Biology subjects

Hjerling-Leffler, J.

Publications and source records attributed to Hjerling-Leffler, J..

17 recordsLinked to original sources

bioRxiv2025

Cell Type Specific Inference of Perturbations in Synaptic Communication with MultiNeuronChat

Inference of differential neuron-neuron and neuron-glia communication from single nucleus RNA sequencing data is a powerful technique to uncover altered communication pathways when comparing groups, such as disease and control, developmental stages, or age groups, or different treatments. Yet, the communication changes are typically not identified on the cell type pair level, limiting its resolution in terms of answering biological and medical questions. Here we present MultiNeuronChat, a highly resolved framework that utilizes gene expression measurements of single cells from case-control single-cell/nucleus RNA-seq data sets together with an existing comprehensive database comprising cell adhesion molecules, gap junctions and synaptic transmission for the differential analysis of neuron-neuron and neuronglia communication. In an in silico study, we show our method accurately and efficiently predicts known cell type-specific perturbations without summarizing communication scores across donor samples. Using a published single-nucleus RNA sequencing dataset, we highlight the sensitivity of our method by showcasing that MultiNeuronChat identifies both known and novel communication pathways in several cell type pairs in Alzheimers disease. Lastly, we highlight that MultiNeuronChats donor-specific communication score calculation can be utilized to inform patient stratification.

bioinformatics↗

bioRxiv2024

Identifying patterns differing between high-dimensional datasets with generalized contrastive PCA

High-dimensional data have become ubiquitous in the biological sciences, and it is often desirable to compare two datasets collected under different experimental conditions to extract low-dimensional patterns enriched in one condition. However, traditional dimensionality reduction techniques cannot accomplish this because they operate on only one dataset. Contrastive principal component analysis (cPCA) has been proposed to address this problem, but it has seen little adoption because it requires tuning a hyperparameter resulting in multiple solutions, with no way of knowing which is correct. Moreover, cPCA uses foreground and background conditions that are treated differently, making it ill-suited to compare two experimental conditions symmetrically. Here we describe the development of generalized contrastive PCA (gcPCA), a flexible hyperparameter-free approach that solves these problems. We first provide analyses explaining why cPCA requires a hyperparameter and how gcPCA avoids this requirement. We then describe an open-source gcPCA toolbox containing Python and MATLAB implementations of several variants of gcPCA tailored for different scenarios. Finally, we demonstrate the utility of gcPCA in analyzing diverse high-dimensional biological data, revealing unsupervised detection of hippocampal replay in neurophysiological recordings and heterogeneity of type II diabetes in single-cell RNA sequencing data. As a fast, robust, and easy-to-use comparison method, gcPCA provides a valuable resource facilitating the analysis of diverse high-dimensional datasets to gain new insights into complex biological phenomena.

neuroscience↗

bioRxiv2023

Single-cell mRNA-regulation analysis reveals cell type-specific mechanisms of type 2 diabetes

Perturbed secretion of insulin and other pancreatic islet hormones is the main cause of type 2 diabetes (T2D). The islets harbor five cell types that are potentially altered differently by T2D. Whole-islet transcriptomics and single-cell RNA-sequencing (scRNAseq) studies have revealed differentially expressed genes without reaching consensus. Here, we demonstrate that unprecedented insights into disease mechanisms can be obtained by network-based analysis of scRNAseq data. We developed differential gene coordination network analysis (dGCNA) and analyzed islet scRNAseq data from 16 T2D and 16 non-T2D individuals. dGCNA revealed T2D-induced cell type-specific networks of dysregulated genes with remarkable ontological specificity, thus allowing for a comprehensive and unbiased functional classification of genes involved in T2D. In beta cells eleven networks of genes were detected, revealing that mitochondrial electron transport chain, glycolysis, cytoskeleton organization, cell proliferation, unfolded protein response and three networks of beta cell transcription factors are perturbed, whereas exocytosis, lysosomal regulation and insulin translation programs are instead enhanced in T2D. Furthermore, we validated the ability of dGCNA to reveal disease mechanisms and predict the functional context of genes by showing that TMEM176A/B regulates the beta cell cytoskeleton and that CEPBG is a key regulator of the unfolded protein response. In addition, comparing beta- and alpha and cells, we found substantial differences, reproduced across independent datasets, confirming cell type-specific alterations in T2D. We conclude that analysis of networks of differentially coordinated genes provides outstanding insight into cell type-specific gene function and T2D pathophysiology.

cell biology↗

bioRxiv2022

Transcriptional diversity in synaptic gene sets is sufficient to discriminate cortical neuronal identity

Synapse diversity has been described from different perspectives, ranging from the specific neurotransmitters released, to their diverse biophysical properties and proteome profiles. However, synapse diversity at the transcriptional level has not been systematically identified across all synapse populations in the brain. To quantify postsynaptic and identify specific synaptic features of neuronal cell types we combined the SynGO (Synaptic Gene Ontology) database with single-cell RNA sequencing data of the mouse neocortex. We show that cell types can be discriminated by synaptic genes alone with the same power as all genes. The cell type discriminatory power is not equally distributed across synaptic genes as we could identify functional categories and synaptic compartments with greater cell type specific expression. Synaptic genes, and specific SynGO categories, belonged to three different types of gene modules: gradient expression over all cell types, gradient expression in selected cell types and cell class- or type-specific profiles. This data provides a deeper understanding of synapse diversity in the neocortex and identifies potential markers to selectively identify synapses from specific neuronal populations.

neuroscience↗

bioRxiv2022

Single cell transcriptomics reveals correct developmental dynamics and high-quality midbrain cell types by improved hESC differentiation.

Stem cell technologies provide new opportunities for modeling cells in the healthy and diseased states and for regenerative medicine. In both cases developmental knowledge as well as the quality and molecular properties of the cells are essential for their future application. In this study we identify developmental factors important for the differentiation of human embryonic stem cells (hESCs) into midbrain dopaminergic (mDA) neurons. We found that Laminin-511, and dual canonical and non-canonical WNT activation followed by GSK3{beta} inhibition plus FGF8b, improved midbrain patterning. In addition, mDA neurogenesis and differentiation was enhanced by activation of liver X receptors and inhibition of fibroblast growth factor signaling. Moreover, single-cell RNA-sequencing analysis revealed a developmental dynamics similar to that of the endogenous human ventral midbrain and the emergence of high quality molecularly-defined midbrain cell types, including mDA neurons that become functional. Thus, our study identifies novel factors important for human midbrain development and opens the door for a future application of molecularly-defined hESC-derived midbrain cell types in Parkinsons disease.

neuroscience↗

bioRxiv2020

Heterogeneous somatostatin-expressing neuron population in mouse ventral tegmental area

The cellular architecture of the ventral tegmental area (VTA), the main hub of the brain reward system, remains only partially characterized. To extend the characterization to inhibitory neurons, we have identified three distinct subtypes of somatostatin (Sst)-expressing neurons in the mouse VTA. These neurons differ in their electrophysiological and morphological properties, anatomical localization, as well as mRNA expression profiles. Importantly, similar to cortical Sst-containing interneurons, most VTA Sst neurons express GABAergic inhibitory markers, but some of them also express glutamatergic excitatory markers and a subpopulation even express dopaminergic markers. Furthermore, only some of the proposed marker genes for cortical Sst neurons were expressed in the VTA Sst neurons. Physiologically, one of the VTA Sst neuron subtypes locally inhibited neighboring dopamine neurons. Overall, our results demonstrate the remarkable complexity and heterogeneity of VTA Sst neurons and suggest that these cells are multifunctional players in the midbrain reward circuitry.

neuroscience↗

bioRxiv2019

Activin receptor ALK4 coordinates extracellular signals and intrinsic transcriptional programs to regulate development of cortical somatostatin interneurons

Although the role of transcription factors in fate specification of cortical interneurons is well established, how these interact with extracellular signals to regulate interneuron development is poorly understood. Here we show that the activin receptor ALK4 is a key regulator of the specification of somatostatin interneurons. Mice lacking ALK4 in GABAergic neurons of the medial ganglionic eminence (MGE) showed marked deficits in distinct subpopulations of somatostatin interneurons from early postnatal stages of cortical development. Specific loses were observed among distinct subtypes of somatostatin+/Reelin+ double-positive cells, including Hpse+ layer IV cells targeting parvalbumin+ interneurons, leading to quantitative alterations in the inhibitory circuitry of this layer. Activin-mediated ALK4 signaling in MGE cells induced interaction of Smad2 with SATB1, a transcription factor critical for somatostatin interneuron development, and promoted SATB1 nuclear translocation and repositioning within the somatostatin gene promoter. These results indicate that intrinsic transcriptional programs interact with extracellular signals present in the environment of MGE cells to regulate cortical interneuron specification.

neuroscience↗

bioRxiv2019

Conditional GWAS analysis identifies putative disorder-specific SNPs for psychiatric disorders

Substantial genetic liability is shared across psychiatric disorders but less is known about risk variants that are specific to a given disorder. We used multi-trait conditional and joint analysis (mtCOJO) to adjust GWAS summary statistics of one disorder for the effects of genetically correlated traits to identify putative disorder-specific SNP associations. We applied mtCOJO to summary statistics for five psychiatric disorders from the Psychiatric Genomics Consortium - schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit hyperactivity disorder (ADHD) and autism (AUT). Most genom-wide significant variants for these disorders had evidence of pleiotropy (i.e., impact on multiple psychiatric disorders) and hence have reduced mtCOJO conditional effect sizes. However, subsets of genome-wide significant variants had larger conditional effect sizes consistent with disorder-specific effects: 15 of 130 genome-wide significant variants for schizophrenia, 5 of 40 for major depression, 3 of 11 for ADHD and 1 of 2 for autism. In addition, we identified a number of variants that approached genome-wide significance in the original GWAS and have larger conditional effect sizes after conditioning on the other disorders. We show that decreased expression of VPS29 in the brain may increase risk to SCZ only and increased expression of CSE1L is associated with SCZ and MD, but not with BIP. Likewise, decreased expression of PCDHA7 in the brain is linked to increased risk of MD but decreased risk of SCZ and BIP.

genetics↗

bioRxiv2019

Genetic Identification of Cell Types Underlying Brain Complex Traits Yields Novel Insights Into the Etiology of Parkinson's Disease

Genome-wide association studies (GWAS) have discovered hundreds of loci associated with complex brain disorders, and provide the best current insights into the etiology of these idiopathic traits. However, it remains unclear in which cell types these variants may be active, which is essential for understanding disease etiology and for disease modelling. Here we integrate GWAS results with single-cell transcriptomic data from the entire nervous system to systematically identify cell types underlying psychiatric disorders, neurological conditions, and other brain complex traits. We show that psychiatric disorders are predominantly associated with excitatory neurons from the cortex/hippocampus, medium spiny neurons from the striatum, diverse sets of midbrain neurons, and inhibitory neurons from the cortex/hippocampus. Cognitive traits were generally associated with similar cell types but their associations were driven by different genes. Neurological disorders were associated with different cell types, consistent with other lines of evidence. Notably, we found that Parkinsons disease is not only genetically associated with dopaminergic neurons but also with serotonergic neurons and cells from the oligodendrocyte lineage. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Altogether, our study provides a solid framework for understanding the cellular basis of complex brain disorders and reveals a new unexpected role of oligodendrocytes in Parkinsons disease.

genomics↗

bioRxiv2018

A spatial atlas of inhibitory cell types in mouse hippocampus

Understanding the function of a tissue requires knowing the spatial organization of its constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patterns that define its many, closely related cell types, but cannot reveal their spatial arrangement. Here we introduce probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages prior scRNA-seq classification to identify cell types using multiplexed in situ RNA detection. We applied this method to map the inhibitory neurons of hippocampal area CA1, a cell system critical for memory function, for which ground truth is available from extensive prior work identifying the laminar organization of subtly differing cell types. Our method confidently identified 16 interneuron classes, in a spatial arrangement closely matching ground truth. This method will allow identifying the spatial organization of fine cell types across the brain and other tissues.

neuroscience↗

bioRxiv2018

Prioritizing risk genes for neurodevelopmental disorders using pathway information

Trio family and case-control studies of next-generation sequencing data have proven integral to understanding the contribution of rare inherited and de novo single-nucleotide variants to the genetic architecture of complex disease. Ideally, such studies should identify individual risk genes of moderate to large effect size to generate novel treatment hypotheses for further follow-up. However, due to insufficient power, gene set enrichment analyses have come to be relied upon for detecting differences between cases and controls, implicating sets of hundreds of genes rather than specific targets for further investigation. Here, we present a Bayesian statistical framework, termed gTADA, that integrates gene-set membership information with gene-level de novo and rare inherited case-control counts, to prioritize risk genes with excess rare variant burden within enriched gene sets. Applying gTADA to available whole-exome sequencing datasets for several neuropsychiatric conditions, we replicated previously reported gene set enrichments and identified novel risk genes. For epilepsy, gTADA prioritized 40 risk genes (posterior probabilities > 0.95), 6 of which replicate in an independent whole-genome sequencing study. In addition, 30/40 genes are novel genes. We found that epilepsy genes had high protein-protein interaction (PPI) network connectivity, and show specific expression during human brain development. Some of the top prioritized EPI genes were connected to a PPI subnetwork of immune genes and show specific expression in prenatal microglia. We also identified multiple enriched drug-target gene sets for EPI which included immunostimulants as well as known antiepileptics. Immune biology was supported specifically by case-control variants from familial epilepsies rather than do novo mutations in generalized encephalitic epilepsy.

genomics↗

bioRxiv2018

Genetic meta-analysis identifies 10 novel loci and functional pathways for Alzheimer’s disease risk

Late onset Alzheimers disease (AD) is the most common form of dementia with more than 35 million people affected worldwide, and no curative treatment available. AD is highly heritable and recent genome-wide meta-analyses have identified over 20 genomic loci associated with AD, yet only explaining a small proportion of the genetic variance indicating that undiscovered loci exist. Here, we performed the largest genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 AD cases, 383,378 controls). AD-by-proxy status is based on parental AD diagnosis, and showed strong genetic correlation with AD (rg=0.81). Genetic meta analysis identified 29 risk loci, of which 9 are novel, and implicating 215 potential causative genes. Independent replication further supports these novel loci in AD. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver and microglia). Furthermore, gene-set analyses indicate the genetic contribution of biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomisation results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying more of the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD to guide new drug development.

genetics↗

bioRxiv2018

Genome-wide Analysis of Insomnia (N=1,331,010) Identifies Novel Loci and Functional Pathways

Insomnia is the second-most prevalent mental disorder, with no sufficient treatment available. Despite a substantial role of genetic factors, only a handful of genes have been implicated and insight into the associated neurobiological pathways remains limited. Here, we use an unprecedented large genetic association sample (N=1,331,010) to allow detection of a substantial number of genetic variants and gain insight into biological functions, cell types and tissues involved in insomnia complaints. We identify 202 genome-wide significant loci implicating 956 genes through positional, eQTL and chromatin interaction mapping. We show involvement of the axonal part of neurons, of specific cortical and subcortical tissues, and of two specific cell-types in insomnia: striatal medium spiny neurons and hypothalamic neurons. These cell-types have been implicated previously in the regulation of reward processing, sleep and arousal in animal studies, but have never been genetically linked to insomnia in humans. We found weak genetic correlations with other sleep-related traits, but strong genetic correlations with psychiatric and metabolic traits. Mendelian randomization identified causal effects of insomnia on specific psychiatric and metabolic traits. Our findings reveal key brain areas and cells implicated in the neurobiology of insomnia and its related disorders, and provide novel targets for treatment.

genetics↗

bioRxiv2017

Single-cell transcriptomic profiling of progenitors of the oligodendrocyte lineage reveals transcriptional convergence during development

Pdgfra+ oligodendrocyte precursor cells (OPCs) arise in distinct specification waves during embryogenesis in the central nervous system (CNS). It is unclear whether there is a correlation between these waves and different transcriptional oligodendrocyte (OL) states at adult stages. Here we present a bulk and single-cell transcriptomics resource providing insights on how transitions between these states occur. We show that E13.5 Pdgfra+ populations are not OPCs, exhibiting instead hallmarks of neural progenitors. A subset of these progenitors, which we refer as pre-OPCs, rewires their transcriptional landscape, converging into indistinguishable OPC states at E17.5 and post-natal stages. P7 brain and spinal cord OPCs present similar transcriptional profiles at the single-cell level, indicating that OPC states are not region-specific. Postnatal OPC progeny of E13.5 Pdgfra+ have electrophysiological and transcriptional profiles similar to OPCs derived from subsequent specification waves. In addition, lineage tracing indicates that a subset of E13.5 Pdgfra+ cells also originate cells of the pericyte lineage. In summary, our results indicate that embryonic Pdgfra+ cells are diverse and give rise at post-natal stages to distinct cell lineages, including OPCs with convergent transcriptional profiles in different CNS regions.

neuroscience↗

bioRxiv2017

GWAS Meta-Analysis of Neuroticism (N=449,484) Identifies Novel Genetic Loci and Pathways

Neuroticism is an important risk factor for psychiatric traits including depression1, anxiety2,3, and schizophrenia4-6. Previous genome-wide association studies7-12 (GWAS) reported 16 genomic loci10-12. Here we report the largest neuroticism GWAS meta-analysis to date (N=449,484), and identify 136 independent genome-wide significant loci (124 novel), implicating 599 genes. Extensive functional follow-up analyses show enrichment in several brain regions and involvement of specific cell-types, including dopaminergic neuroblasts (P=3x10-8), medium spiny neurons (P=4x10-8) and serotonergic neurons (P=1x10-7). Gene-set analyses implicate three specific pathways: neurogenesis (P=4.4x10-9), behavioural response to cocaine processes (P=1.84x10-7), and axon part (P=5.26x10-8). We show that neuroticisms genetic signal partly originates in two genetically distinguishable subclusters13 (depressed affect and worry, the former being genetically strongly related to depression, rg=0.84), suggesting distinct causal mechanisms for subtypes of individuals. These results vastly enhance our neurobiological understanding of neuroticism, and provide specific leads for functional follow-up experiments.

genetics↗

bioRxiv2017

Genetic Identification Of Brain Cell Types Underlying Schizophrenia

With few exceptions, the marked advances in knowledge about the genetic basis for schizophrenia have not converged on findings that can be confidently used for precise experimental modeling. Applying knowledge of the cellular taxonomy of the brain from single-cell RNA-sequencing, we evaluated whether the genomic loci implicated in schizophrenia map onto specific brain cell types. The common variant genomic results consistently mapped to pyramidal cells, medium spiny neurons, and certain interneurons but far less consistently to embryonic, progenitor, or glial cells. These enrichments were due to distinct sets of genes specifically expressed in each of these cell types. Many of the diverse gene sets associated with schizophrenia (including antipsychotic targets) implicate the same brain cell types. Our results provide a parsimonious explanation: the common-variant genetic results for schizophrenia point at a limited set of neurons, and the gene sets point to the same cells. While some of the genetic risk is associated with GABAergic interneurons, this risk largely does not overlap with that from projecting cells.

genomics↗

bioRxiv2017

Single-Cell Transcriptomic Analysis Of CA1 Inhibitory Neurons

Understanding any brain circuit will require a categorization of its constituent neurons. In hippocampal area CA1, at least 23 classes of GABAergic neuron have been proposed to date. However, this list may be incomplete; additionally, it is unclear whether discrete classes are sufficient to describe the diversity of cortical inhibitory neurons, or whether continuous modes of variability are also required. We studied the transcriptomes of 3663 CA1 inhibitory cells, revealing 10 major GABAergic groups that divided into 49 fine-scale clusters. All previously described and several novel cell classes were identified, with three previously-described classes unexpectedly found to be identical. A division into discrete classes however was not sufficient to describe the diversity of these cells, as continuous variation also occurred between and within classes. Latent factor analysis revealed that a single continuous variable could predict the expression levels of several genes, which correlated similarly with it across multiple cell types. Analysis of the genes correlating with this variable suggested it reflects a range from metabolically highly active faster-spiking cells that proximally target pyramidal cells, to slower-spiking cells targeting distal dendrites or interneurons. These results elucidate the complexity of inhibitory neurons in one of the simplest cortical structures, and show that characterizing these cells requires continuous modes of variation as well as discrete cell classes.

neuroscience↗