scholarly journals Single-Cell Gene Expression Profiling and Cell State Dynamics: Collecting Data, Correlating Data Points and Connecting the Dots

2016 ◽  
Author(s):  
Carsten Marr ◽  
Joseph X. Zhou ◽  
Sui Huang
Keyword(s):  
Single Cell ◽  
Computational Analysis ◽  
Expression Profiles ◽  
Cell Populations ◽  
Monitoring Methods ◽  
Time Resolved ◽  
Cell State ◽  
Data Points ◽  
Low Dimensional ◽  
Protein Expression Profiles

AbstractSingle-cell analyses of transcript and protein expression profiles – more precisely, single-cell resolution analysis of molecular profiles of cell populations – have now entered the center stage with widespread applications of single-cell qPCR, single-cell RNA-Seq and CyTOF. These high-dimensional population snapshot techniques are complemented by low-dimensional time-resolved, microscopy-based monitoring methods. Both fronts of advance have exposed a rich heterogeneity of cell states within uniform cell populations in many biological contexts, producing a new kind of data that has stimulated a series of computational analysis methods for data visualization, dimensionality reduction, and cluster (subpopulation) identification. The next step is now to go beyond collecting data and correlating data points: to connect the dots, that is, to understand what actually underlies the identified data patterns. This entails interpreting the “clouds of points” in state space as a manifestation of the underlying molecular regulatory network. In that way control of cell state dynamics can be formalized as a quasi-potential landscape, as first proposed by Waddington. We summarize key methods of data acquisition and computational analysis and explain the principles that link the single-cell resolution measurements to dynamical systems theory.

PATH-29. GLIOBLASTOMA TUMOR CLASSIFICATION BASED ON SPATIAL ANALYSIS OF ONCOGENIC AMPLIFICATIONS AND PROTEIN EXPRESSION

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi121-vi121
Author(s):  
Kacper Walentynowicz ◽  
Dalit Engelhardt ◽  
Shreya Yadav ◽  
Ugoma Onubogu ◽  
Roberto Salatino ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Protein Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Protein Profiling ◽  
Intratumor Heterogeneity ◽  
Neuronal Markers ◽  
Associated Proteins ◽  
Dual Amplification ◽  
Protein Expression Profiles

Abstract Heterogeneity of glioblastoma (GBM) has been extensively studied in recent years with identification of oncogenic drivers of GBM cellular subtypes. However, little is known about how these cells interact with each other or with the surrounding tumor microenvironment (TME). We employed spatial protein profiling targeting immune and neuronal markers (79 proteins) coupled with single-cell spatial maps of fluorescence in situ hybridization (FISH) for EGFR, CDK4, and PDGFRA on human GBM tissue sections. Several cores from 20 GBM samples were collected to create a tissue microarray, and 96 regions of interests were profiled with 37,844 nuclei for oncogenic amplification screen. Spatial protein profiling identified strong correlation of certain immune markers, TAU-associated proteins, and oligodendrocyte-enriched protein groups and overall high intratumor heterogeneity of TME. Our single-cell quantification of FISH signals showed differences among tumors based on the prevalence of dual amplification of EGFR and CDK4 within a cell relative to single oncogene amplified cells. High relative frequency of dual amplification was associated with increased expression of immune-related markers and decreased expression of EGFR protein. Moreover, this protein expression signature was associated with survival in another GBM dataset. Here, we present spatial genetic analysis at the single cell level coupled with protein expression profiles associated with tumor microenvironment. Our results suggest that assessment of genetic heterogeneity in GBM could potentially drive improved patient stratification and treatment.

scholarly journals Discovering a sparse set of pairwise discriminating features in high-dimensional data

2020 ◽  
Author(s):  
Samuel Melton ◽  
Sharad Ramanathan
Keyword(s):  
Single Cell ◽  
Dimensional Space ◽  
Cell Types ◽  
Dimensional Subspace ◽  
Supplementary Information ◽  
High Dimensional ◽  
Technological Advances ◽  
Data Points ◽  
Low Dimensional ◽  
Sparse Set

Abstract Motivation Recent technological advances produce a wealth of high-dimensional descriptions of biological processes, yet extracting meaningful insight and mechanistic understanding from these data remains challenging. For example, in developmental biology, the dynamics of differentiation can now be mapped quantitatively using single-cell RNA sequencing, yet it is difficult to infer molecular regulators of developmental transitions. Here, we show that discovering informative features in the data is crucial for statistical analysis as well as making experimental predictions. Results We identify features based on their ability to discriminate between clusters of the data points. We define a class of problems in which linear separability of clusters is hidden in a low-dimensional space. We propose an unsupervised method to identify the subset of features that define a low-dimensional subspace in which clustering can be conducted. This is achieved by averaging over discriminators trained on an ensemble of proposed cluster configurations. We then apply our method to single-cell RNA-seq data from mouse gastrulation, and identify 27 key transcription factors (out of 409 total), 18 of which are known to define cell states through their expression levels. In this inferred subspace, we find clear signatures of known cell types that eluded classification prior to discovery of the correct low-dimensional subspace. Availability and implementation https://github.com/smelton/SMD. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SuperCT: A supervised-learning-framework to enhance the characterization of single-cell transcriptomic profiles

2018 ◽  
Author(s):  
Peng Xie ◽  
Mingxuan Gao ◽  
Chunming Wang ◽  
Pawan Noel ◽  
Chaoyong Yang ◽  
...  
Keyword(s):  
Single Cell ◽  
Dimensional Space ◽  
Expression Profiles ◽  
Cell Types ◽  
Learning Framework ◽  
Tumor Tissues ◽  
Low Dimensional ◽  
Different Cell Types ◽  
Technical Framework

AbstractCharacterization of individual cell types is fundamental to the study of multicellular samples such as tumor tissues. Single-cell RNAseq techniques, which allow high-throughput expression profiling of individual cells, have significantly advanced our ability of this task. Currently, most of the scRNA-seq data analyses are commenced with unsupervised clustering of cells followed by visualization of clusters in a low-dimensional space. Clusters are often assigned to different cell types based on canonical markers. However, the efficiency of characterizing the known cell types in this way is low and limited by the investigator[s] knowledge. In this study, we present a technical framework of training the expandable supervised-classifier in order to reveal the single-cell identities based on their RNA expression profiles. Using multiple scRNA-seq datasets we demonstrate the superior accuracy, robustness, compatibility and expandability of this new solution compared to the traditional methods. We use two examples of model upgrade to demonstrate how the projected evolution of the cell-type classifier is realized.

scholarly journals Newly Emergent Apelin Expressing Endothelial Stem-like Cells Orchestrate Lung Microvascular Repair

2021 ◽  
Author(s):  
Rafael S Godoy ◽  
David P Cook ◽  
Nicholas D Cober ◽  
Yupu Deng ◽  
Liyuan Wang ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Acute Lung Injury ◽  
Endothelial Cell ◽  
Lung Injury ◽  
Single Cell ◽  
Diphtheria Toxin ◽  
Expression Profiles ◽  
Cell Populations ◽  
Cell Ablation ◽  
Apelin Receptor

Rational: Endothelial damage plays a central role in acute lung injury, and regeneration of lung vascular endothelium is required for its resolution in preclinical models. Objectives: We sought to define the cellular and molecular mechanisms underlying lung microvascular regeneration in acute lung injury induced by lung endothelial cell ablation. Methods: Transgenic mice were created expressing endothelial-targeted human diphtheria toxin receptor. Changes in lung cell populations and gene expression profiles were determined using single-cell RNA sequencing of dissociated lung cells (10x Genomics) at baseline (day 0) and days 3, 5 and 7 days after lung endothelial cell ablation. Measurements and Main Results: Intratracheal instillation of diphtheria toxin resulted in ablation of ~70% of lung endothelial cells, producing severe acute lung injury, with complete resolution by 7 days. Single cell analysis revealed 8 distinct endothelial cell clusters, including type-A capillary endothelial cells which were characterized by the unique expression of apelin at baseline. Diphtheria toxin-induced ablation resulted in the emergence of novel stem-like endothelial cells in the transitional 'general' capillary type-B endothelial population at day 3, characterized by the de novo expression of apelin. This was followed by the appearance of proliferative endothelial cells at day 5 expressing apelin receptor and Forkhead box M1 which were responsible for replenishment of all depleted endothelial cell populations. Treatment with an apelin receptor antagonist prevented recovery post DT resulting in excessive mortality. Conclusions: Targeted endothelial cell ablation revealed a remarkable regenerative capacity of the lung microvasculature orchestrated by newly emergent apelin-expressing endothelial stem-like cells primed for endothelial repair.

scholarly journals Single-cell RNA-seq analysis reveals the progression of human osteoarthritis

2018 ◽  
Vol 78 (1) ◽  
pp. 100-110 ◽  
Author(s):  
Quanbo Ji ◽  
Yuxuan Zheng ◽  
Guoqiang Zhang ◽  
Yuqiong Hu ◽  
Xiaoying Fan ◽  
...  
Keyword(s):  
Single Cell ◽  
Molecular Mechanisms ◽  
Computational Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cartilage Regeneration ◽  
Severity Index ◽  
Cartilage Degeneration ◽  
Cell Types ◽  
Human Cartilage

ObjectivesUnderstanding the molecular mechanisms underlying human cartilage degeneration and regeneration is helpful for improving therapeutic strategies for treating osteoarthritis (OA). Here, we report the molecular programmes and lineage progression patterns controlling human OA pathogenesis using single-cell RNA sequencing (scRNA-seq).MethodsWe performed unbiased transcriptome-wide scRNA-seq analysis, computational analysis and histological assays on 1464 chondrocytes from 10 patients with OA undergoing knee arthroplasty surgery. We investigated the relationship between transcriptional programmes of the OA landscape and clinical outcome using severity index and correspondence analysis.ResultsWe identified seven molecularly defined populations of chondrocytes in the human OA cartilage, including three novel phenotypes with distinct functions. We presented gene expression profiles at different OA stages at single-cell resolution. We found a potential transition among proliferative chondrocytes, prehypertrophic chondrocytes and hypertrophic chondrocytes (HTCs) and defined a new subdivision within HTCs. We revealed novel markers for cartilage progenitor cells (CPCs) and demonstrated a relationship between CPCs and fibrocartilage chondrocytes using computational analysis. Notably, we derived predictive targets with respect to clinical outcomes and clarified the role of different cell types for the early diagnosis and treatment of OA.ConclusionsOur results provide new insights into chondrocyte taxonomy and present potential clues for effective and functional manipulation of human OA cartilage regeneration that could lead to improved health.

scholarly journals K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data

2017 ◽  
Author(s):  
Florian Wagner ◽  
Yun Yan ◽  
Itai Yanai
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Nearest Neighbor ◽  
Expression Profiles ◽  
Simulated Data ◽  
T Cell Subsets ◽  
Cell Populations ◽  
Rna Seq ◽  
K Nearest Neighbor ◽  
Heterogeneous Tissues

High-throughput single-cell RNA-Seq (scRNA-Seq) is a powerful approach for studying heterogeneous tissues and dynamic cellular processes. However, compared to bulk RNA-Seq, single-cell expression profiles are extremely noisy, as they only capture a fraction of the transcripts present in the cell. Here, we propose the k-nearest neighbor smoothing (kNN-smoothing) algorithm, designed to reduce noise by aggregating information from similar cells (neighbors) in a computationally efficient and statistically tractable manner. The algorithm is based on the observation that across protocols, the technical noise exhibited by UMI-filtered scRNA-Seq data closely follows Poisson statistics. Smoothing is performed by first identifying the nearest neighbors of each cell in a step-wise fashion, based on partially smoothed and variance-stabilized expression profiles, and then aggregating their transcript counts. We show that kNN-smoothing greatly improves the detection of clusters of cells and co-expressed genes, and clearly outperforms other smoothing methods on simulated data. To accurately perform smoothing for datasets containing highly similar cell populations, we propose the kNN-smoothing 2 algorithm, in which neighbors are determined after projecting the partially smoothed data onto the first few principal components. We show that unlike its predecessor, kNN-smoothing 2 can accurately distinguish between cells from different T cell subsets, and enables their identification in peripheral blood using unsupervised methods. Our work facilitates the analysis of scRNA-Seq data across a broad range of applications, including the identification of cell populations in heterogeneous tissues and the characterization of dynamic processes such as cellular differentiation. Reference implementations of our algorithms can be found at https://github.com/yanailab/knn-smoothing.

scholarly journals Interpretable dimensionality reduction of single cell transcriptome data with deep generative models

2017 ◽  
Author(s):  
Jiarui Ding ◽  
Anne Condon ◽  
Sohrab P. Shah
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Spatial Organization ◽  
Mapping Function ◽  
Cell Types ◽  
Generative Models ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Data Points ◽  
Low Dimensional

Single-cell RNA-sequencing has great potential to discover cell types, identify cell states, trace development lineages, and reconstruct the spatial organization of cells. However, dimension reduction to interpret structure in single-cell sequencing data remains a challenge. Existing algorithms are either not able to uncover the clustering structures in the data, or lose global information such as groups of clusters that are close to each other. We present a robust statistical model, scvis, to capture and visualize the low-dimensional structures in single-cell gene expression data. Simulation results demonstrate that low-dimensional representations learned by scvis preserve both the local and global neighbour structures in the data. In addition, scvis is robust to the number of data points and learns a probabilistic parametric mapping function to add new data points to an existing embedding. We then use scvis to analyze four single-cell RNA-sequencing datasets, exemplifying interpretable two-dimensional representations of the high-dimensional single-cell RNA-sequencing data.

scholarly journals Single-cell RNA sequencing of freshly isolated bovine milk cells and cultured primary mammary epithelial cells

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Doreen Becker ◽  
Rosemarie Weikard ◽  
Frieder Hadlich ◽  
Christa Kühn
Keyword(s):  
Gene Expression ◽  
Epithelial Cells ◽  
Single Cell ◽  
Mammary Epithelial Cells ◽  
Expression Profiles ◽  
Bovine Milk ◽  
Mammary Epithelial ◽  
Mammary Tissue ◽  
Cell Populations ◽  
Cell Composition

AbstractBovine mammary function at molecular level is often studied using mammary tissue or primary bovine mammary epithelial cells (pbMECs). However, bulk tissue and primary cells are heterogeneous with respect to cell populations, adding further transcriptional variation in addition to genetic background. Thus, understanding of the variation in gene expression profiles of cell populations and their effect on function are limited. To investigate the mononuclear cell composition in bovine milk, we analyzed a single-cell suspension from a milk sample. Additionally, we harvested cultured pbMECs to characterize gene expression in a homogeneous cell population. Using the Drop-seq technology, we generated single-cell RNA datasets of somatic milk cells and pbMECs. The final datasets after quality control filtering contained 7,119 and 10,549 cells, respectively. The pbMECs formed 14 indefinite clusters displaying intrapopulation heterogeneity, whereas the milk cells formed 14 more distinct clusters. Our datasets constitute a molecular cell atlas that provides a basis for future studies of milk cell composition and gene expression, and could serve as reference datasets for milk cell analysis.

EPCO-13. MULTIOMIC SINGLE NUCLEUS RNA- AND ATACseq PROFILING REVEALS REGULATORS OF GLIOMA CELL STATE DIVERSITY

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi4-vi4
Author(s):  
Kevin Anderson ◽  
Kevin Johnson ◽  
Frederick Varn ◽  
Shannon Bessonett ◽  
Amit Gujar ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Single Cell ◽  
Glioma Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Chromatin Accessibility ◽  
Cell State ◽  
Oncogene Amplification ◽  
Distinct Cell ◽  
Single Nucleus

Abstract Multiomic single nucleus RNA- and ATACseq profiling reveals regulators of glioma cell state diversity. The extensive intra- and intertumoral heterogeneity observed in glioma reflects the resistance to therapy and poor prognosis observed clinically. Single-cell sequencing studies have highlighted that glioma heterogeneity reflects the co-existence of cell subpopulations with distinct cell states. Prior studies have also shown that EGFR-amplifying extrachromosomal DNA (ecDNA) elements in IDH-wild-type gliomas can contribute to heterogeneity by driving oncogene amplification through long range chromatin contacts. However, single cell studies have largely focused on analyses of transcriptional profiles, and the epigenetic mechanisms underlying the contribution of ecDNA elements to tumor cell state diversity remain poorly understood. To further our understanding of the regulatory programs that contribute to transcriptional diversity and mediate the distribution of tumor cell states, we profiled primary-recurrent tumor pairs from 18 patient samples with multiomic single-nucleus RNA- and ATACseq, resulting in 86,135 cells identified with linked chromatin accessibility and gene expression profiles. Integrative clustering of the tumor cells identified tumor cell states ranging from a stem-like to differentiated- phenotype that were also associated with differences in chromatin accessibility and inferred transcription factor binding activity. Analyses of chromatin accessibility resulted in the identification of ecDNA, and integrative clustering of ecDNA+ cells highlighted distinct cell states with increased copy number burden, oncogene amplification, and differential chromatin accessibility. These results suggest that a better understanding of extrachromosomal contributions to tumor diversity would aid in development of more efficient therapies.

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