

10.1101/2021.06.30.450319 ◽

2021 ◽

Author(s):

Manuel Neumann ◽

Xiaocai Xu ◽

Cezary Smaczniak ◽

Julia Schumacher ◽

Wenhao Yan ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Patterns ◽

Floral Meristem ◽

Cellular Heterogeneity ◽

Sequencing Data ◽

Gene Expression Atlas ◽

Spatial Reconstruction ◽

Expression Atlas ◽

3D Gene

Identity and functions of plant cells are influenced by their precise cellular location within the plant body. Cellular heterogeneity in growth and differentiation trajectories results in organ patterning. Therefore, assessing this heterogeneity at molecular scale is a major question in developmental biology. Single-cell transcriptomics (scRNA-seq) allows to characterize and quantify gene expression heterogeneity in developing organs at unprecedented resolution. However, the original physical location of the cell is lost during the scRNA-seq procedure. To recover the original location of cells is essential to link gene activity with cellular function and morphology. Here, we reconstruct genome-wide gene expression patterns of individual cells in a floral meristem by combining single-nuclei RNA-seq with 3D spatial reconstruction. By this, gene expression differences among meristematic domains giving rise to different tissue and organ types can be determined. As a proof of principle, the data are used to trace the initiation of vascular identity within the floral meristem. Our work demonstrates the power of spatially reconstructed single cell transcriptome atlases to understand plant morphogenesis. The floral meristem 3D gene expression atlas can be accessed at http://threed-flower-meristem.herokuapp.com

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What Do Machines Tell us About Dementia? Machine Learning Applied to Aging, Dementia and Traumatic Brain Injury Study

10.21203/rs.3.rs-840907/v1 ◽

2021 ◽

Author(s):

Denis Arthur Pinheiro Moura ◽

Joao Ricardo Mendes de Oliveira

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Traumatic Brain Injury ◽

Brain Injury ◽

Expression Profiles ◽

Brain Regions ◽

Machine Learning Algorithms ◽

Differentially Expressed ◽

Classification Model ◽

Specific Gene

Abstract Dementia, a syndrome characterized by the progressive deterioration of memory and cognition, arises from different pathologies, with Alzheimer's Disease (AD) its most common cause. Patterns of gene expression during dementia of different etiologies may function as generalist biomarkers of the condition. We used RNA-Seq data from the Allen Dementia and Traumatic Brain Injury Study (ADTBI) to identify differentially expressed genes in brains with dementia. Machine Learning algorithms Decision Trees (DT) and Random Forest (RF) were used to create models to identify dementia samples based on their gene expression profile. Importance analyses were conducted to identify the most relevant genes in each classification model. A total of 1629 differentially expressed (DE) genes were found in brains with the condition. Gene PAN3-AS1 was the only DE gene across more than three brain regions. The artificial intelligence models were capable of identifying correctly up to 92.85% of dementia samples. Our analyses provide interesting insights regarding using brain-specific gene expression profiles as biomarkers of dementia, identifying genes possibly involved with dementia, and guiding future studies in prediction and early identification of the syndrome.

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C-terminal binding proteins 1 and 2 in traumatic brain injury-induced inflammation and their inhibition as an approach for anti-inflammatory treatment

10.1101/820258 ◽

2019 ◽

Author(s):

Hong Li ◽

Caiguo Zhang ◽

Chunxia Yang ◽

Melanie Blevins ◽

David Norris ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Inflammatory Response ◽

Molecular Mechanisms ◽

Target Genes ◽

Dependent Manner ◽

Blood Leukocytes ◽

Cellular Processes ◽

Transcriptional Coregulators ◽

The Central Nervous System

AbstractTraumatic brain injury (TBI) induces an acute inflammatory response in the central nervous system that involves both resident and peripheral immune cells. The ensuing chronic neuroinflammation causes cell death and tissue damage and may contribute to neurodegeneration. The molecular mechanisms involved in the maintenance of this chronic inflammation state remain underexplored. C-terminal binding protein (CtBP) 1 and 2 are transcriptional coregulators that repress diverse cellular processes. Unexpectedly, we find that the CtBPs can transactivate a common set of proinflammatory genes both in lipopolysaccharide-activated microglia, astrocytes and macrophages, and in a mouse model of the mild form of TBI. We also find that the expression of these genes is markedly enhanced by a single mild injury in both brain and peripheral blood leukocytes in a severity- and time-dependent manner. Moreover, we were able to demonstrate that specific inhibitors of the CtBPs effectively suppress the expression of the CtBP target genes and thus improve neurological outcome in mice receiving single and repeated mild TBIs. This discovery suggests new avenues for therapeutic modulation of the inflammatory response to brain injury.

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Gene expression patterns following unilateral traumatic brain injury reveals a local pro-inflammatory and remote anti-inflammatory response

BMC Genomics ◽

10.1186/1471-2164-14-282 ◽

2013 ◽

Vol 14 (1) ◽

pp. 282 ◽

Cited By ~ 40

Author(s):

Todd E White ◽

Gregory D Ford ◽

Monique C Surles-Zeigler ◽

Alicia S Gates ◽

Michelle C LaPlaca ◽

...

Keyword(s):

Gene Expression ◽

Traumatic Brain Injury ◽

Brain Injury ◽

Inflammatory Response ◽

Expression Patterns ◽

Gene Expression Patterns ◽

Anti Inflammatory

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The Gene Coexpression Analysis Identifies Functional Modules Dynamically Changed After Traumatic Brain Injury

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/5511598 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Zhi-jie Zhao ◽

Dong-po Wei ◽

Rui-zhe Zheng ◽

Tinghua Peng ◽

Xiang Xiao ◽

...

Keyword(s):

Gene Expression ◽

Traumatic Brain Injury ◽

Brain Injury ◽

Cell Activation ◽

Expression Profiles ◽

Expression Patterns ◽

Protein Translation ◽

Functional Modules ◽

Coexpression Analysis ◽

Gene Coexpression

Traumatic brain injury (TBI) is a major cause of morbidity and mortality, both in adult and pediatric populations. However, the dynamic changes of gene expression profiles following TBI have not been fully understood. In this study, we identified the differentially expressed genes (DEGs) following TBI. Remarkably, Serpina3n, Asf1b, Folr1, LOC100366216, Clec12a, Olr1, Timp1, Hspb1, Lcn2, and Spp1 were identified as the top 10 with the highest statistical significance. The weighted gene coexpression analysis (WGCNA) identified 12 functional modules from the DEGs, which showed specific expression patterns over time and were characterized by enrichment analysis. Specifically, the black and turquoise modules were mainly involved in energy metabolism and protein translation. The green yellow and yellow modules including Hmox1, Mif, Anxa2, Timp1, Gfap, Cd9, Gja1, Pdpn, and Gpx1 were related to response to wounding, indicating that expression of these genes such as Hmox1, Anxa2, and Timp1 could protect the brains from brain injury. The green yellow module highlighted genes involved in microglial cell activation such as Tyrobp, Cx3cr1, Grn, Trem2, C1qa, and Aif1, suggesting that these genes were responsible for the inflammatory response caused by TBI. The upregulation of these genes has been validated in an independent dataset. These results indicated that the key genes in microglia cell activation may serve as a promising therapeutic target for TBI. In summary, the present study provided a full view of the dynamic gene expression changes following TBI.

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Identification of target genes in neuroinflammation and neurodegeneration after traumatic brain injury in rats

PeerJ ◽

10.7717/peerj.8324 ◽

2019 ◽

Vol 7 ◽

pp. e8324 ◽

Cited By ~ 1

Author(s):

Jianwei Zhao ◽

Chen Xu ◽

Heli Cao ◽

Lin Zhang ◽

Xuyang Wang ◽

...

Keyword(s):

Gene Expression ◽

Traumatic Brain Injury ◽

Brain Injury ◽

Signaling Pathway ◽

Target Genes ◽

Gene Expression Omnibus ◽

Integrated Analysis ◽

Positive Regulation ◽

Chronic Neuroinflammation ◽

Number Of Patients

Background Traumatic brain injury (TBI) is a common neurological emergency observed in hospitals. A considerable number of patients suffer from long-term disabilities after TBI. This study aimed to identify altered gene expression signatures and mechanisms related to TBI-induced chronic neuroinflammation and neurodegeneration. Methods An integrated analysis was performed using published RNA-sequencing studies to determine TBI-induced differentially expressed genes (DEGs). Based on the DEG data, functional annotation, signal-net, and transcription factor analyses were conducted to understand the mechanism of chronic neuroinflammation and neurodegeneration induced after TBI. Results Two datasets were obtained using the Gene Expression Omnibus database, of which, 6,513 DEGs were identified (6,464 upregulated and 49 downregulated). Positive regulation of biological process, positive regulation of cellular process, nucleus, and heterocyclic compound binding were Gene Ontology terms significantly enriched in post-TBI rat models. Leukocyte transendothelial migration, chemokine signaling pathway, neurotrophin signaling pathway, and longevity-regulating pathway were significantly enriched after TBI. With regard to the signal-net analysis, FOXO3, DGKZ and ILK were considered the most critical genes derived using high–betweenness centrality calculation. A total of 44 TFs, including FOXO1, SRY and KLF4, were predicted to play an important role in the upregulation of gene expression. Using integrated bioinformatics analysis, TBI was found to be associated with a significant inflammatory response and neurodegeneration. FOXO3, apolipoprotein (APOE), microtubule-associated protein tau (MAPT), and TREM2 were probably associated with the TBI pathological process. The mitochondrial electron transport chain may be associated with neurodegeneration in patients with TBI, serving as a potential therapeutic target.

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3521 Distinct single cell gene expression in peripheral blood monocytes correlates with treatment response groups to TNF-alpha inhibition in rheumatoid arthritis

Journal of Clinical and Translational Science ◽

10.1017/cts.2019.24 ◽

2019 ◽

Vol 3 (s1) ◽

pp. 8-9

Author(s):

Theresa Wampler Muskardin ◽

Zhongbo Jin ◽

Jessica M. Dorschner ◽

Yogita Ghodke-Puranik ◽

Timothy Niewold

Keyword(s):

Gene Expression ◽

Single Cell ◽

Peripheral Blood ◽

Target Genes ◽

Biologic Therapy ◽

Expression Patterns ◽

Type I ◽

Type I Ifn ◽

Cellular Mechanisms ◽

Α Activity

OBJECTIVES/SPECIFIC AIMS: The cellular mechanisms that underlie the IFNβ/α ratio that predicts response are not known. Effects of IFN on single immune cells may be masked in whole blood or mixed cell populations. By studying the effect of IFNβ/α activity ratio on individual monocytes, we can determine the functional impact of the IFN ratio and suggest the cellular mechanisms that underlie response/non-response to TNFi therapy in RA. METHODS/STUDY POPULATION: We used single cell analysis to investigate whether monocyte gene expression differs significantly between RA patients according to their pre-TNFi serum IFN-β/α ratio. Single classical (CL) and non-classical (NC) blood-derived monocytes were isolated from 15 seropositive RA subjects prior to biologic therapy. Subjects were grouped by pre-TNFi serum IFN-β/α ratio into two groups, those with a high IFN-β/α ratio (≥1.3, n = 6) and those with a low IFN-β/α ratio (<1.3, n = 9). 87 target genes were analyzed. Genes that varied significantly between the groups by categorical analyses were tested in multivariate logistic regression models. RESULTS/ANTICIPATED RESULTS: Every participant was seropositive for rheumatoid factor and antibodies to cyclic citrullinated peptide. Among the participants in the groups, there were no significant differences in age or DAS scores (P>0.05). The treatments were comparable and none were being treated with biologic therapy. There were striking differences in monocyte gene expression between patients with pre-treatment blood IFNβ/α activity <1.3 and ≥1.3. Expression of (1) key type I IFN pathway genes (JAK1, STAT2, IFIT2, IFIH1, PRDM1); (2) IL12; (3) CD36; and (4) CTLA4 were the strongest differentiators between groups (p<0.0001 for each, corrected for multiple comparisons). DISCUSSION/SIGNIFICANCE OF IMPACT: In this study we were able to measure gene expression in single monocytes from seropositive RA patients prior to biologic treatment. Within-cell co-expression patterns demonstrate biological differences in monocytes of RA patients with an IFNβ/α ≥1.3, the ratio of type I IFNs which predicts non-response to TNFi. The data suggest that there may be differential IFN production and pathway activation in patients who do not respond to TNFi. The increased expression of CD36 in monocytes from RA patients with high IFN β/α activity may be a reflection of increased “foam cells” in the inflamed tissue of patients who do not respond to TNFi. Enrichment of CTLA4 in those with high serum IFNβ/α suggests that CTLA4-Ig may be less likely to be an effective alternative for someone who is not likely to respond to TNFi. Current work includes determining whether the peripheral blood findings reflect altered cellular composition, type I IFN production and signaling in the synovium. Significance: This work will help to develop a more individualized approach to therapy in RA and determine an immunological basis of response/non-response to TNFi.

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Codependency and mutual exclusivity for gene community detection from sparse single-cell transcriptome data

10.1101/2021.03.15.435370 ◽

2021 ◽

Author(s):

Natsu Nakajima ◽

Tomoatsu Hayashi ◽

Katsunori Fujiki ◽

Katsuhiko Shirahige ◽

Tetsu Akiyama ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Single Cell Analysis ◽

Expression Patterns ◽

Gene Clusters ◽

Genetic Network ◽

Cellular Heterogeneity ◽

Mutual Exclusivity ◽

Gene Pairs ◽

Serum Stimulation

Single-cell RNA-seq (scRNA-seq) can be used to characterize cellular heterogeneity in thousands of cells. The reconstruction of a gene network based on coexpression patterns is a fundamental task in scRNA-seq analyses, and the mutual exclusivity of gene expression can be critical for understanding such heterogeneity. Here, we propose an approach for detecting communities from a genetic network constructed on the basis of coexpression properties. The community-based comparison of multiple coexpression networks enables the identification of functionally related gene clusters that cannot be fully captured through differential gene expression-based analysis. We also developed a novel metric referred to as the exclusively expressed index (EEI) that identifies mutually exclusive gene pairs from sparse scRNA-seq data. EEI quantifies and ranks the exclusive expression levels of all gene pairs from binary expression patterns while maintaining robustness against a low sequencing depth. We applied our methods to glioblastoma scRNA-seq data and found that gene communities were partially conserved after serum stimulation despite a considerable number of diﬀerentially expressed genes. We also demonstrate that the identification of mutually exclusive gene sets with EEI can improve the sensitivity of capturing cellular heterogeneity. Our methods complement existing approaches and provide new biological insights, even for a large, sparse dataset, in the single-cell analysis field.

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Gene expression following traumatic brain injury in humans: analysis by microarray

Journal of Clinical Neuroscience ◽

10.1016/j.jocn.2004.11.003 ◽

2005 ◽

Vol 12 (3) ◽

pp. 284-290 ◽

Cited By ~ 43

Author(s):

Daniel B. Michael ◽

Donna M. Byers ◽

Louis N. Irwin

Keyword(s):

Gene Expression ◽

Traumatic Brain Injury ◽

Brain Injury

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