scholarly journals A network regularized linear model to infer spatial expression pattern for single cells

2021 ◽  
Author(s):  
Chaohao Gu ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spatial Information ◽  
Expression Profiles ◽  
Single Cells ◽  
Expression Patterns ◽  
Capture Rate ◽  
Marker Genes ◽  
Spatial Expression ◽  
Cellular Expression

Abstract Spatial gene-expression is a crucial determinant of cell fate and behavior. Recent imaging and sequencing-technology advancements have enabled scientists to develop new tools that use spatial information to measure gene-expression at close to single-cell levels. Yet, while Fluorescence In-situ Hybridization (FISH) can quantify transcript numbers at single-cell resolution, it is limited to a small number of genes. Similarly, slide-seq was designed to measure spatial-expression profiles at the single-cell level but has a relatively low gene-capture rate. And although single-cell RNA-seq enables deep cellular gene-expression profiling, it loses spatial information during sample-collection. These major limitations have stymied these methods’ broader application in the field. To overcome spatio-omics technology’s limitations and better understand spatial patterns at single-cell resolution, we designed a computation algorithm that uses glmSMA to predict cell locations by integrating scRNA-seq data with a spatial-omics reference atlas. We treated cell-mapping as a convex optimization problem by minimizing the differences between cellular-expression profiles and location-expression profiles with an L1 regularization and graph Laplacian based L2 regularization to ensure a sparse and smooth mapping. We validated the mapping results by reconstructing spatial- expression patterns of well-known marker genes in complex tissues, like the mouse cerebellum and hippocampus. We used the biological literature to verify that the reconstructed patterns can recapitulate cell-type and anatomy structures. Our work thus far shows that, together, we can use glmSMA to accurately assign single cells to their original reference-atlas locations.

scholarly journals A network regularized linear model to infer spatial expression pattern for single cells

2021 ◽  
Author(s):  
Chaohao Gu ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spatial Information ◽  
Expression Profiles ◽  
Single Cells ◽  
Expression Patterns ◽  
Capture Rate ◽  
Marker Genes ◽  
Spatial Expression ◽  
Cellular Expression

AbstractSpatial gene-expression is a crucial determinant of cell fate and behavior. Recent imaging and sequencing-technology advancements have enabled scientists to develop new tools that use spatial information to measure gene-expression at close to single-cell levels. Yet, while Fluorescence In-situ Hybridization (FISH) can quantify transcript numbers at single-cell resolution, it is limited to a small number of genes. Similarly, slide-seq was designed to measure spatial-expression profiles at the single-cell level but has a relatively low gene-capture rate. And although single-cell RNA-seq enables deep cellular gene-expression profiling, it loses spatial information during sample-collection. These major limitations have stymied these methods’ broader application in the field. To overcome spatio-omics technology’s limitations and better understand spatial patterns at single-cell resolution, we designed a computation algorithm that uses glmSMA to predict cell locations by integrating scRNA-seq data with a spatialomics reference atlas. We treated cell-mapping as a convex optimization problem by minimizing the differences between cellular-expression profiles and location-expression profiles with a L1 regularization and graph Laplacian based L2 regularization to ensure a sparse and smooth mapping. We validated the mapping results by reconstructing spatial-expression patterns of well-known marker genes in complex tissues, like the mouse cerebellum and hippocampus. We used the biological literature to verify that the reconstructed patterns can recapitulate cell-type and anatomy structures. Our work thus far shows that, together, we can use glmSMA to accurately assign single cells to their original reference-atlas locations.

scholarly journals Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries

2018 ◽  
Author(s):  
Kent A. Riemondy ◽  
Monica Ransom ◽  
Christopher Alderman ◽  
Austin E. Gillen ◽  
Rui Fu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Sequencing ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Single Cells ◽  
Expression Patterns ◽  
Complex Mixture ◽  
Gene Expression Information ◽  
Single Cell Rna Sequencing

ABSTRACTSingle-cell RNA sequencing (scRNA-seq) methods generate sparse gene expression profiles for thousands of single cells in a single experiment. The information in these profiles is sufficient to classify cell types by distinct expression patterns but the high complexity of scRNA-seq libraries often prevents full characterization of transcriptomes from individual cells. To extract more focused gene expression information from scRNA-seq libraries, we developed a strategy to physically recover the DNA molecules comprising transcriptome subsets, enabling deeper interrogation of the isolated molecules by another round of DNA sequencing. We applied the method in cell-centric and gene-centric modes to isolate cDNA fragments from scRNA-seq libraries. First, we resampled the transcriptomes of rare, single megakaryocytes from a complex mixture of lymphocytes and analyzed them in a second round of DNA sequencing, yielding up to 20-fold greater sequencing depth per cell and increasing the number of genes detected per cell from a median of 1,313 to 2,002. We similarly isolated mRNAs from targeted T cells to improve the reconstruction of their VDJ-rearranged immune receptor mRNAs. Second, we isolatedCD3DmRNA fragments expressed across cells in a scRNA-seq library prepared from a clonal T cell line, increasing the number of cells with detectedCD3Dexpression from 59.7% to 100%. Transcriptome resampling is a general approach to recover targeted gene expression information from single-cell RNA sequencing libraries that enhances the utility of these costly experiments, and may be applicable to the targeted recovery of molecules from other single-cell assays.

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 P02.10 FocuSCOPE: a single cell, multi-omics solution to simultaneously analyze tumor variants and microenvironment

2021 ◽  
Vol 9 (Suppl 1) ◽  
pp. A12.1-A12
Author(s):  
Y Arjmand Abbassi ◽  
N Fang ◽  
W Zhu ◽  
Y Zhou ◽  
Y Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
High Throughput ◽  
Immune Cells ◽  
Genetic Variants ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Single Cell Sequencing

Recent advances of high-throughput single cell sequencing technologies have greatly improved our understanding of the complex biological systems. Heterogeneous samples such as tumor tissues commonly harbor cancer cell-specific genetic variants and gene expression profiles, both of which have been shown to be related to the mechanisms of disease development, progression, and responses to treatment. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in tumor responses to systematic therapy such as immunotherapy or cell therapy. However, most current high-throughput single cell sequencing methods detect only gene expression levels or epigenetics events such as chromatin conformation. The information on important genetic variants including mutation or fusion is not captured. To better understand the mechanisms of tumor responses to systematic therapy, it is essential to decipher the connection between genotype and gene expression patterns of both tumor cells and cells in the tumor microenvironment. We developed FocuSCOPE, a high-throughput multi-omics sequencing solution that can detect both genetic variants and transcriptome from same single cells. FocuSCOPE has been used to successfully perform single cell analysis of both gene expression profiles and point mutations, fusion genes, or intracellular viral sequences from thousands of cells simultaneously, delivering comprehensive insights of tumor and immune cells in tumor microenvironment at single cell resolution.Disclosure InformationY. Arjmand Abbassi: None. N. Fang: None. W. Zhu: None. Y. Zhou: None. Y. Chen: None. U. Deutsch: None.

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 PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing

2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Imputation Method ◽  
Supplementary Information ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

Abstract Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.

Inhibition of Elastase or SDF-1/CXCR4 Axis Promotes Differentiation of Human AML Cells.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 1891-1891
Author(s):  
Sigal Tavor ◽  
Jasmine Jacob-Hirsch ◽  
Manny Eisenbach ◽  
Sigi Kay ◽  
Shoshana Baron ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Line ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Critical Role ◽  
Marker Genes ◽  
Granulocytic Differentiation ◽  
Elastase Inhibitor ◽  
Granule Proteins ◽  
Cxcr4 Axis

Abstract Elastase, along with other azurophil granule proteins like proteinase 3 regulates normal and leukemic granulopoiesis in an un-defined mechanism. We have recently showed that human acute myeloid leukemic (AML) cells constitutively express and secrete stromal derived factor 1 (SDF-1) dependent cell surface elastase, which regulates their migration and proliferation. To elucidate the molecular events and genes regulated by elastase and SDF-1/CXCR4 axis in AML cells, we examined gene expression of U937 AML cell line treated with neutralizing anti-CXCR4 Abs or elastase inhibitor (EI) compared to untreated cells, using DNA microarray technology. Unsupervised hierarchical clustering analysis showed very similar gene expression profiles of EI and anti CXCR4 Abs treated cells as compared to control. 230 of 8400 genes interrogated were repressed, and 164 were induced after culturing AML cells in the presence of EI or anti CXCR4 Abs at different time points as compared to untreated cells. Inhibition of elastase or CXCR4 was accompanied by down regulation of the transcripts of primary granule proteins. Functional classification of elastase or SDF-1/CXCR4 axis regulated genes revealed downregulation of HOXA9, HOXA10, ETS2, as well as other transcription factors that are over expressed in AML and are important for the development of leukemia. Whereas, transcriptional factors and regulators known to be induced during myeloid differentiation like C/EBPε, ID1, RUNX3 and HHEX were up-regulated in treated cells. Expression patterns of apoptosis genes indicated decline in death control by the p53 dependent pathway and a more prominent control by mitochondrial mediated apoptotic pathway like bcl2 related genes. In addition, receptors for interleukins, growth factors (G-CSFR and GM-CSF), complement component (C1QR1) were upregulated in the treated cells. In contrast, FLT-3, a growth factor receptor stimulating growth of early progenitor cells and AML blasts, was down regulated in AML cell treated with EI or anti CXCR4 Abs. These data were confirmed by real time PCR for selected marker genes of granulocytic differentiation. Interestingly, many of the differentially expressed genes were common to the transcriptional program of normal terminal granulocytic differentiation (Theilgaard-Monch & Borregarrd 2005. Blood 105:1785) suggesting that inhibition of elastase may induce differentiation in AML cells. Thus we further analyzed the effect of elastase inhibition on AML cell differentiation and growth. Treatment of HL60 AML cell line with EI triggered a proliferative arrest, apoptosis and mimicked terminal granulocytic differentiation, including morphologic changes, increased CD11b expression, and the ability to produce oxidative bursts. In summary, our study showed that inhibition of elastase or SDF-1/CXCR4 axis in AML cells affects similar pathways related to differentiation and malignant transformation, implying a critical role for those molecules in regulating leukemic development. Repression of elastase decreases proliferation and induces differentiation of AML cells, suggesting a potential new therapeutic approach for AML.

Circulating melanoma cells isolated from clinical blood samples and characterized by full-length mRNA sequencing at single-cell level.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 10539-10539 ◽  
Author(s):  
Yu-Chieh Wang ◽  
Daniel Ramskold ◽  
Shujun Luo ◽  
Robin Li ◽  
Qiaolin Deng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Melanoma Cells ◽  
Clinical Samples ◽  
Single Cell Level ◽  
Cell Level ◽  
Blood Samples

10539 Background: Melanoma is the most aggressive type of skin cancer. Late-stage melanoma is highly metastatic and currently lacks effective treatment. This discouraging clinical observation highlights the need for a better understanding of the molecular mechanisms underlying melanoma initiation and progression and for developing new therapeutic approaches based on novel targets. Although genome-wide transcriptome analyses have been frequently used to study molecular alterations in clinical samples, it has been technically challenging to obtain the transcriptomic profiles at single-cell level. Methods: Using antibody-mediated magnetic activated cell separation (MACS), we isolated and individualized putative circulating melanoma cells (CMCs) from the blood samples of the melanoma patients at advance stages. The transcriptomic analysis based on a novel and robust mRNA-Seq protocol (Smart-Seq) was established and applied to the putative CMCs for single-cell profiling. Results: We have discovered distinct gene expression patterns, including new putative markers for CMCs. Meanwhile, the gene expression profiles derived of the CMC candidates isolated from the patient’s blood samples are closely-related to the expression profiles of other cells originated from human melanocytes, including normal melanocytes in primary culture and melanoma cell lines. Compared with existing methods, Smart-Seq has improved read coverage across transcripts, which provides advantage for better analyzing transcript isoforms and SNPs. Conclusions: Our results suggest that the techniques developed in this research for cell isolation and transcriptomic analyses can potentially be used for addressing many biological and clinical questions requiring genomewide transcriptome profiling in rare cells.

scholarly journals Self-reporting transposons enable simultaneous readout of gene expression and transcription factor binding in single cells

2019 ◽  
Author(s):  
Arnav Moudgil ◽  
Michael N. Wilkinson ◽  
Xuhua Chen ◽  
June He ◽  
Alex J. Cammack ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Single Cell ◽  
Binding Sites ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Specific Cell

AbstractIn situ measurements of transcription factor (TF) binding are confounded by cellular heterogeneity and represent averaged profiles in complex tissues. Single cell RNA-seq (scRNA-seq) is capable of resolving different cell types based on gene expression profiles, but no technology exists to directly link specific cell types to the binding pattern of TFs in those cell types. Here, we present self-reporting transposons (SRTs) and their use in single cell calling cards (scCC), a novel assay for simultaneously capturing gene expression profiles and mapping TF binding sites in single cells. First, we show how the genomic locations of SRTs can be recovered from mRNA. Next, we demonstrate that SRTs deposited by the piggyBac transposase can be used to map the genome-wide localization of the TFs SP1, through a direct fusion of the two proteins, and BRD4, through its native affinity for piggyBac. We then present the scCC method, which maps SRTs from scRNA-seq libraries, thus enabling concomitant identification of cell types and TF binding sites in those same cells. As a proof-of-concept, we show recovery of cell type-specific BRD4 and SP1 binding sites from cultured cells. Finally, we map Brd4 binding sites in the mouse cortex at single cell resolution, thus establishing a new technique for studying TF biology in situ.

scholarly journals Revisit the Cellular Transmission and Emerging Techniques in Understanding the Mechanisms of Proteinopathies

2021 ◽  
Vol 15 ◽  
Author(s):  
Jinwen Jiang ◽  
Yu Liu ◽  
Qihui Wu
Keyword(s):  
Gene Expression ◽  
Nervous System ◽  
Single Cell ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Molecular Taxonomy ◽  
Gene Expression Profiles ◽  
Underlying Mechanism ◽  
Signal Pathways ◽  
Parkinson’S Diseases

Alzheimer’s and Parkinson’s diseases (AD and PD) are amongst top of the prevalent neurodegenerative disease. One-third of PD patients are diagnosed with dementia, a pre-symptom of AD, but the underlying mechanism is elusive. Amyloid beta (Aβ) and α-synuclein are two of the most investigated proteins, whose pathological aggregation and spreading are crucial to the pathogenesis of AD and PD, respectively. Transcriptomic studies of the mammalian central nervous system shed light on gene expression profiles at molecular levels, regarding the complexity of neuronal morphologies and electrophysiological inputs/outputs. In the last decade, the booming of the single-cell RNA sequencing technique helped to understand gene expression patterns, alternative splicing, novel transcripts, and signal pathways in the nervous system at single-cell levels, providing insight for molecular taxonomy and mechanistic targets of the degenerative nervous system. Here, we re-visited the cell-cell transmission mechanisms of Aβ and α-synuclein in mediating disease propagation, and summarized recent single-cell transcriptome sequencing from different perspectives and discussed its understanding of neurodegenerative diseases.

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