Cell Type-specific Expression Profiles Research Articles

Mitochondrial complex I deficiency stratifies idiopathic Parkinson’s disease

Idiopathic Parkinson’s disease (iPD) is believed to have a heterogeneous pathophysiology, but molecular disease subtypes have not been identified. Here, we show that iPD can be stratified according to the severity of neuronal respiratory complex I (CI) deficiency, and identify two emerging disease subtypes with distinct molecular and clinical profiles. The CI deficient (CI-PD) subtype accounts for approximately a fourth of all cases, and is characterized by anatomically widespread neuronal CI deficiency, a distinct cell type-specific gene expression profile, increased load of neuronal mtDNA deletions, and a predilection for non-tremor dominant motor phenotypes. In contrast, the non-CI deficient (nCI-PD) subtype exhibits no evidence of mitochondrial impairment outside the dopaminergic substantia nigra and has a predilection for a tremor dominant phenotype. These findings constitute a step towards resolving the biological heterogeneity of iPD with implications for both mechanistic understanding and treatment strategies.

Abstract 102: Microanatomy of Intracranial Human Thrombi by Single Nuclei Transcriptomics

Introduction: Microvascular dysfunction and inflammation are thought to underlie reperfusion failure following endovascular thrombectomy. Here we present the first study to produce a transcriptome-based cellular landscape of human intracranial thrombi highlighting the importance of cellular plasticity and intercellular communication at the site of occlusion. Methods: We performed single nuclei RNA-sequencing (snRNA-seq) on intracranial human thrombi harvested during revascularization. Unsupervised clustering, data visualization, differential expression and functional gene ontology/pathway analysis were utilized to deconvolve expression data into cell type-specific expression profiles. Thrombi snRNA-seq data were integrated with single cell RNA-seq data from symptomatic and asymptomatic human carotid plaques and healthy brain vasculature to contextualize thrombi in atherosclerotic disease, perform diffusion coefficient analysis, and identify ligand-receptor interactions. Results: Unbiased clustering identified six cellular populations in distinct effector states within the thrombus microenvironment. Functional gene ontology and pathway analysis revealed a monocyte subpopulation in a distinct endothelial-activation and neuroplasticity state, supporting the notion of cellular trans-differentiation and interconversion. Integration with carotid plaque data and diffusion coefficient analysis revealed shared transcriptional programs existing in a continuum, progressing from asymptomatic plaque to embolized thrombus. Ligand-receptor analysis identified macrophage mediated T-cell activation, pro-inflammatory response, immunological synapse stabilization, neuroplasticity, and monocyte trans-endothelial migration. Conclusion: Our novel application of snRNA-seq to thrombectomy specimens provides the highest resolution exploration of cellular differences and a best understanding of individual contextualized cell function to date. This detailed cellular landscape will facilitate mapping novel interventional targets with direct functional relevance and allow for greater personalization of post-reperfusion therapy.

Transcriptional Targeting of Dendritic Cells Using an Optimized Human Fascin1 Gene Promoter

Deeper knowledge about the role of the tumor microenvironment (TME) in cancer development and progression has resulted in new strategies such as gene-based cancer immunotherapy. Whereas some approaches focus on the expression of tumoricidal genes within the TME, DNA-based vaccines are intended to be expressed in antigen-presenting cells (e.g., dendritic cells, DCs) in secondary lymphoid organs, which in turn induce anti-tumor T cell responses. Besides effective delivery systems and the requirement of appropriate adjuvants, DNA vaccines themselves need to be optimized regarding efficacy and selectivity. In this work, the concept of DC-focused transcriptional targeting was tested by applying a plasmid encoding for the luciferase reporter gene under the control of a derivative of the human fascin1 gene promoter (pFscnLuc), comprising the proximal core promoter fused to the normally more distantly located DC enhancer region. DC-focused activity of this reporter construct was confirmed in cell culture in comparison to a standard reporter vector encoding for luciferase under the control of the strong ubiquitously active cytomegalovirus promoter and enhancer (pCMVLuc). Both plasmids were also compared upon intravenous administration in mice. The organ- and cell type-specific expression profile of pFscnLuc versus pCMVLuc demonstrated favorable activity especially in the spleen as a central immune organ and within the spleen in DCs.

Projecting RNA measurements onto single cell atlases to extract cell type-specific expression profiles using scProjection

Multi-modal single cell RNA assays capture RNA content as well as other data modalities, such as spatial cell position or the electrophysiological properties of cells. Compared to dedicated scRNA-seq assays however, they may unintentionally capture RNA from multiple adjacent cells, exhibit lower RNA sequencing depth compared to scRNA-seq, or lack genome-wide RNA measurements. We present scProjection, a method for mapping individual multi-modal RNA measurements to deeply sequenced scRNA-seq atlases to extract cell type-specific, single cell gene expression profiles. We demonstrate several use cases of scProjection, including identifying spatial motifs from spatial transcriptome assays, distinguishing RNA contributions from neighboring cells in both spatial and multi-modal single cell assays, and imputing expression measurements of un-measured genes from gene markers. scProjection therefore combines the advantages of both multi-modal and scRNA-seq assays to yield precise multi-modal measurements of single cells.

The protein kinases of Dictyostelia and their incorporation into a signalome

Protein kinases are major regulators of cellular processes, but the roles of most kinases remain unresolved. Dictyostelid social amoebas have been useful in identifying functions for 30% of its kinases in cell migration, cytokinesis, vesicle trafficking, gene regulation and other processes but their upstream regulators and downstream effectors are mostly unknown. Comparative genomics can assist to distinguish between genes involved in deeply conserved core processes and those involved in species-specific innovations, while co-expression of genes as evident from comparative transcriptomics can provide cues to the protein complement of regulatory networks. Genomes and developmental and cell-type specific transcriptomes are available for species that span the 0.5 billion years of evolution of Dictyostelia from their unicellular ancestors. In this work we analysed conservation and change in the abundance, functional domain architecture and developmental regulation of protein kinases across the 4 major taxon groups of Dictyostelia. All data are summarized in annotated phylogenetic trees of the kinase subtypes and accompanied by functional information of all kinases that were experimentally studied. We detected 393 different protein kinase domains across the five studied genomes, of which 212 were fully conserved. Conservation was highest (71%) in the previously defined AGC, CAMK, CK1, CMCG, STE and TKL groups and lowest (26%) in the “other” group of typical protein kinases. This was mostly due to species-specific single gene amplification of “other” kinases. Apart from the AFK and α-kinases, the atypical protein kinases, such as the PIKK and histidine kinases were also almost fully conserved. The phylogeny-wide developmental and cell-type specific expression profiles of the protein kinase genes were combined with profiles from the same transcriptomic experiments for the families of G-protein coupled receptors, small GTPases and their GEFs and GAPs, the transcription factors and for all genes that upon lesion generate a developmental defect. This dataset was subjected to hierarchical clustering to identify clusters of co-expressed genes that potentially act together in a signalling network. The work provides a valuable resource that allows researchers to identify protein kinases and other regulatory proteins that are likely to act as intermediates in a network of interest.

TEDD: a database of temporal gene expression patterns during multiple developmental periods in human and model organisms.

Characterization of the specific expression and chromatin profiles of genes enables understanding how they contribute to tissue/organ development and the mechanisms leading to diseases. Whilst the number of single-cell sequencing studies is increasing dramatically; however, data mining and reanalysis remains challenging. Herein, we systematically curated the up-to-date and most comprehensive datasets of sequencing data originating from 2760 bulk samples and over 5.1 million single-cells from multiple developmental periods from humans and multiple model organisms. With unified and systematic analysis, we profiled the gene expression and chromatin accessibility among 481 cell-types, 79 tissue-types and 92 timepoints, and pinpointed cells with the co-expression of target genes. We also enabled the detection of gene(s) with a temporal and cell-type specific expression profile that is similar to or distinct from that of a target gene. Additionally, we illustrated the potential upstream and downstream gene-gene regulation interactions, particularly under the same biological process(es) or KEGG pathway(s). Thus, TEDD (Temporal Expression during Development Database), a value-added database with a user-friendly interface, not only enables researchers to identify cell-type/tissue-type specific and temporal gene expression and chromatin profiles but also facilitates the association of genes with undefined biological functions in development and diseases. The database URL is https://TEDD.obg.cuhk.edu.hk/.

Skin-Expressing lncRNAs in Inflammatory Responses.

Long non-coding RNAs (lncRNAs) have attracted attention for their potential roles in modulating keratinocyte differentiation and inflammatory response; however, for many identified skin-expressing lncRNAs, there is no comprehensive characterization regarding their biological roles. In addition, the reported expression profiles for lncRNAs can be ambiguous due to their low-expressing nature. The objective of this review is to utilize large scale genomic data to characterize the prominent skin-expressing lncRNAs, aiming to provide additional insights for their potential roles in the pathology of inflammatory skin of psoriasis and atopic dermatitis by integrating in vitro and in vivo data. We highlighted the different skin-expressing lncRNAs, including H19, which is significantly down-regulated in lesional skin of AD/psoriasis and upon cytokine stimulation in keratinocytes; it is also negatively correlated with CYP1A1 (r = -0.75, p = 8 × 10−73), a gene involved in drug metabolism and skin barrier homeostasis, in keratinocytes. In addition, SPRR2C, a potential regulator that modulates IL-22 stimulation, was upregulated in both atopic dermatitis and psoriasis lesional skin and was also downstream of the IL-17A and IL-17 + TNF signaling in keratinocytes. Using scRNAseq, we further revealed the cell type specificity of lncRNAs, including basal-expressing nature of H19 in the epidermis. Interestingly, instead of having cell type specific expression profile, we found few lncRNAs that are express across different cell types in skin, including MALAT1, NEAT1, and GAS5. While lncRNAs in general have lower expression, our results combining in vitro and in vivo experimental data demonstrate how some of these lncRNAs can play mediator roles in the cytokine-stimulated pathway.

Sensei: how many samples to tell a change in cell type abundance?

Cellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html.

Cell type-specific gene expression changes in Alzheimer's patients with varying levels of cerebral amyloid angiopathy.

Tau neurofibrillary tangles and senile plaques comprised of insoluble amyloid beta are the major histopathological hallmarks of Alzheimer's disease (AD). More than 85% of autopsy-confirmed AD cases also exhibit some degree of cerebral amyloid angiopathy (CAA), which is characterized by amyloid beta peptide deposits predominantly in blood vessels in the meningeal and intracerebral blood vessels. Consequently, CAA predisposes individuals with AD to cerebral infarction and hemorrhages, accounting for ∼20% of cases in the elderly, which could lead to faster cognitive decline through neurovascular pathway dysfunction. We sought to identify cellular composition changes and characterize cell type-specific expression associated with CAA pathology in AD patients. Using 10x Genomics single nuclei RNA sequencing, we profiled nuclei from frozen post-mortem temporal cortex tissue of 80 individuals with neuropathologic diagnosis of AD and varying levels of CAA, ranging from zero to severe CAA pathology. Braak and Thal measures were also obtained. Downstream analyses were performed in R using Seurat v3.2.3. Quality control filtering based on percentage of genes mapped to the mitochondrial genome and number of genes and UMIs detected resulted in 135,092 nuclei for analysis. Differential gene expression analyses were performed using R package MAST. We identified 30 clusters which were annotated using established cell type marker genes. There were 3 oligodendrocyte, 14 neuronal (2 excitatory and 3 inhibitory), 5 microglial, 3 endothelial, 4 astrocyte and 1 oligodendrocyte precursor cell cluster. We observed a significant increase in microglial cell proportions with increasing CAA severity, for 3 of the microglial clusters. Conversely, the excitatory neuronal proportion were decreased in individuals with more severe CAA. Further investigation is ongoing to identify the genes underlying the dysregulation of these cell types in CAA. We observed cellular proportion changes for multiple cell types with increasing CAA severity. Additional analyses to determine the effects of CAA independent of AD severity will be performed by adjusting for Braak and Thal measures. Deeper characterization of cell type-specific expression profiles is expected to provide novel insights into cell-type specific transcriptome changes associated with vascular risk factors in AD.

From Cellular Infiltration Assessment to a Functional Gene Set-Based Prognostic Model for Breast Cancer.

BackgroundCancer heterogeneity is a major challenge in clinical practice, and to some extent, the varying combinations of different cell types and their cross-talk with tumor cells that modulate the tumor microenvironment (TME) are thought to be responsible. Despite recent methodological advances in cancer, a reliable and robust model that could effectively investigate heterogeneity with direct prognostic/diagnostic clinical application remained elusive.ResultsTo investigate cancer heterogeneity, we took advantage of single-cell transcriptome data and constructed the first indication- and cell type-specific reference gene expression profile (RGEP) for breast cancer (BC) that can accurately predict the cellular infiltration. By utilizing the BC-specific RGEP combined with a proven deconvolution model (LinDeconSeq), we were able to determine the intrinsic gene expression of 15 cell types in BC tissues. Besides identifying significant differences in cellular proportions between molecular subtypes, we also evaluated the varying degree of immune cell infiltration (basal-like subtype: highest; Her2 subtype: lowest) across all available TCGA-BRCA cohorts. By converting the cellular proportions into functional gene sets, we further developed a 24 functional gene set-based prognostic model that can effectively discriminate the overall survival (P = 5.9 × 10−33, n = 1091, TCGA-BRCA cohort) and therapeutic response (chemotherapy and immunotherapy) (P = 6.5 × 10−3, n = 348, IMvigor210 cohort) in the tumor patients.ConclusionsHerein, we have developed a highly reliable BC-RGEP that adequately annotates different cell types and estimates the cellular infiltration. Of importance, the functional gene set-based prognostic model that we have introduced here showed a great ability to screen patients based on their therapeutic response. On a broader perspective, we provide a perspective to generate similar models in other cancer types to identify shared factors that drives cancer heterogeneity.

Microrna Regulation of Nodule Zone-Specific Gene Expression In Soybean

Microrna Regulation of Nodule Zone-Specific Gene Expression In Soybean

A computational method for direct imputation of cell type-specific expression profiles and cellular compositions from bulk-tissue RNA-Seq in brain disorders.

The importance of cell type-specific gene expression in disease-relevant tissues is increasingly recognized in genetic studies of complex diseases. However, most gene expression studies are conducted on bulk tissues, without examining cell type-specific expression profiles. Several computational methods are available for cell type deconvolution (i.e. inference of cellular composition) from bulk RNA-Seq data, but few of them impute cell type-specific expression profiles. We hypothesize that with external prior information such as single cell RNA-seq and population-wide expression profiles, it can be computationally tractable to estimate both cellular composition and cell type-specific expression from bulk RNA-Seq data. Here we introduce CellR, which addresses cross-individual gene expression variations to adjust the weights of cell-specific gene markers. It then transforms the deconvolution problem into a linear programming model while taking into account inter/intra cellular correlations and uses a multi-variate stochastic search algorithm to estimate the cell type-specific expression profiles. Analyses on several complex diseases such as schizophrenia, Alzheimer’s disease, Huntington’s disease and type 2 diabetes validated the efficiency of CellR, while revealing how specific cell types contribute to different diseases. In summary, CellR compares favorably against competing approaches, enabling cell type-specific re-analysis of gene expression data on bulk tissues in complex diseases.

Computational deconvolution to estimate cell type-specific gene expression from bulk data.

Computational deconvolution is a time and cost-efficient approach to obtain cell type-specific information from bulk gene expression of heterogeneous tissues like blood. Deconvolution can aim to either estimate cell type proportions or abundances in samples, or estimate how strongly each present cell type expresses different genes, or both tasks simultaneously. Among the two separate goals, the estimation of cell type proportions/abundances is widely studied, but less attention has been paid on defining the cell type-specific expression profiles. Here, we address this gap by introducing a novel method Rodeo and empirically evaluating it and the other available tools from multiple perspectives utilizing diverse datasets.

Identification of bona fide B2 SINE retrotransposon transcription through single-nucleus RNA-seq of the mouse hippocampus.

Currently, researchers rely on generalized methods to quantify transposable element (TE) RNA expression, such as RT-qPCR and RNA-seq, that do not distinguish between TEs expressed from their own promoter (bona fide) and TEs that are transcribed from a neighboring gene promoter such as within an intron or exon. This distinction is important owing to the differing functional roles of TEs depending on whether they are independently transcribed. Here we report a simple strategy to examine bona fide TE expression, termed BonaFide-TEseq. This approach can be used with any template-switch based library such as Smart-seq2 or the single-cell 5′ gene expression kit from 10x, extending its utility to single-cell RNA-sequencing. This approach does not require TE-specific enrichment, enabling the simultaneous examination of TEs and protein-coding genes. We show that TEs identified through BonaFide-TEseq are expressed from their own promoter, rather than captured as internal products of genes. We reveal the utility of BonaFide-TEseq in the analysis of single-cell data and show that short-interspersed nuclear elements (SINEs) show cell type–specific expression profiles in the mouse hippocampus. We further show that, in response to a brief exposure of home-cage mice to a novel stimulus, SINEs are activated in dentate granule neurons in a time course that is similar to that of protein-coding immediate early genes. This work provides a simple alternative approach to assess bona fide TE transcription at single-cell resolution and provides a proof-of-concept using this method to identify SINE activation in a context that is relevant for normal learning and memory.

Cell type specific gene expression profiling reveals a role for complement component C3 in neutrophil responses to tissue damage

Tissue damage induces rapid recruitment of leukocytes and changes in the transcriptional landscape that influence wound healing. However, the cell-type specific transcriptional changes that influence leukocyte function and tissue repair have not been well characterized. Here, we employed translating ribosome affinity purification (TRAP) and RNA sequencing, TRAP-seq, in larval zebrafish to identify genes differentially expressed in neutrophils, macrophages, and epithelial cells in response to wounding. We identified the complement pathway and c3a.1, homologous to the C3 component of human complement, as significantly increased in neutrophils in response to wounds. c3a.1−/− zebrafish larvae have impaired neutrophil directed migration to tail wounds with an initial lag in recruitment early after wounding. Moreover, c3a.1−/− zebrafish larvae have impaired recruitment to localized bacterial infections and reduced survival that is, at least in part, neutrophil mediated. Together, our findings support the power of TRAP-seq to identify cell type specific changes in gene expression that influence neutrophil behavior in response to tissue damage.

Genome-wide Association Analysis of Parkinson’s Disease and Schizophrenia Reveals Shared Genetic Architecture and Identifies Novel Risk Loci

BackgroundParkinson’s disease (PD) and schizophrenia (SCZ) are heritable brain disorders that involve dysregulation of the dopaminergic system. Epidemiological studies have reported potential comorbidity between the disorders, and movement disturbances are common in patients with SCZ before treatment with antipsychotic drugs. Despite this, little is known about shared genetic etiology between the disorders. MethodsWe analyzed recent large genome-wide association studies on patients with SCZ (N = 77,096) and PD (N = 417,508) using a conditional/conjunctional false discovery rate (FDR) approach to evaluate overlap in common genetic variants and improve statistical power for genetic discovery. Using a variety of biological resources, we functionally characterized the identified genomic loci. ResultsWe observed genetic enrichment in PD conditional on associations with SCZ and vice versa, indicating polygenic overlap. We then leveraged this cross-trait enrichment using conditional FDR analysis and identified 9 novel PD risk loci and 1 novel SCZ locus at conditional FDR < .01. Furthermore, we identified 9 genomic loci jointly associated with PD and SCZ at conjunctional FDR < .05. There was an even distribution of antagonistic and agonistic effect directions among the shared loci, in line with the insignificant genetic correlation between the disorders. Of 67 genes mapped to the shared loci, 65 are expressed in the human brain and show cell type–specific expression profiles. ConclusionsAltogether, the study increases understanding of the genetic architectures underlying SCZ and PD, indicating that common molecular genetic mechanisms may contribute to overlapping pathophysiological and clinical features between the disorders.

Genome-wide association study of cerebral small vessel disease reveals established and novel loci.

Intracerebral haemorrhage and small vessel ischaemic stroke (SVS) are the most acute manifestations of cerebral small vessel disease, with no established preventive approaches beyond hypertension management. Combined genome-wide association study (GWAS) of these two correlated diseases may improve statistical power to detect novel genetic factors for cerebral small vessel disease, elucidating underlying disease mechanisms that may form the basis for future treatments. Because intracerebral haemorrhage location is an adequate surrogate for distinct histopathological variants of cerebral small vessel disease (lobar for cerebral amyloid angiopathy and non-lobar for arteriolosclerosis), we performed GWAS of intracerebral haemorrhage by location in 1813 subjects (755 lobar and 1005 non-lobar) and 1711 stroke-free control subjects. Intracerebral haemorrhage GWAS results by location were meta-analysed with GWAS results for SVS from MEGASTROKE, using 'Multi-Trait Analysis of GWAS' (MTAG) to integrate summary data across traits and generate combined effect estimates. After combining intracerebral haemorrhage and SVS datasets, our sample size included 241 024 participants (6255 intracerebral haemorrhage or SVS cases and 233 058 control subjects). Genome-wide significant associations were observed for non-lobar intracerebral haemorrhage enhanced by SVS with rs2758605 [MTAG P-value (P) = 2.6 × 10-8] at 1q22; rs72932727 (P = 1.7 × 10-8) at 2q33; and rs9515201 (P = 5.3 × 10-10) at 13q34. In the GTEx gene expression library, rs2758605 (1q22), rs72932727 (2q33) and rs9515201 (13q34) are significant cis-eQTLs for PMF1 (P = 1 × 10-4 in tibial nerve), NBEAL1, FAM117B and CARF (P < 2.1 × 10-7 in arteries) and COL4A2 and COL4A1 (P < 0.01 in brain putamen), respectively. Leveraging S-PrediXcan for gene-based association testing with the predicted expression models in tissues related with nerve, artery, and non-lobar brain, we found that experiment-wide significant (P < 8.5 × 10-7) associations at three genes at 2q33 including NBEAL1, FAM117B and WDR12 and genome-wide significant associations at two genes including ICA1L at 2q33 and ZCCHC14 at 16q24. Brain cell-type specific expression profiling libraries reveal that SEMA4A, SLC25A44 and PMF1 at 1q22 and COL4A1 and COL4A2 at 13q34 were mainly expressed in endothelial cells, while the genes at 2q33 (FAM117B, CARF and NBEAL1) were expressed in various cell types including astrocytes, oligodendrocytes and neurons. Our cross-phenotype genetic study of intracerebral haemorrhage and SVS demonstrates novel genome-wide associations for non-lobar intracerebral haemorrhage at 2q33 and 13q34. Our replication of the 1q22 locus previous seen in traditional GWAS of intracerebral haemorrhage, as well as the rediscovery of 13q34, which had previously been reported in candidate gene studies with other cerebral small vessel disease-related traits strengthens the credibility of applying this novel genome-wide approach across intracerebral haemorrhage and SVS.

Gradual differentiation and confinement of the cardiac conduction system as indicated by marker gene expression

The components of the cardiac conduction system, responsible for coordinated activation of the heart chambers, are well defined and their cells differ in gene expression profile and phenotype from those of the surrounding working myocardium. Yet, when and on what basis the myocardium of each of the conduction system components become distinguishable from other myocardium during heart development has not been well established. To identify and assess cell type-specific expression profiles and differentiation markers, we performed transcriptome analysis on fluorescence activated cell sorted purified conduction system (Venus+) and chamber myocardial cells (Katushka+) of Tbx3+/Venus;TgNppb(Katushka) double transgenic mouse fetuses. We found that transcripts associated with nervous system development and ion channel activity were enriched in Tbx3+ conduction system cells, whereas transcripts associated with mitochondrial function, muscle contraction and fatty acid metabolism were enriched in the Nppb+ working myocardium. We analyzed spatio-temporal expression patterns of several candidate markers (Cacna2d2, Cacna1g, Ephb3, Tnni1), reviewed those of established conduction system markers (Tbx3, Hcn4, Gja5, Cntn2), and placed the patterns in the context of conduction system development. The overview indicates that different properties of conduction system components develop gradually and at different developmental stages, and that chamber myocardium gradually differentiates and diverges from conduction system myocardium until after birth.

Abstract 151: Comprehensive characterization of gastric cancer at single-cell resolution

Abstract The gastric carcinoma cells have complex interactions with diverse cell types in the associated tumor microenvironment. The tumor microenvironment plays important roles on gastric cancer development, progression and therapy response. In this study, we leveraged high-throughput single-cell RNA sequencing (scRNAseq) to characterize the diverse cell population in gastric tumors. In total, we analyzed more than 30,000 single cells from eight gastric tumors and matched normal tissues. Cells were sequenced at an average depth of 70000 reads/cell to guarantee sufficient coverage for downstream analysis. Using graph-based cell clustering methods and known cell markers, we identified heterogenous epithelial cells (PGC+, MUC5AC+, TFF1+, EPCAM+, CDH1+), fibroblasts (ACTA2+, THY1+, COLA1+), endothelial cells (VWF+, PECAM1+), and a variety of immune cells including M1 and M2 macrophages (IL1A+, IL1B+, TNF+, MARCO+, MSR1+, CD68+), CD4 T cells (CD3D+, CD4+), CD8 T cells (CD8A+), Gamma Delta T cells (TRGC2+, TRDC+), B cells (CD79+), and plasma cells (CD19+, CD20+, IgG+). Specifically, gastric epithelial cancer cells demonstrated distinct CNVs inferred from single cell transcriptome files. We established and characterized patient derived gastric cancer organoids from single tumor cells. The organoids recapitulate CNVs found in the original gastric tumors and displayed varied degree of drug responses. We identified specific molecular features of the tumor microenvironment that modulate cell growth and immune responses. For example, we identified heterogenous myofibroblast populations with distinct chemokine secretory profiles compared to normal gastric tissue. We characterized gene signatures associated with immune checkpoints on T cells and targetable molecules in innate cells. Using cell type specific expression profiles, we were able to establish a comprehensive signaling network of tumor-tumor, tumor-stromal, stromal-stromal and stromal-tumor signaling via receptor-ligand pairing. Citation Format: Jiamin Chen, Anuja Sathe, Sue Grimes, Stephanie Greer, Billy Lau, Ann Renschler, George Poultsides, Carlos Suarez, Hanlee Ji. Comprehensive characterization of gastric cancer at single-cell resolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 151.

Cell type-specific expression profiling unravels the development and evolution of stinging cells in sea anemone

BackgroundCnidocytes are specialized cells that define the phylum Cnidaria. They possess an “explosive” organelle called cnidocyst that is important for prey capture and anti-predator defense. An extraordinary morphological and functional complexity of the cnidocysts has inspired numerous studies to investigate their structure and development. However, the transcriptomes of the cells bearing these unique organelles are yet to be characterized, impeding our understanding of the genetic basis of their biogenesis.ResultsIn this study, we generated a nematocyte reporter transgenic line of the sea anemone Nematostella vectensis using the CRISPR/Cas9 system. By using a fluorescence-activated cell sorter (FACS), we have characterized cell type-specific transcriptomic profiles of various stages of cnidocyte maturation and showed that nematogenesis (the formation of functional cnidocysts) is underpinned by dramatic shifts in the spatiotemporal gene expression. Among the genes identified as upregulated in cnidocytes were Cnido-Jun and Cnido-Fos1—cnidarian-specific paralogs of the highly conserved c-Jun and c-Fos proteins of the stress-induced AP-1 transcriptional complex. The knockdown of the cnidocyte-specific c-Jun homolog by microinjection of morpholino antisense oligomer results in disruption of normal nematogenesis.ConclusionsHere, we show that the majority of upregulated genes and enriched biochemical pathways specific to cnidocytes are uncharacterized, emphasizing the need for further functional research on nematogenesis. The recruitment of the metazoan stress-related transcription factor c-Fos/c-Jun complex into nematogenesis highlights the evolutionary ingenuity and novelty associated with the formation of these highly complex, enigmatic, and phyletically unique organelles. Thus, we provide novel insights into the biology, development, and evolution of cnidocytes.