scholarly journals Microfluidic System-based Time-course Tracking of Physical Proximity Between Cells and Its Effect on Gene Expression for Elucidating Live Single Cancer-immune Cell Interactions

2021 ◽  
Author(s):  
Bianca Flores ◽  
Smriti Chawla ◽  
Ning Ma ◽  
Chad Sanada ◽  
Praveen Kujur ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Immune Cell ◽  
Cell Communication ◽  
Time Lapse ◽  
Cell Types ◽  
Temporal Variations ◽  
Microfluidic System ◽  
Cell Interactions ◽  
Cell Cell

Abstract Cell-cell communication and physical interactions play a vital role in cancer initiation, homeostasis, progression, and immune response. Here, we report a system that combines live capture of different cell types, co-incubation, time-lapse imaging, and gene expression profiling of doublets using a microfluidic integrated fluidic circuit (IFC) that enables measurement of physical distances between cells and the associated transcriptional profiles due to cell-cell interactions. The temporal variations in natural killer (NK) - triple-negative breast cancer (TNBC) cell distances were tracked and compared with terminally profiled cellular transcriptomes. The results showed the time-bound activities of regulatory modules and alluded to the existence of transcriptional memory. Our experimental and bioinformatic approaches serve as a proof of concept for interrogating live cell interactions at doublet resolution, which can be applied across different cancers and cell types.

scholarly journals Microfluidic system-based time-course tracking of physical proximity between cells and its effect on gene expression for elucidating live single cancer-immune cell interactions

2021 ◽  
Author(s):  
Bianca C.T Flores ◽  
Smriti Chawla ◽  
Ning Ma ◽  
Chad Sanada ◽  
Praveen Kumar Kujur ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Immune Cell ◽  
Cell Communication ◽  
Time Lapse ◽  
Cell Types ◽  
Temporal Variations ◽  
Microfluidic System ◽  
Cell Interactions ◽  
Cell Cell

Cell-cell communication and physical interactions play a vital role in cancer initiation, homeostasis, progression, and immune response. Here, we report a system that combines live capture of different cell types, co-incubation, time-lapse imaging, and gene expression profiling of doublets using a microfluidic integrated fluidic circuit (IFC) that enables measurement of physical distances between cells and the associated transcriptional profiles due to cell-cell interactions. The temporal variations in natural killer (NK) - triple-negative breast cancer (TNBC) cell distances were tracked and compared with terminally profiled cellular transcriptomes. The results showed the time-bound activities of regulatory modules and alluded to the existence of transcriptional memory. Our experimental and bioinformatic approaches serve as a proof of concept for interrogating live cell interactions at doublet resolution, which can be applied across different cancers and cell types.

scholarly journals Deconvolving clinically relevant cellular immune crosstalk from bulk gene expression using CODEFACS and LIRICS

2021 ◽  
Author(s):  
Kun Wang ◽  
Sushant Patkar ◽  
Joo Sang Lee ◽  
E. Michael Gertz ◽  
Welles Robinson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Mixture ◽  
Cell Types ◽  
Cell Interactions ◽  
Specific Gene ◽  
Cell Type ◽  
Patient Response ◽  
Receptor Interactions ◽  
Cell Type Specific ◽  
Cell Cell

AbstractThe tumor microenvironment (TME) is a complex mixture of cell-types that interact with each other to affect tumor growth and clinical outcomes. To accelerate the discovery of such interactions, we developed CODEFACS (COnfident DEconvolution For All Cell Subsets), a deconvolution tool inferring cell-type-specific gene expression in each sample from bulk expression measurements, and LIRICS (LIgand Receptor Interactions between Cell Subsets), a supporting pipeline that analyzes the deconvolved gene expression from CODEFACS to identify clinically relevant ligand-receptor interactions between cell-types. Using 15 benchmark test datasets, we first demonstrate that CODEFACS substantially improves the ability to reconstruct cell-type-specific transcriptomes from individual bulk samples, compared to the state-of-the-art method, CIBERSORTx. Second, analyzing the TCGA, we uncover cell-cell interactions that specifically occur in TME of mismatch-repair-deficient tumors and are associated with their high response rates to anti-PD1 treatment. These results point to specific T-cell co-stimulating interactions that enhance immunotherapy responses in tumors independently of their mutation burden levels. Finally, using machine learning, we identify a subset of cell-cell interactions that predict patient response to anti-PD1 therapy in melanoma better than recently published bulk transcriptomics-based signatures. CODEFACS offers a way to study bulk cancer and normal transcriptomes at a cell type-specific resolution, complementing single-cell transcriptomics.

scholarly journals Investigating immune and non-immune cell interactions in head and neck tumors by single-cell RNA sequencing

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Cornelius H. L. Kürten ◽  
Aditi Kulkarni ◽  
Anthony R. Cillo ◽  
Patricia M. Santos ◽  
Anna K. Roble ◽  
...  
Keyword(s):  
Head And Neck ◽  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cell ◽  
Cell Communication ◽  
Cell Interactions ◽  
Cell Populations ◽  
Hpv Status ◽  
Single Cell Rna Sequencing ◽  
Cell Cell

AbstractHead and neck squamous cell carcinoma (HNSCC) is characterized by complex relations between stromal, epithelial, and immune cells within the tumor microenvironment (TME). To enable the development of more efficacious therapies, we aim to study the heterogeneity, signatures of unique cell populations, and cell-cell interactions of non-immune and immune cell populations in 6 human papillomavirus (HPV)+ and 12 HPV– HNSCC patient tumor and matched peripheral blood specimens using single-cell RNA sequencing. Using this dataset of 134,606 cells, we show cell type-specific signatures associated with inflammation and HPV status, describe the negative prognostic value of fibroblasts with elastic differentiation specifically in the HPV+ TME, predict therapeutically targetable checkpoint receptor-ligand interactions, and show that tumor-associated macrophages are dominant contributors of PD-L1 and other immune checkpoint ligands in the TME. We present a comprehensive single-cell view of cell-intrinsic mechanisms and cell-cell communication shaping the HNSCC microenvironment.

scholarly journals stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues

2020 ◽  
Author(s):  
Duy Pham ◽  
Xiao Tan ◽  
Jun Xu ◽  
Laura F. Grice ◽  
Pui Yeng Lam ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cell Types ◽  
Cell Interactions ◽  
Specific Cell ◽  
Cell Type ◽  
Transcriptional Profiles ◽  
Tissue Morphology ◽  
Spatial Transition ◽  
Cell Cell

ABSTRACTSpatial Transcriptomics is an emerging technology that adds spatial dimensionality and tissue morphology to the genome-wide transcriptional profile of cells in an undissociated tissue. Integrating these three types of data creates a vast potential for deciphering novel biology of cell types in their native morphological context. Here we developed innovative integrative analysis approaches to utilise all three data types to first find cell types, then reconstruct cell type evolution within a tissue, and search for tissue regions with high cell-to-cell interactions. First, for normalisation of gene expression, we compute a distance measure using morphological similarity and neighbourhood smoothing. The normalised data is then used to find clusters that represent transcriptional profiles of specific cell types and cellular phenotypes. Clusters are further sub-clustered if cells are spatially separated. Analysing anatomical regions in three mouse brain sections and 12 human brain datasets, we found the spatial clustering method more accurate and sensitive than other methods. Second, we introduce a method to calculate transcriptional states by pseudo-space-time (PST) distance. PST distance is a function of physical distance (spatial distance) and gene expression distance (pseudotime distance) to estimate the pairwise similarity between transcriptional profiles among cells within a tissue. We reconstruct spatial transition gradients within and between cell types that are connected locally within a cluster, or globally between clusters, by a directed minimum spanning tree optimisation approach for PST distance. The PST algorithm could model spatial transition from non-invasive to invasive cells within a breast cancer dataset. Third, we utilise spatial information and gene expression profiles to identify locations in the tissue where there is both high ligand-receptor interaction activity and diverse cell type co-localisation. These tissue locations are predicted to be hotspots where cell-cell interactions are more likely to occur. We detected tissue regions and ligand-receptor pairs significantly enriched compared to background distribution across a breast cancer tissue. Together, these three algorithms, implemented in a comprehensive Python software stLearn, allow for the elucidation of biological processes within healthy and diseased tissues.

scholarly journals Gene Expression Analysis of Astrocyte and Microglia Endocannabinoid Signaling during Autoimmune Demyelination

Biomolecules ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1228 ◽  
Author(s):  
Álvaro Moreno-García ◽  
Ana Bernal-Chico ◽  
Teresa Colomer ◽  
Alfredo Rodríguez-Antigüedad ◽  
Carlos Matute ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Cannabinoid Receptors ◽  
Immune Cell ◽  
Hydrolytic Enzymes ◽  
Endocannabinoid System ◽  
Cell Types ◽  
Protective Effects ◽  
Genes Encoding ◽  
Autoimmune Demyelination

The endocannabinoid system is associated with protective effects in multiple sclerosis (MS) that involve attenuated innate immune cell responses. Astrocytes and microglia are modulated by endocannabinoids and participate in the biosynthesis and metabolism of these compounds. However, the role of neuroglial cells as targets and mediators of endocannabinoid signaling in MS is poorly understood. Here we used a microfluidic RT-qPCR screen to assess changes in the expression of the main endocannabinoid signaling genes in astrocytes and microglia purified from female mice during the time-course of experimental autoimmune encephalomyelitis (EAE). We show that astrocytes and microglia upregulate the expression of genes encoding neurotoxic A1 and pro-inflammatory molecules at the acute disease with many of these transcripts remaining elevated during the recovery phase. Both cell populations exhibited an early onset decrease in the gene expression levels of 2-arachidonoylglycerol (2-AG) hydrolytic enzymes that persisted during EAE progression as well as cell-type-specific changes in the transcript levels for genes encoding cannabinoid receptors and molecules involved in anandamide (AEA) signaling. Our results demonstrate that astrocytes and microglia responses to autoimmune demyelination involve alterations in the expression of multiple endocannabinoid signaling-associated genes and suggest that this system may regulate the induction of neurotoxic and pro-inflammatory transcriptional programs in both cell types during MS.

scholarly journals An organism-wide atlas of hormonal signaling based on the mouse lemur single-cell transcriptome

2021 ◽  
Author(s):  
Shixuan Liu ◽  
Camille Ezran ◽  
Michael F.Z. Wang ◽  
Zhengda Li ◽  
Jonathan Z. Long ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Cell Communication ◽  
Cell Types ◽  
Mouse Lemur ◽  
Target Cells ◽  
Endocrine Gland ◽  
Hormone Regulation ◽  
Cell Cell

Hormones coordinate long-range cell-cell communication in multicellular organisms and play vital roles in normal physiology, metabolism, and health. Using the newly-completed organism-wide single cell transcriptional atlas of a non-human primate, the mouse lemur (Microcebus murinus), we have systematically identified hormone-producing and -target cells for 87 classes of hormones, and have created a browsable atlas for hormone signaling that reveals previously unreported sites of hormone regulation and species-specific rewiring. Hormone ligands and receptors exhibited cell-type-dependent, stereotypical expression patterns, and their transcriptional profiles faithfully classified the discrete cell types defined by the full transcriptome, despite their comprising less than 1% of the transcriptome. Although individual cell types generally exhibited the same characteristic patterns of hormonal gene expression, a number of examples of similar or seemingly-identical cell types (e.g., endothelial cells of the lung versus of other organs) displayed different hormonal gene expression patterns. By linking ligand-expressing cells to the cells expressing the corresponding receptor, we constructed an organism-wide map of the hormonal cell-cell communication network. The hormonal cell-cell network was remarkably densely and robustly connected, and included classical hierarchical circuits (e.g. pituitary → peripheral endocrine gland → diverse cell types) as well as examples of highly distributed control. The network also included both well-known examples of feedback loops and a long list of potential novel feedback circuits. This primate hormone atlas provides a powerful resource to facilitate discovery of regulation on an organism-wide scale and at single-cell resolution, complementing the single-site-focused strategy of classical endocrine studies. The network nature of hormone regulation and the principles discovered here further emphasize the importance of a systems approach to understanding hormone regulation.

scholarly journals CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

2022 ◽  
Author(s):  
Takaho Tsuchiya ◽  
Hiroki Hori ◽  
Haruka Ozaki
Keyword(s):  
Gene Expression ◽  
Single Cells ◽  
Cell Types ◽  
Regression Modeling ◽  
Transcriptome Data ◽  
Cell Type ◽  
Neighboring Cell ◽  
Expression Variability ◽  
Cell Expression ◽  
Cell Cell

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

scholarly journals Enabling out-of-clinic human immunity studies via single-cell profiling of capillary blood

2020 ◽  
Author(s):  
Tatyana Dobreva ◽  
David Brown ◽  
Jong Hwee Park ◽  
Matt Thomson
Keyword(s):  
Gene Expression ◽  
Immune System ◽  
Environmental Factors ◽  
Single Cell ◽  
Immune Cell ◽  
Cell Types ◽  
Capillary Blood ◽  
Specific Gene ◽  
Human Immune ◽  
Over Time

AbstractAn individual’s immune system is driven by both genetic and environmental factors that vary over time. To better understand the temporal and inter-individual variability of gene expression within distinct immune cell types, we developed a platform that leverages multiplexed single-cell sequencing and out-of-clinic capillary blood extraction to enable simplified, cost-effective profiling of the human immune system across people and time at single-cell resolution. Using the platform, we detect widespread differences in cell type-specific gene expression between subjects that are stable over multiple days.SummaryIncreasing evidence implicates the immune system in an overwhelming number of diseases, and distinct cell types play specific roles in their pathogenesis.1,2 Studies of peripheral blood have uncovered a wealth of associations between gene expression, environmental factors, disease risk, and therapeutic efficacy.4 For example, in rheumatoid arthritis, multiple mechanistic paths have been found that lead to disease, and gene expression of specific immune cell types can be used as a predictor of therapeutic non-response.12 Furthermore, vaccines, drugs, and chemotherapy have been shown to yield different efficacy based on time of administration, and such findings have been linked to the time-dependence of gene expression in downstream pathways.21,22,23 However, human immune studies of gene expression between individuals and across time remain limited to a few cell types or time points per subject, constraining our understanding of how networks of heterogeneous cells making up each individual’s immune system respond to adverse events and change over time.

scholarly journals An endometrial organoid model of Chlamydia-epithelial and immune cell interactions

2020 ◽  
Author(s):  
Lee Dolat ◽  
Raphael H. Valdivia
Keyword(s):  
Epithelial Cell ◽  
Cell Biology ◽  
Immune Cell ◽  
Three Dimensional ◽  
Time Lapse ◽  
Cell Types ◽  
Infection Process ◽  
Host Cells ◽  
Cell Diversity ◽  
Intracellular Organelles

ABSTRACTOur understanding of how the obligate intracellular bacterium Chlamydia trachomatis reprograms the cell biology of host cells in the upper genital tract is largely based on observations made in cell culture with transformed epithelial cell lines. Here we describe a primary spherical organoid system derived from endometrial tissue to recapitulate epithelial cell diversity, polarity, and ensuing responses to Chlamydia infection. Using high-resolution and time-lapse microscopy, we catalogue the infection process in organoids from invasion to egress, including the reorganization of the cytoskeleton and positioning of intracellular organelles. We show this model is amenable to screening C. trachomatis mutants for defects in the fusion of pathogenic vacuoles, the recruitment of intracellular organelles, and inhibition of cell death. Moreover, we reconstructed a primary immune cell response by co-culturing infected organoids with neutrophils, and determined that the effector TepP limits the recruitment of neutrophils to infected organoids. Collectively, our model details a system to study the cell biology of Chlamydia infections in three dimensional structures that better reflect the diversity of cell types and polarity encountered by Chlamydia upon infection of their animal hosts.Summary statement3D endometrial organoids to model Chlamydia infection and the role of secreted virulence factors in reprogramming host epithelial cells and immune cell recruitment

scholarly journals GSDMA drives the most replicated association with asthma in naïve CD4+ T cells

2019 ◽  
Author(s):  
Anne-Marie Madore ◽  
Lucile Pain ◽  
Anne-Marie Boucher-Lafleur ◽  
Jolyane Meloche ◽  
Andréanne Morin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
T Cells ◽  
Genetic Variants ◽  
Immune Cell ◽  
Cell Types ◽  
Sequencing Data ◽  
Opposite Pattern ◽  
Genetic Mechanisms ◽  
New Gene

AbstractBackgroundThe 17q12-21 locus is the most replicated association with asthma. However, no study had described the genetic mechanisms underlying this association considering all genes of the locus in immune cell samples isolated from asthmatic and non-asthmatic individuals.ObjectiveThis study takes benefit of samples from naïve CD4+ T cells and eosinophils isolated from the same 200 individuals to describe specific interactions between genetic variants, gene expression and DNA methylation levels for the 17q12-21 asthma locus.Methods and ResultsAfter isolation of naïve CD4+ T cells and eosinophils from blood samples, next generation sequencing was used to measure DNA methylation levels and gene expression counts. Genetic interactions were then evaluated considering genetic variants from imputed genotype data. In naïve CD4+ T cells but not eosinophils, 20 SNPs in the fourth and fifth haplotype blocks modulated both GSDMA expression and methylation levels, showing an opposite pattern of allele frequencies and expression counts in asthmatics compared to controls. Moreover, negative correlations have been measured between methylation levels of CpG sites located within the 1.5 kb region from the transcription start site of GSDMA and its expression counts.ConclusionAvailability of sequencing data from two key cell types isolated from asthmatic and non-asthmatic individuals allowed identifying a new gene in naïve CD4+ T cells that drives the association with the 17q12-21 locus, leading to a better understanding of the genetic mechanisms taking place in it.

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