scholarly journals A spatiotemporally resolved single cell atlas of the Plasmodium liver stage

2021 ◽  
Author(s):  
Amichay Afriat ◽  
Vanessa Zuzarte-Luís ◽  
Keren Bahar Halpern ◽  
Lisa Buchauer ◽  
Sofia Marques ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Molecule ◽  
Parasitophorous Vacuole ◽  
Expression Patterns ◽  
Parasite Development ◽  
Temporal Expression ◽  
Rodent Malaria Parasite ◽  
Liver Stage ◽  
Heterogeneous Behavior

AbstractMalaria infection involves an obligatory, yet clinically silent liver stage1,2. Hepatocytes operate in repeating units termed lobules, exhibiting heterogeneous gene expression patterns along the lobule axis3, but the effects of hepatocyte zonation on parasite development have not been molecularly explored. Here, we combine single-cell RNA sequencing4 and single-molecule transcript imaging5 to characterize the host’s and parasite’s temporal expression programs in a zonally-controlled manner for the rodent malaria parasite Plasmodium berghei ANKA. We identify differences in parasite gene expression in distinct zones, and a sub-population of periportally-biased hepatocytes that harbor abortive infections associated with parasitophorous vacuole breakdown. These ‘abortive hepatocytes’ up-regulate immune recruitment and key signaling programs. They exhibit reduced levels of Plasmodium transcripts, perturbed parasite mRNA localization, and may give rise to progressively lower abundance of periportal infections. Our study provides a resource for understanding the liver stage of Plasmodium infection at high spatial resolution and highlights heterogeneous behavior of both the parasite and the host hepatocyte.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

4 Molecularly guided multiplexed digital spatial analysis reveals differential gene expression profiles in the WNT-β-catenin pathway between melanoma and prostate tumors

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A4-A4
Author(s):  
Anushka Dikshit ◽  
Dan Zollinger ◽  
Karen Nguyen ◽  
Jill McKay-Fleisch ◽  
Kit Fuhrman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Molecule ◽  
Spatial Information ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Fluorescence Assay ◽  
Differentially Expressed ◽  
Tumor Type ◽  
Prostate Tumors

BackgroundThe canonical WNT-β-catenin signaling pathway is vital for development and tissue homeostasis but becomes strongly tumorigenic when dysregulated. and alter the transcriptional signature of a cell to promote malignant transformation. However, thorough characterization of these transcriptomic signatures has been challenging because traditional methods lack either spatial information, multiplexing, or sensitivity/specificity. To overcome these challenges, we developed a novel workflow combining the single molecule and single cell visualization capabilities of the RNAscope in situ hybridization (ISH) assay with the highly multiplexed spatial profiling capabilities of the GeoMx™ Digital Spatial Profiler (DSP) RNA assays. Using these methods, we sought to spatially profile and compare gene expression signatures of tumor niches with high and low CTNNB1 expression.MethodsAfter screening 120 tumor cores from multiple tumors for CTNNB1 expression by the RNAscope assay, we identified melanoma as the tumor type with the highest CTNNB1 expression while prostate tumors had the lowest expression. Using the RNAscope Multiplex Fluorescence assay we selected regions of high CTNNB1 expression within 3 melanoma tumors as well as regions with low CTNNB1 expression within 3 prostate tumors. These selected regions of interest (ROIs) were then transcriptionally profiled using the GeoMx DSP RNA assay for a set of 78 genes relevant in immuno-oncology. Target genes that were differentially expressed were further visualized and spatially assessed using the RNAscope Multiplex Fluorescence assay to confirm GeoMx DSP data with single cell resolution.ResultsThe GeoMx DSP analysis comparing the melanoma and prostate tumors revealed that they had significantly different gene expression profiles and many of these genes showed concordance with CTNNB1 expression. Furthermore, immunoregulatory targets such as ICOSLG, CTLA4, PDCD1 and ARG1, also demonstrated significant correlation with CTNNB1 expression. On validating selected targets using the RNAscope assay, we could distinctly visualize that they were not only highly expressed in melanoma compared to the prostate tumor, but their expression levels changed proportionally to that of CTNNB1 within the same tumors suggesting that these differentially expressed genes may be regulated by the WNT-β-catenin pathway.ConclusionsIn summary, by combining the RNAscope ISH assay and the GeoMx DSP RNA assay into one joint workflow we transcriptionally profiled regions of high and low CTNNB1 expression within melanoma and prostate tumors and identified genes potentially regulated by the WNT- β-catenin pathway. This novel workflow can be fully automated and is well suited for interrogating the tumor and stroma and their interactions.GeoMx Assays are for RESEARCH ONLY, not for diagnostics.

scholarly journals Sequestration of cholesterol within the host late endocytic pathway restricts liver-stage Plasmodium development

2017 ◽  
Vol 28 (6) ◽  
pp. 726-735 ◽  
Author(s):  
Wiebke Petersen ◽  
Werner Stenzel ◽  
Olivier Silvie ◽  
Judith Blanz ◽  
Paul Saftig ◽  
...  
Keyword(s):  
Parasitophorous Vacuole ◽  
Parasite Burden ◽  
Endocytic Pathway ◽  
Host Cells ◽  
Parasite Development ◽  
Liver Stage ◽  
Niemann Pick ◽  
Development Analysis ◽  
Endocytic Compartments

While lysosomes are degradative compartments and one of the defenses against invading pathogens, they are also hubs of metabolic activity. Late endocytic compartments accumulate around Plasmodium berghei liver-stage parasites during development, and whether this is a host defense strategy or active recruitment by the parasites is unknown. In support of the latter hypothesis, we observed that the recruitment of host late endosomes (LEs) and lysosomes is reduced in uis4− parasites, which lack a parasitophorous vacuole membrane protein and arrest during liver-stage development. Analysis of parasite development in host cells deficient for late endosomal or lysosomal proteins revealed that the Niemann–Pick type C (NPC) proteins, which are involved in cholesterol export from LEs, and the lysosome-associated membrane proteins (LAMP) 1 and 2 are important for robust liver-stage P. berghei growth. Using the compound U18666A, which leads to cholesterol sequestration in LEs similar to that seen in NPC- and LAMP-deficient cells, we show that the restriction of parasite growth depends on cholesterol sequestration and that targeting this process can reduce parasite burden in vivo. Taken together, these data reveal that proper LE and lysosome function positively contributes to liver-stage Plasmodium development.

scholarly journals Cryopreservation of microglia enables single-cell RNA sequencing with minimal effects on disease-related gene expression patterns

iScience ◽  
2021 ◽  
Vol 24 (4) ◽  
pp. 102357
Author(s):  
Brenda Morsey ◽  
Meng Niu ◽  
Shetty Ravi Dyavar ◽  
Courtney V. Fletcher ◽  
Benjamin G. Lamberty ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Related Gene ◽  
Single Cell Rna Sequencing ◽  
Disease Related Gene

scholarly journals Joint profiling of gene expression and chromatin accessibility of amphioxus development at single cell resolution

2021 ◽  
Author(s):  
Pengcheng Ma ◽  
Xingyan Liu ◽  
Huimin Liu ◽  
Zaoxu Xu ◽  
Xiangning Ding ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Regulatory Networks ◽  
Functional Divergence ◽  
Expression Patterns ◽  
Genetic Regulatory Networks ◽  
Public Access ◽  
Origin And Evolution ◽  
Key Species

Abstract Vertebrate evolution was accompanied with two rounds of whole genome duplication followed by functional divergence in terms of regulatory circuits and gene expression patterns. As a basal and slow-evolving chordate species, amphioxus is an ideal paradigm for exploring the origin and evolution of vertebrates. Single cell sequencing has been widely employed to construct the developmental cell atlas of several key species of vertebrates (human, mouse, zebrafish and frog) and tunicate (sea squirts). Here, we performed single-nucleus RNA sequencing (snRNA-seq) and single-cell assay for transposase accessible chromatin sequencing (scATAC-seq) for different stages of amphioxus (covering embryogenesis and adult tissues). With the datasets generated we constructed the developmental tree for amphioxus cell fate commitment and lineage specification, and revealed the underlying key regulators and genetic regulatory networks. The generated data were integrated into an online platform, AmphioxusAtlas, for public access at http://120.79.46.200:81/AmphioxusAtlas.

scholarly journals Spatially resolved transcriptomics reveals unique gene signatures associated with human temporal cortical architecture and Alzheimer's pathology

2021 ◽  
Author(s):  
Shuo Chen ◽  
Yuzhou Chang ◽  
Liangping Li ◽  
Diana Acosta ◽  
Cody Morrison ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Molecule ◽  
Brain Region ◽  
Expression Patterns ◽  
Middle Temporal Gyrus ◽  
Marker Genes ◽  
Gene Signatures ◽  
Unique Gene ◽  
Spatially Resolved ◽  
And Gender

Alzheimer's disease (AD) is pathologically characterized by amyloid beta (Aβ) plaques, neurofibrillary tangles (tau aggregates), and alterations in microglia, astrocytes and oligodendrocytes. The mesial temporal lobe is a vulnerable brain region in early AD; however, little is known about the transcriptome-scale gene expression in this region and its relation to AD pathology. Here we use the 10x Genomics Visium platform in combination with co-immunofluorescence staining of AD-associated pathological markers to define the spatial topography of gene expression in the middle temporal gyrus (MTG) from both early AD and age- and gender-matched control cases. We identify unique marker genes for six cortical layers and the adjacent white matter as well as gene expression patterns and alterations that showcase unique gene signatures and pathways associated with a range of AD pathology. Also, gene co-expression analyses of differentially expressed genes (DEGs) between AD and controls reveal four unique gene modules, which significantly change their co-expression patterns in the presence of variations of AD pathology. Furthermore, we validate the changes of key representative DEGs that are associated with AD pathology in neurons, microglia, astrocytes and oligodendrocytes using single-molecule fluorescent in situ hybridization. In summary, we provide a rich resource for the spatial transcriptomic profile of the human MTG, which will contribute to our understanding of the complex architecture and AD pathology of this vulnerable brain region.

scholarly journals Cell-to-cell heterogeneity in Escherichia coli stress response originates from pulsatile expression and growth

2020 ◽  
Author(s):  
Nadia M. V. Sampaio ◽  
Caroline M. Blassick ◽  
Jean-Baptiste Lugagne ◽  
Mary J. Dunlop
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Stress Response ◽  
Single Cell ◽  
Phenotypic Variability ◽  
Growth Dynamics ◽  
Time Lapse ◽  
Cell Heterogeneity ◽  
Temporal Expression ◽  
Functional Consequences

AbstractCell-to-cell heterogeneity in gene expression and growth can have critical functional consequences, such as determining whether individual bacteria survive or die following stress. Although phenotypic variability is well documented, the dynamics that underlie it are often unknown. This information is critical because dramatically different outcomes can arise from gradual versus rapid changes in expression and growth. Using single-cell time-lapse microscopy, we measured the temporal expression of a suite of stress response reporters in Escherichia coli, while simultaneously monitoring growth rate. In conditions without stress, we found widespread examples of pulsatile expression. Single-cell growth rates were often anti-correlated with gene expression, with changes in growth preceding changes in expression. These pulsatile dynamics have functional consequences, which we demonstrate by measuring survival after challenging cells with the antibiotic ciprofloxacin. Our results suggest that pulsatile expression and growth dynamics are common in stress response networks and can have direct consequences for survival.

scholarly journals ZipSeq : Barcoding for Real-time Mapping of Single Cell Transcriptomes

2020 ◽  
Author(s):  
Kenneth H. Hu ◽  
John P. Eichorst ◽  
Chris S. McGinnis ◽  
David M. Patterson ◽  
Eric D. Chow ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Real Time ◽  
Dimensional Space ◽  
Single Cells ◽  
Expression Patterns ◽  
Live Cells ◽  
Glass Substrates ◽  
Complete Mapping

ABSTRACTSpatial transcriptomics seeks to integrate single-cell transcriptomic data within the 3-dimensional space of multicellular biology. Current methods use glass substrates pre-seeded with matrices of barcodes or fluorescence hybridization of a limited number of probes. We developed an alternative approach, called ‘ZipSeq’, that uses patterned illumination and photocaged oligonucleotides to serially print barcodes (Zipcodes) onto live cells within intact tissues, in real-time and with on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in-vitro wound healing, live lymph node sections and in a live tumor microenvironment (TME). In all cases, we discovered new gene expression patterns associated with histological structures. In the TME, this demonstrated a trajectory of myeloid and T cell differentiation, from periphery inward. A variation of ZipSeq efficiently scales to the level of single cells, providing a pathway for complete mapping of live tissues, subsequent to real-time imaging or perturbation.

scholarly journals scGNN: a novel graph neural network framework for single-cell RNA-Seq analyses

2020 ◽  
Author(s):  
Juexin Wang ◽  
Anjun Ma ◽  
Yuzhou Chang ◽  
Jianting Gong ◽  
Yuexu Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Single Cell ◽  
Neural Development ◽  
Expression Patterns ◽  
Gaussian Model ◽  
Postmortem Brain ◽  
Differential Mechanism ◽  
Disease Study ◽  
Cell Cell

ABSTRACTSingle-cell RNA-sequencing (scRNA-Seq) is widely used to reveal the heterogeneity and dynamics of tissues, organisms, and complex diseases, but its analyses still suffer from multiple grand challenges, including the sequencing sparsity and complex differential patterns in gene expression. We introduce the scGNN (single-cell graph neural network) to provide a hypothesis-free deep learning framework for scRNA-Seq analyses. This framework formulates and aggregates cell-cell relationships with graph neural networks and models heterogeneous gene expression patterns using a left-truncated mixture Gaussian model. scGNN integrates three iterative multi-modal autoencoders and outperforms existing tools for gene imputation and cell clustering on four benchmark scRNA-Seq datasets. In an Alzheimer’s disease study with 13,214 single nuclei from postmortem brain tissues, scGNN successfully illustrated disease-related neural development and the differential mechanism. scGNN provides an effective representation of gene expression and cell-cell relationships. It is also a novel and powerful framework that can be applied to scRNA-Seq analyses.

scholarly journals 1450-1545 Young Investigator Awards & Presentations Basic/Translational Exploring cellular subpopulations in glioblastoma and matched organoids using single-cell RNA-seq 52

2018 ◽  
Vol 45 (S3) ◽  
pp. S13-S14
Author(s):  
VG LeBlanc ◽  
D Trinh ◽  
M Hughes ◽  
I Luthra ◽  
D Livingstone ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Tissue Sample ◽  
Expression Patterns ◽  
Model Systems ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Original Tumour ◽  
Current Therapies ◽  
Intratumoural Heterogeneity

Glioblastomas (GBMs) account for nearly half of all primary malignant brain tumours, and current therapies are often only marginally effective. Our understanding of the underlying biology of these tumours and the development of new therapies have been complicated in part by widespread inter- and intratumoural heterogeneity. To characterize this heterogeneity, we performed regional subsampling of primary glioblastomas and derived organoids from these tissue samples. We then performed single-cell RNA-sequencing (scRNA-seq) on these primary regional subsamples and 1-3 matched organoids per sample. We have profiled samples from six tumour sets to date and have obtained sequencing data for 21,234 primary tissue cells and 14,742 organoid cells. While the most apparent differences in gene expression appear to be between individual tumours, we were also able to identify similar cellular subpopulations across tissue samples and across organoids. Importantly, organoids derived from the same tissue sample appeared to be composed of similar cellular subpopulations and were highly comparable to each other, indicating that replicate organoids faithfully represent the original tumour tissue. Overall, our scRNA-seq approach will help evaluate the utility of tumour-derived organoids as model systems for GBM and will aid in identifying cellular subpopulations defined by gene expression patterns, both in primary GBM regional subsamples and their associated organoids. These analyses will allow for the characterization of clonal or subclonal populations that are likely to respond to different therapeutic approaches and may also uncover novel therapeutic targets previously unrevealed through bulk analyses.

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