scholarly journals Schizophrenia is defined by cell-specific neuropathology and multiple neurodevelopmental mechanisms in patient-derived cerebral organoids

Author(s):  
Michael Notaras ◽  
Aiman Lodhi ◽  
Friederike Dündar ◽  
Paul Collier ◽  
Nicole M. Sayles ◽  
...  

AbstractDue to an inability to ethically access developing human brain tissue as well as identify prospective cases, early-arising neurodevelopmental and cell-specific signatures of Schizophrenia (Scz) have remained unknown and thus undefined. To overcome these challenges, we utilized patient-derived induced pluripotent stem cells (iPSCs) to generate 3D cerebral organoids to model neuropathology of Scz during this critical period. We discovered that Scz organoids exhibited ventricular neuropathology resulting in altered progenitor survival and disrupted neurogenesis. This ultimately yielded fewer neurons within developing cortical fields of Scz organoids. Single-cell sequencing revealed that Scz progenitors were specifically depleted of neuronal programming factors leading to a remodeling of cell-lineages, altered differentiation trajectories, and distorted cortical cell-type diversity. While Scz organoids were similar in their macromolecular diversity to organoids generated from healthy controls (Ctrls), four GWAS factors (PTN, COMT, PLCL1, and PODXL) and peptide fragments belonging to the POU-domain transcription factor family (e.g., POU3F2/BRN2) were altered. This revealed that Scz organoids principally differed not in their proteomic diversity, but specifically in their total quantity of disease and neurodevelopmental factors at the molecular level. Single-cell sequencing subsequently identified cell-type specific alterations in neuronal programming factors as well as a developmental switch in neurotrophic growth factor expression, indicating that Scz neuropathology can be encoded on a cell-type-by-cell-type basis. Furthermore, single-cell sequencing also specifically replicated the depletion of BRN2 (POU3F2) and PTN in both Scz progenitors and neurons. Subsequently, in two mechanistic rescue experiments we identified that the transcription factor BRN2 and growth factor PTN operate as mechanistic substrates of neurogenesis and cellular survival, respectively, in Scz organoids. Collectively, our work suggests that multiple mechanisms of Scz exist in patient-derived organoids, and that these disparate mechanisms converge upon primordial brain developmental pathways such as neuronal differentiation, survival, and growth factor support, which may amalgamate to elevate intrinsic risk of Scz.

2021 ◽  
Author(s):  
Michael Notaras ◽  
Aiman Lodhi ◽  
Friederike Dundar ◽  
Paul Collier ◽  
Nicole Sayles ◽  
...  

Due to an inability to ethically access developing human brain tissue as well as identify prospective cases, early-arising neurodevelopmental and cell-specific signatures of Schizophrenia (Scz) have remained unknown and thus undefined. To overcome these challenges, we utilized Scz patient-derived stem cells to generate 3D cerebral organoids to model neuropathology of Scz during this critical period. We discovered that Scz organoids exhibited ventricular neuropathology resulting in altered progenitor survival and disrupted neurogenesis. This ultimately yielded fewer neurons within developing cortical fields of Scz organoids. Single-cell sequencing revealed that Scz progenitors were specifically depleted of neuronal programming factors leading to a remodeling of cell-lineages, altered differentiation trajectories, and distorted cortical cell-type diversity. While Scz organoids were 99.95% similar in their macromolecular diversity to Ctrls, four GWAS factors (PTN, COMT, PLCL1, and PODXL) and peptide fragments belonging to the POU-domain transcription factor family (e.g. POU3F2/BRN2) were altered. This revealed that Scz organoids principally differed not in their proteomic diversity, but specifically in their total quantity of disease and neurodevelopmental factors at the molecular level. Single-cell sequencing also subsequently identified cell-type specific alterations in neuronal programming factors and growth factors, and specifically replicated the depletion of POU3F2 (BRN2) and PTN in both Scz progenitors and neurons. Consequently, in two mechanistic rescue experiments we identified that the transcription factor POU3F2 (BRN2) and growth factor PTN operate as mechanistic substrates of neurogenesis and cellular survival, respectively, in Scz organoids. This suggests that multiple mechanisms of Scz exist in patient-derived organoids, and that these disparate mechanisms converge upon primordial brain developmental pathways such as neuronal differentiation, survival, and growth factor support, which may amalgamate to elevate intrinsic risk of Scz.


2021 ◽  
Author(s):  
Zhengyu Ouyang ◽  
Nathanael Bourgeois ◽  
Eugenia Lyashenko ◽  
Paige Cundiff ◽  
Patrick F Cullen ◽  
...  

Induced pluripotent stem cell (iPSC) derived cell types are increasingly employed as in vitro model systems for drug discovery. For these studies to be meaningful, it is important to understand the reproducibility of the iPSC-derived cultures and their similarity to equivalent endogenous cell types. Single-cell and single-nucleus RNA sequencing (RNA-seq) are useful to gain such understanding, but they are expensive and time consuming, while bulk RNA-seq data can be generated quicker and at lower cost. In silico cell type decomposition is an efficient, inexpensive, and convenient alternative that can leverage bulk RNA-seq to derive more fine-grained information about these cultures. We developed CellMap, a computational tool that derives cell type profiles from publicly available single-cell and single-nucleus datasets to infer cell types in bulk RNA-seq data from iPSC-derived cell lines.


2019 ◽  
Author(s):  
Alexandra Grubman ◽  
Gabriel Chew ◽  
John F. Ouyang ◽  
Guizhi Sun ◽  
Xin Yi Choo ◽  
...  

AbstractAlzheimer’s disease (AD) is a heterogeneous disease that is largely dependent on the complex cellular microenvironment in the brain. This complexity impedes our understanding of how individual cell types contribute to disease progression and outcome. To characterize the molecular and functional cell diversity in the human AD brain we utilized single nuclei RNA- seq in AD and control patient brains in order to map the landscape of cellular heterogeneity in AD. We detail gene expression changes at the level of cells and cell subclusters, highlighting specific cellular contributions to global gene expression patterns between control and Alzheimer’s patient brains. We observed distinct cellular regulation of APOE which was repressed in oligodendrocyte progenitor cells (OPCs) and astrocyte AD subclusters, and highly enriched in a microglial AD subcluster. In addition, oligodendrocyte and microglia AD subclusters show discordant expression of APOE. Integration of transcription factor regulatory modules with downstream GWAS gene targets revealed subcluster-specific control of AD cell fate transitions. For example, this analysis uncovered that astrocyte diversity in AD was under the control of transcription factor EB (TFEB), a master regulator of lysosomal function and which initiated a regulatory cascade containing multiple AD GWAS genes. These results establish functional links between specific cellular sub-populations in AD, and provide new insights into the coordinated control of AD GWAS genes and their cell-type specific contribution to disease susceptibility. Finally, we created an interactive reference web resource which will facilitate brain and AD researchers to explore the molecular architecture of subtype and AD-specific cell identity, molecular and functional diversity at the single cell level.HighlightsWe generated the first human single cell transcriptome in AD patient brainsOur study unveiled 9 clusters of cell-type specific and common gene expression patterns between control and AD brains, including clusters of genes that present properties of different cell types (i.e. astrocytes and oligodendrocytes)Our analyses also uncovered functionally specialized sub-cellular clusters: 5 microglial clusters, 8 astrocyte clusters, 6 neuronal clusters, 6 oligodendrocyte clusters, 4 OPC and 2 endothelial clusters, each enriched for specific ontological gene categoriesOur analyses found manifold AD GWAS genes specifically associated with one cell-type, and sets of AD GWAS genes co-ordinately and differentially regulated between different brain cell-types in AD sub-cellular clustersWe mapped the regulatory landscape driving transcriptional changes in AD brain, and identified transcription factor networks which we predict to control cell fate transitions between control and AD sub-cellular clustersFinally, we provide an interactive web-resource that allows the user to further visualise and interrogate our dataset.Data resource web interface:http://adsn.ddnetbio.com


2020 ◽  
Vol 4 (Supplement_1) ◽  
Author(s):  
Leonard Cheung ◽  
Alexandre Daly ◽  
Michelle Brinkmeier ◽  
Sally Ann Camper

Abstract We implemented single-cell RNA sequencing (scRNAseq) technology as a discovery tool to identify factors enriched in differentiated thyrotropes. Thyroid-stimulating hormone (TSH) is produced in the pars distalis of the anterior pituitary (AP) and primarily acts on the thyroid gland to regulate metabolism through T3/T4. However, TSH is also produced by cells in the pars tuberalis (PT), which is comprised of a thin layer of cells that extends rostrally from the pars distalis along the pituitary stalk to the median eminence in the hypothalamus. TSH produced by PT thyrotropes acts on hypothalamic tanycytes to regulate seasonal reproduction. PT thyrotropes likely descend from rostral tip thyrotropes that arise at e12.5 of mouse development, which transcribe the TSH beta subunit (Tshb) without detectable expression of the transcription factor POU1F1. POU1F1 is required for Tshb transcription in thyrotropes of the adenohypophysis, and it acts synergistically with GATA2 to drive cell fate. The molecular mechanisms driving Tshb expression independently of Pou1f1 in PT thyrotropes are unclear. Thyrotropes are the least abundant endocrine cell-type in the pituitary gland. We used genetic labeling and fluorescence-activated cell sorting (FACS) to enrich for thyrotropes for single-cell sequencing. We performed scRNAseq on 7-day-old GFP-positive pituitary cells from Tshb-Cre; R26-LSL-eYFP and intact whole pituitaries, recovering more than 15,000 cells altogether. We observe two distinct populations of cells expressing Tshb. The larger thyrotrope population has approximately twenty fold higher levels of Tshb and five fold higher Cga transcripts than the smaller population, and they are also distinguished by expression of Pou1f1, TSH-releasing hormone receptor (Trhr), and deiodinase 2 (Dio2), consistent with expectations for AP thyrotropes. The smaller thyrotrope population does not express Pou1f1, but those cells are characterized by expression of TSH receptor (Tshr) and melatonin receptor 1A (Mtnr1a), consistent with expectations for PT thyrotropes. They express mildly increased levels of Eya3 and Six1, although these genes are expressed in other cell-types including AP thyrotropes, stem cells, and gonadotropes. They have two-fold higher levels of Gata2 transcripts and uniquely express the transcription factor Sox14. SOX14 is a SoxB2 family transcription factor that counteracts the transcriptional activity of SoxB1 family members, such as Sox2. In conclusion, our scRNAseq has identified novel markers of PT thyrotropes and unveils novel insights into the similarities and differences in the development and function of pituitary thyrotrope subpopulations.


2021 ◽  
Author(s):  
Jessica Neely ◽  
George Hartoularos ◽  
Daniel Bunis ◽  
Yang Sun ◽  
David Lee ◽  
...  

Juvenile dermatomyositis (JDM) is a rare autoimmune condition with insufficient biomarkers and treatments, in part, due to incomplete knowledge of the cell types mediating disease. We investigated immunophenotypes and cell-specific genes associated with disease activity using multiplexed RNA and protein single-cell sequencing applied to PBMCs from 4 treatment-naive JDM (TN-JDM) subjects at baseline, 2, 4, and 6 months and 4 subjects with inactive disease. Analysis of 55,564 cells revealed separate clustering of TN-JDM cells within monocyte, NK, CD8+ effector T and naive B populations. The proportion of CD16+ monocytes was reduced in TN-JDM, and naive B cells were expanded. Cell-type differential gene expression analysis and hierarchical clustering identified a pan-cell-type IFN gene signature over-expressed in TN-JDM in all cell types and correlated with disease activity. TN-JDM monocytes displayed an inflammatory state: CD16+ monocytes expressed the highest IFN gene score and differential protein expression of adhesion molecules, CD49d and CD56, compared to CD14+ inflammatory monocytes. A transitional B cell population expressing higher CD24 and CD5 proteins and an IFN-hi naive B population were associated with TN-JDM and exhibited less CD39, an immunoregulatory protein. This data provides new insights into JDM immune dysregulation at cellular resolution and novel resource for myositis investigators.


2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Alexander Davis ◽  
Ruli Gao ◽  
Nicholas E. Navin

Abstract Background In single cell DNA and RNA sequencing experiments, the number of cells to sequence must be decided before running an experiment, and afterwards, it is necessary to decide whether sufficient cells were sampled. These questions can be addressed by calculating the probability of sampling at least a defined number of cells from each subpopulation (cell type or cancer clone). Results We developed an interactive web application called SCOPIT (Single-Cell One-sided Probability Interactive Tool), which calculates the required probabilities using a multinomial distribution (www.navinlab.com/SCOPIT). In addition, we created an R package called pmultinom for scripting these calculations. Conclusions Our tool for fast multinomial calculations provide a simple and intuitive procedure for prospectively planning single-cell experiments or retrospectively evaluating if sufficient numbers of cells have been sequenced. The web application can be accessed at navinlab.com/SCOPIT.


iScience ◽  
2020 ◽  
pp. 101991
Author(s):  
Kevin Chen ◽  
Kivilcim Ozturk ◽  
Ryne L. Contreras ◽  
Jessica Simon ◽  
Sean McCann ◽  
...  

2019 ◽  
Author(s):  
Hongyi Xin ◽  
Qi Yan ◽  
Yale Jiang ◽  
Qiuyu Lian ◽  
Jiadi Luo ◽  
...  

AbstractIdentifying and removing multiplets from downstream analysis is essential to improve the scalability and reliability of single cell RNA sequencing (scRNA-seq). High multiplet rates create artificial cell types in the dataset. Sample barcoding, including the cell hashing technology and the MULTI-seq technology, enables analytical identification of a fraction of multiplets in a scRNA-seq dataset.We propose a Gaussian-mixture-model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes the sample-barcoding-detectable multiplets and estimates the percentage of sample-barcoding-undetectable multiplets in the remaining dataset. GMM-Demux describes the droplet formation process with an augmented binomial probabilistic model, and uses the model to authenticate cell types discovered from a scRNA-seq dataset.We conducted two cell-hashing experiments, collected a public cell-hashing dataset, and generated a simulated cellhashing dataset. We compared the classification result of GMM-Demux against a state-of-the-art heuristic-based classifier. We show that GMM-Demux is more accurate, more stable, reduces the error rate by up to 69×, and is capable of reliably recognizing 9 multiplet-induced fake cell types and 8 real cell types in a PBMC scRNA-seq dataset.


Nature ◽  
2021 ◽  
Vol 598 (7879) ◽  
pp. 86-102 ◽  
Author(s):  
◽  
Edward M. Callaway ◽  
Hong-Wei Dong ◽  
Joseph R. Ecker ◽  
Michael J. Hawrylycz ◽  
...  

AbstractHere we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input–output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1–5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.


Genes ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 2015
Author(s):  
Harini V. Gudiseva ◽  
Vrathasha Vrathasha ◽  
Jie He ◽  
Devesh Bungatavula ◽  
Joan M. O’Brien ◽  
...  

We intend to identify marker genes with differential gene expression (DEG) and RGC subtypes in cultures of human-induced pluripotent stem cell (iPSC)-derived retinal ganglion cells. Single-cell sequencing was performed on mature and functional iPSC-RGCs at day 40 using Chromium Single Cell 3’ V3 protocols (10X Genomics). Sequencing libraries were run on Illumina Novaseq to generate 150 PE reads. Demultiplexed FASTQ files were mapped to the hg38 reference genome using the STAR package, and cluster analyses were performed using a cell ranger and BBrowser2 software. QC analysis was performed by removing the reads corresponding to ribosomal and mitochondrial genes, as well as cells that had less than 1X mean absolute deviation (MAD), resulting in 4705 cells that were used for further analyses. Cells were separated into clusters based on the gene expression normalization via PCA and TSNE analyses using the Seurat tool and/or Louvain clustering when using BBrowser2 software. DEG analysis identified subsets of RGCs with markers like MAP2, RBPMS, TUJ1, BRN3A, SOX4, TUBB3, SNCG, PAX6 and NRN1 in iPSC-RGCs. Differential expression analysis between separate clusters identified significant DEG transcripts associated with cell cycle, neuron regulatory networks, protein kinases, calcium signaling, growth factor hormones, and homeobox transcription factors. Further cluster refinement identified RGC diversity and subtype specification within iPSC-RGCs. DEGs can be used as biomarkers for RGC subtype classification, which will allow screening model systems that represent a spectrum of diseases with RGC pathology.


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