Defining the T cell transcriptional landscape in pediatric liver transplant rejection at single cell resolution.

Acute cellular rejection (ACR) affects >80% of pediatric liver transplant recipients within 5 years, and late ACR is associated with graft failure. Traditional anti-rejection therapy for late ACR is ineffective and has remained unchanged for six decades. Although CD8+ T cells promote late ACR, little has been done to define their specificity and gene expression. Here, we used single-cell sequencing and immune repertoire profiling (10X Genomics) on 30 cryopreserved 16G liver biopsies from 14 patients (5 pre-transplant or with no ACR, 9 with ACR). We identified expanded intragraft CD8+ T cell clonotypes (CD8EXP) and their gene expression profiles in response to anti-rejection treatment. Notably, we found that expanded CD8+ clonotypes (CD8EXP) bore markers of effector and CD56hiCD161- 'NK-like' T cells, retaining their clonotype identity and phenotype in subsequent biopsies from the same patients despite histologic ACR resolution. CD8EXP clonotypes localized to portal infiltrates during active ACR, and persisted in the lobule after histologic ACR resolution. CellPhoneDB analysis revealed differential crosstalk between KC and CD8EXP during late ACR, with activation of the LTB-LTBR pathway and downregulation of TGFß signaling. Therefore, persistently-detected intragraft CD8EXP clones remain active despite ACR treatment and may contribute to long-term allograft fibrosis and failure of operational tolerance.

WAT3R: recovery of T-cell receptor variable regions from 3' single-cell RNA-sequencing.

SUMMARY: Diversity of the T-cell receptor (TCR) repertoire is central to adaptive immunity. The TCR is composed of α and ß chains, encoded by the TRA and TRB genes, of which the variable regions determine antigen specificity. To generate novel biological insights into the complex functioning of immune cells, combined capture of variable regions and single-cell transcriptomes provides a compelling approach. Recent developments enable the enrichment of TRA and TRB variable regions from widely used technologies for 3'-based single-cell RNA-sequencing (scRNA-seq). However, a comprehensive computational pipeline to process TCR-enriched data from 3' scRNA-seq is not available. Here, we present an analysis pipeline to process TCR variable regions enriched from 3' scRNA-seq cDNA. The tool reports TRA and TRB nucleotide and amino acid sequences linked to cell barcodes, enabling the reconstruction of T-cell clonotypes with associated transcriptomes. We demonstrate the software using peripheral blood mononuclear cells from a healthy donor and detect TCR sequences in a high proportion of single T cells. Detection of TCR sequences is low in non-T-cell populations, demonstrating specificity. Finally, we show that TCR clones are larger in CD8 Memory T cells than in other T-cell types, indicating an association between T-cell clonotypes and differentiation states. AVAILABILITY AND IMPLEMENTATION: The Workflow for Association of T-cell receptors from 3' single-cell RNA-seq (WAT3R), including test data, is available on GitHub (https://github.com/mainciburu/WAT3R), Docker Hub (https://hub.docker.com/r/mainciburu/wat3r) and a workflow on the Terra platform (https://app.terra.bio). The test dataset is available on GEO (accession number GSE195956). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Single-Cell Multiomics Reveals Clonal T-Cell Expansions and Exhaustion in Blastic Plasmacytoid Dendritic Cell Neoplasm.

The immune system represents a major barrier to cancer progression, driving the evolution of immunoregulatory interactions between malignant cells and T-cells in the tumor environment. Blastic plasmacytoid dendritic cell neoplasm (BPDCN), a rare acute leukemia with plasmacytoid dendritic cell (pDC) differentiation, provides a unique opportunity to study these interactions. pDCs are key producers of interferon alpha (IFNA) that play an important role in T-cell activation at the interface between the innate and adaptive immune system. To assess how uncontrolled proliferation of malignant BPDCN cells affects the tumor environment, we catalog immune cell heterogeneity in the bone marrow (BM) of five healthy controls and five BPDCN patients by analyzing 52,803 single-cell transcriptomes, including 18,779 T-cells. We test computational techniques for robust cell type classification and find that T-cells in BPDCN patients consistently upregulate interferon alpha (IFNA) response and downregulate tumor necrosis factor alpha (TNFA) pathways. Integrating transcriptional data with T-cell receptor sequencing via shared barcodes reveals significant T-cell exhaustion in BPDCN that is positively correlated with T-cell clonotype expansion. By highlighting new mechanisms of T-cell exhaustion and immune evasion in BPDCN, our results demonstrate the value of single-cell multiomics to understand immune cell interactions in the tumor environment.

Mitochondrial variant enrichment from high-throughput single-cell RNA sequencing resolves clonal populations.

The combination of single-cell transcriptomics with mitochondrial DNA variant detection can be used to establish lineage relationships in primary human cells, but current methods are not scalable to interrogate complex tissues. Here, we combine common 3' single-cell RNA-sequencing protocols with mitochondrial transcriptome enrichment to increase coverage by more than 50-fold, enabling high-confidence mutation detection. The method successfully identifies skewed immune-cell expansions in primary human clonal hematopoiesis.

KRSA: An R package and R Shiny web application for an end-to-end upstream kinase analysis of kinome array data.

Phosphorylation by serine-threonine and tyrosine kinases is critical for determining protein function. Array-based platforms for measuring reporter peptide signal levels allow for differential phosphorylation analysis between conditions for distinct active kinases. Peptide array technologies like the PamStation12 from PamGene allow for generating high-throughput, multi-dimensional, and complex functional proteomics data. As the adoption rate of such technologies increases, there is an imperative need for software tools that streamline the process of analyzing such data. We present Kinome Random Sampling Analyzer (KRSA), an R package and R Shiny web-application for analyzing kinome array data to help users better understand the patterns of functional proteomics in complex biological systems. KRSA is an All-In-One tool that reads, formats, fits models, analyzes, and visualizes PamStation12 kinome data. While the underlying algorithm has been experimentally validated in previous publications, we demonstrate KRSA workflow on dorsolateral prefrontal cortex (DLPFC) in male (n = 3) and female (n = 3) subjects to identify differential phosphorylation signatures and upstream kinase activity. Kinase activity differences between males and females were compared to a previously published kinome dataset (11 female and 7 male subjects) which showed similar global phosphorylation signals patterns.

Transcriptional profile of pyramidal neurons in chronic schizophrenia reveals lamina-specific dysfunction of neuronal immunity.

While the pathophysiology of schizophrenia has been extensively investigated using homogenized postmortem brain samples, few studies have examined changes in brain samples with techniques that may attribute perturbations to specific cell types. To fill this gap, we performed microarray assays on mRNA isolated from anterior cingulate cortex (ACC) superficial and deep pyramidal neurons from 12 schizophrenia and 12 control subjects using laser-capture microdissection. Among all the annotated genes, we identified 134 significantly increased and 130 decreased genes in superficial pyramidal neurons, while 93 significantly increased and 101 decreased genes were found in deep pyramidal neurons, in schizophrenia compared to control subjects. In these differentially expressed genes, we detected lamina-specific changes of 55 and 31 genes in superficial and deep neurons in schizophrenia, respectively. Gene set enrichment analysis (GSEA) was applied to the entire pre-ranked differential expression gene lists to gain a complete pathway analysis throughout all annotated genes. Our analysis revealed overrepresented groups of gene sets in schizophrenia, particularly in immunity and synapse-related pathways, suggesting the disruption of these pathways plays an important role in schizophrenia. We also detected other pathways previously demonstrated in schizophrenia pathophysiology, including cytokine and chemotaxis, postsynaptic signaling, and glutamatergic synapses. In addition, we observed several novel pathways, including ubiquitin-independent protein catabolic process. Considering the effects of antipsychotic treatment on gene expression, we applied a novel bioinformatics approach to compare our differential expression gene profiles with 51 antipsychotic treatment datasets, demonstrating that our results were not influenced by antipsychotic treatment. Taken together, we found pyramidal neuron-specific changes in neuronal immunity, synaptic dysfunction, and olfactory dysregulation in schizophrenia, providing new insights for the cell-subtype specific pathophysiology of chronic schizophrenia.

Corticostriatal dysfunction and social interaction deficits in mice lacking the cystine/glutamate antiporter.

The astrocytic cystine/glutamate antiporter system xc- represents an important source of extracellular glutamate in the central nervous system, with potential impact on excitatory neurotransmission. Yet, its function and importance in brain physiology remain incompletely understood. Employing slice electrophysiology and mice with a genetic deletion of the specific subunit of system xc-, xCT (xCT-/- mice), we uncovered decreased neurotransmission at corticostriatal synapses. This effect was partly mitigated by replenishing extracellular glutamate levels, indicating a defect linked with decreased extracellular glutamate availability. We observed no changes in the morphology of striatal medium spiny neurons, the density of dendritic spines, or the density or ultrastructure of corticostriatal synapses, indicating that the observed functional defects are not due to morphological or structural abnormalities. By combining electron microscopy with glutamate immunogold labeling, we identified decreased intracellular glutamate density in presynaptic terminals, presynaptic mitochondria, and in dendritic spines of xCT-/- mice. A proteomic and kinomic screen of the striatum of xCT-/- mice revealed decreased expression of presynaptic proteins and abnormal kinase network signaling, that may contribute to the observed changes in postsynaptic responses. Finally, these corticostriatal deregulations resulted in a behavioral phenotype suggestive of autism spectrum disorder in the xCT-/- mice; in tests sensitive to corticostriatal functioning we recorded increased repetitive digging behavior and decreased sociability. To conclude, our findings show that system xc- plays a previously unrecognized role in regulating corticostriatal neurotransmission and influences social preference and repetitive behavior.

Protocol for Identification and Removal of Doublets with DoubletDecon.

Retention of multiplet captures in single-cell RNA sequencing (scRNA-seq) data can hinder identification of discrete or transitional cell populations and associated marker genes. To overcome this challenge, we created DoubletDecon to identify and remove doublets, multiplets of two cells, by using a combination of deconvolution to identify putative doublets and analyses of unique gene expression. Here, we provide the protocol for running DoubletDecon on scRNA-seq data. For complete details on the use and execution of this protocol, please refer to DePasquale et al. (2019).

DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data.

Methods for single-cell RNA sequencing (scRNA-seq) have greatly advanced in recent years. While droplet- and well-based methods have increased the capture frequency of cells for scRNA-seq, these technologies readily produce technical artifacts, such as doublet cell captures. Doublets occurring between distinct cell types can appear as hybrid scRNA-seq profiles, but do not have distinct transcriptomes from individual cell states. We introduce DoubletDecon, an approach that detects doublets with a combination of deconvolution analyses and the identification of unique cell-state gene expression. We demonstrate the ability of DoubletDecon to identify synthetic, mixed-species, genetic, and cell-hashing cell doublets from scRNA-seq datasets of varying cellular complexity with a high sensitivity relative to alternative approaches. Importantly, this algorithm prevents the prediction of valid mixed-lineage and transitional cell states as doublets by considering their unique gene expression. DoubletDecon has an easy-to-use graphical user interface and is compatible with diverse species and unsupervised population detection algorithms.

cellHarmony: cell-level matching and holistic comparison of single-cell transcriptomes.

To understand the molecular pathogenesis of human disease, precision analyses to define alterations within and between disease-associated cell populations are desperately needed. Single-cell genomics represents an ideal platform to enable the identification and comparison of normal and diseased transcriptional cell populations. We created cellHarmony, an integrated solution for the unsupervised analysis, classification, and comparison of cell types from diverse single-cell RNA-Seq datasets. cellHarmony efficiently and accurately matches single-cell transcriptomes using a community-clustering and alignment strategy to compute differences in cell-type specific gene expression over potentially dozens of cell populations. Such transcriptional differences are used to automatically identify distinct and shared gene programs among cell-types and identify impacted pathways and transcriptional regulatory networks to understand the impact of perturbations at a systems level. cellHarmony is implemented as a python package and as an integrated workflow within the software AltAnalyze. We demonstrate that cellHarmony has improved or equivalent performance to alternative label projection methods, is able to identify the likely cellular origins of malignant states, stratify patients into clinical disease subtypes from identified gene programs, resolve discrete disease networks impacting specific cell-types, and illuminate therapeutic mechanisms. Thus, this approach holds tremendous promise in revealing the molecular and cellular origins of complex disease.

Kinase network dysregulation in a human induced pluripotent stem cell model of DISC1 schizophrenia.

Protein kinases orchestrate signal transduction pathways involved in central nervous system functions ranging from neurodevelopment to synaptic transmission and plasticity. Abnormalities in kinase-mediated signaling are involved in the pathophysiology of neurological disorders, including neuropsychiatric disorders. Here, we expand on the hypothesis that kinase networks are dysregulated in schizophrenia. We investigated changes in serine/threonine kinase activity in cortical excitatory neurons differentiated from induced pluripotent stem cells (iPSCs) from a schizophrenia patient presenting with a 4 bp mutation in the disrupted in schizophrenia 1 (DISC1) gene and a corresponding control. Using kinome peptide arrays, we demonstrate large scale abnormalities in DISC1 cells, including a global depression of serine/threonine kinase activity, and changes in activity of kinases, including AMP-activated protein kinase (AMPK), extracellular signal-regulated kinases (ERK), and thousand-and-one amino acid (TAO) kinases. Using isogenic cell lines in which the DISC1 mutation is either introduced in the control cell line, or rescued in the schizophrenia cell line, we ascribe most of these changes to a direct effect of the presence of the DISC1 mutation. Investigating the gene expression signatures downstream of the DISC1 kinase network, and mapping them on perturbagen signatures obtained from the Library of Integrated Network-based Cellular Signatures (LINCS) database, allowed us to propose novel drug targets able to reverse the DISC1 kinase dysregulation gene expression signature. Altogether, our findings provide new insight into abnormalities of kinase networks in schizophrenia and suggest possible targets for disease intervention.

Chronic Dysregulation of Cortical and Subcortical Metabolism After Experimental Traumatic Brain Injury.

Traumatic brain injury (TBI) is a leading cause of death and long-term disability worldwide. Although chronic disability is common after TBI, effective treatments remain elusive and chronic TBI pathophysiology is not well understood. Early after TBI, brain metabolism is disrupted due to unregulated ion release, mitochondrial damage, and interruption of molecular trafficking. This metabolic disruption causes at least part of the TBI pathology. However, it is not clear how persistent or pervasive metabolic injury is at later stages of injury. Using untargeted 1H-NMR metabolomics, we examined ex vivo hippocampus, striatum, thalamus, frontal cortex, and brainstem tissue in a rat lateral fluid percussion model of chronic brain injury. We found altered tissue concentrations of metabolites in the hippocampus and thalamus consistent with dysregulation of energy metabolism and excitatory neurotransmission. Furthermore, differential correlation analysis provided additional evidence of metabolic dysregulation, most notably in brainstem and frontal cortex, suggesting that metabolic consequences of injury are persistent and widespread. Interestingly, the patterns of network changes were region-specific. The individual metabolic signatures after injury in different structures of the brain at rest may reflect different compensatory mechanisms engaged to meet variable metabolic demands across brain regions.

Connectivity Analyses of Bioenergetic Changes in Schizophrenia: Identification of Novel Treatments.

We utilized a cell-level approach to examine glycolytic pathways in the DLPFC of subjects with schizophrenia (n = 16) and control (n = 16) and found decreased mRNA expression of glycolytic enzymes in pyramidal neurons, but not astrocytes. To replicate these novel bioenergetic findings, we probed independent datasets for bioenergetic targets and found similar abnormalities. Next, we used a novel strategy to build a schizophrenia bioenergetic profile by a tailored application of the Library of Integrated Network-Based Cellular Signatures data portal (iLINCS) and investigated connected cellular pathways, kinases, and transcription factors using Enrichr. Finally, with the goal of identifying drugs capable of "reversing" the bioenergetic schizophrenia signature, we performed a connectivity analysis with iLINCS and identified peroxisome proliferator-activated receptor (PPAR) agonists as promising therapeutic targets. We administered a PPAR agonist to the GluN1 knockdown model of schizophrenia and found it improved long-term memory. Taken together, our findings suggest that tailored bioinformatics approaches, coupled with the LINCS library of transcriptional signatures of chemical and genetic perturbagens, may be employed to identify novel treatment strategies for schizophrenia and related diseases.

Abnormalities of signal transduction networks in chronic schizophrenia.

Schizophrenia is a serious neuropsychiatric disorder characterized by disruptions of brain cell metabolism, microstructure, and neurotransmission. All of these processes require coordination of multiple kinase-mediated signaling events. We hypothesize that imbalances in kinase activity propagate through an interconnected network of intracellular signaling with potential to simultaneously contribute to many or all of the observed deficits in schizophrenia. We established a workflow distinguishing schizophrenia-altered kinases in anterior cingulate cortex using a previously published kinome array data set. We compared schizophrenia-altered kinases to haloperidol-altered kinases, and identified systems, functions, and regulators predicted using pathway analyses. We used kinase inhibitors with the kinome array to test hypotheses about imbalance in signaling and conducted preliminary studies of kinase proteins, phosphoproteins, and activity for kinases of interest. We investigated schizophrenia-associated single nucleotide polymorphisms in one of these kinases, AKT, for genotype-dependent changes in AKT protein or activity. Kinome analyses identified new kinases as well as some previously implicated in schizophrenia. These results were not explained by chronic antipsychotic treatment. Kinases identified in our analyses aligned with cytoskeletal arrangement and molecular trafficking. Of the kinases we investigated further, AKT and (unexpectedly) JNK, showed the most dysregulation in the anterior cingulate cortex of schizophrenia subjects. Changes in kinase activity did not correspond to protein or phosphoprotein levels. We also show that AKT single nucleotide polymorphism rs1130214, previously associated with schizophrenia, influenced enzyme activity but not protein or phosphoprotein levels. Our data indicate subtle changes in kinase activity and regulation across an interlinked kinase network, suggesting signaling imbalances underlie the core symptoms of schizophrenia. DISEASE MECHANISMS: A SIGNALING IMBALANCE: A study by US scientists indicates that changes in the activity of key signaling proteins may underlie core symptoms of schizophrenia. Protein kinases mediate the activation of intracellular signaling events and analyses of the kinome, the complete set of protein kinases encoded in the genome, previously revealed significant changes in phosphorylation patterns in postmortem brain tissue from patients with schizophrenia. Based on these findings, Jennifer McGuire at the University of Cincinnati and colleagues investigated the upstream regulation of these proteins. They identified both established and novel proteins associated with schizophrenia in the anterior cingulate cortex, with JNK and AKT activity being the most disrupted in schizophrenia patients. Their findings highlight how subtle changes in the activity of a small number of signaling proteins can propagate and have major consequences for mental health.

Traumatic Brain Injury Induces Alterations in Cortical Glutamate Uptake without a Reduction in Glutamate Transporter-1 Protein Expression.

We hypothesize that the primary mechanism for removal of glutamate from the extracellular space is altered after traumatic brain injury (TBI). To evaluate this hypothesis, we initiated TBI in adult male rats using a 2.0 atm lateral fluid percussion injury (LFPI) model. In the ipsilateral cortex and hippocampus, we found no differences in expression of the primary glutamate transporter in the brain (GLT-1) 24 h after TBI. In contrast, we found a decrease in glutamate uptake in the cortex, but not the hippocampus, 24 h after injury. Because glutamate uptake is potently regulated by protein kinases, we assessed global serine-threonine protein kinase activity using a kinome array platform. Twenty-five kinome array peptide substrates were differentially phoshorylated between LFPI and controls in the cortex, whereas 19 peptide substrates were differentially phosphorylated in the hippocampus (fold change ≥ ± 1.15). We identified several kinases as likely to be involved in acute TBI, including protein kinase B (Akt) and protein kinase C (PKC), which are well-characterized modulators of GLT-1. Exploratory studies using an inhibitor of Akt suggest selective activation of kinases in LFPI versus controls. Ingenuity pathway analyses of implicated kinases from our network model found apoptosis and cell death pathways as top functions in acute LFPI. Taken together, our data suggest diminished activity of glutamate transporters in the prefrontal cortex, with no changes in protein expression of the primary glutamate transporter GLT-1, and global alterations in signaling networks that include serine-threonine kinases that are known modulators of glutamate transport activity.

Glutamate Neurotransmission in Rodent Models of Traumatic Brain Injury.

Traumatic brain injury (TBI) is a leading cause of death and disability in people younger than 45 and is a significant public health concern. In addition to primary mechanical damage to cells and tissue, TBI involves additional molecular mechanisms of injury, termed secondary injury, that continue to evolve over hours, days, weeks, and beyond. The trajectory of recovery after TBI is highly unpredictable and in many cases results in chronic cognitive and behavioral changes. Acutely after TBI, there is an unregulated release of glutamate that cannot be buffered or cleared effectively, resulting in damaging levels of glutamate in the extracellular space. This initial loss of glutamate homeostasis may initiate additional changes in glutamate regulation. The excitatory amino acid transporters (EAATs) are expressed on both neurons and glia and are the principal mechanism for maintaining extracellular glutamate levels. Diffusion of glutamate outside the synapse due to impaired uptake may lead to increased extrasynaptic glutamate signaling, secondary injury through activation of cell death pathways, and loss of fidelity and specificity of synaptic transmission. Coordination of glutamate release and uptake is critical to regulating synaptic strength, long-term potentiation and depression, and cognitive processes. In this review, we will discuss dysregulation of extracellular glutamate and glutamate uptake in the acute stage of TBI and how failure to resolve acute disruptions in glutamate homeostatic mechanisms may play a causal role in chronic cognitive symptoms after TBI.