Abstract 3391: Dissecting intratumoral cell-cell interactions in myeloid reprogramming by single cell RNA-seq

2019 ◽  
Author(s):  
Qianqian Song ◽  
Gregory A. Hawkins ◽  
Leonard Wudel ◽  
Ping-Chieh Chou ◽  
Elizabeth Forbes ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Interactions ◽  
Rna Seq ◽  
Cell Cell

scholarly journals Runx2-Twist1 interaction coordinates cranial neural crest guidance of soft palate myogenesis

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Xia Han ◽  
Jifan Feng ◽  
Tingwei Guo ◽  
Yong-Hwee Eddie Loh ◽  
Yuan Yuan ◽  
...  
Keyword(s):  
Neural Crest ◽  
Single Cell ◽  
Soft Palate ◽  
Muscle Development ◽  
Cell Interactions ◽  
Lineage Commitment ◽  
Rna Seq ◽  
Cranial Neural Crest ◽  
Marker Expression ◽  
Cell Cell

Cranial neural crest (CNC) cells give rise to bone, cartilage, tendons, and ligaments of the vertebrate craniofacial musculoskeletal complex, as well as regulate mesoderm-derived craniofacial muscle development through cell-cell interactions. Using the mouse soft palate as a model, we performed an unbiased single-cell RNA-seq analysis to investigate the heterogeneity and lineage commitment of CNC derivatives during craniofacial muscle development. We show that Runx2, a known osteogenic regulator, is expressed in the CNC-derived perimysial and progenitor populations. Loss of Runx2 in CNC-derivatives results in reduced expression of perimysial markers (Aldh1a2 and Hic1) as well as soft palate muscle defects in Osr2-Cre;Runx2fl/fl mice. We further reveal that Runx2 maintains perimysial marker expression through suppressing Twist1, and that myogenesis is restored in Osr2-Cre;Runx2fl/fl;Twist1fl/+ mice. Collectively, our findings highlight the roles of Runx2, Twist1, and their interaction in regulating the fate of CNC-derived cells as they guide craniofacial muscle development through cell-cell interactions.

Abstract 3391: Dissecting intratumoral cell-cell interactions in myeloid reprogramming by single cell RNA-seq

2019 ◽  
Author(s):  
Qianqian Song ◽  
Gregory A. Hawkins ◽  
Leonard Wudel ◽  
Ping-Chieh Chou ◽  
Elizabeth Forbes ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Interactions ◽  
Rna Seq ◽  
Cell Cell

scholarly journals Analysis of Single-Cell RNA-Seq Identifies Cell-Cell Communication Associated with Tumor Characteristics

Cell Reports ◽  
2018 ◽  
Vol 25 (6) ◽  
pp. 1458-1468.e4 ◽  
Author(s):  
Manu P. Kumar ◽  
Jinyan Du ◽  
Georgia Lagoudas ◽  
Yang Jiao ◽  
Andrew Sawyer ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Communication ◽  
Tumor Characteristics ◽  
Rna Seq ◽  
Cell Cell

scholarly journals Identifying signaling genes in spatial single-cell expression data

2020 ◽  
Author(s):  
Dongshunyi Li ◽  
Jun Ding ◽  
Ziv Bar-Joseph
Keyword(s):  
Single Cell ◽  
Cell Interactions ◽  
Excitatory Neuron ◽  
Expression Data ◽  
Mixture Of Experts ◽  
Prediction Of Response ◽  
Technological Advances ◽  
A Cell ◽  
Cell Expression ◽  
Cell Cell

Abstract Motivation Recent technological advances enable the profiling of spatial single-cell expression data. Such data present a unique opportunity to study cell–cell interactions and the signaling genes that mediate them. However, most current methods for the analysis of these data focus on unsupervised descriptive modeling, making it hard to identify key signaling genes and quantitatively assess their impact. Results We developed a Mixture of Experts for Spatial Signaling genes Identification (MESSI) method to identify active signaling genes within and between cells. The mixture of experts strategy enables MESSI to subdivide cells into subtypes. MESSI relies on multi-task learning using information from neighboring cells to improve the prediction of response genes within a cell. Applying the methods to three spatial single-cell expression datasets, we show that MESSI accurately predicts the levels of response genes, improving upon prior methods and provides useful biological insights about key signaling genes and subtypes of excitatory neuron cells. Availability and implementation MESSI is available at: https://github.com/doraadong/MESSI

scholarly journals Spatial analysis of ligand-receptor interactions in skin cancer at genome-wide and single-cell resolution

2020 ◽  
Author(s):  
M Tran ◽  
S Yoon ◽  
ST Min ◽  
S Andersen ◽  
K Devitt ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Single Cell ◽  
Molecular Mechanisms ◽  
Digital Pcr ◽  
Droplet Digital Pcr ◽  
Cell Interactions ◽  
Genome Wide ◽  
Receptor Interactions ◽  
Cell Cell

AbstractThe ability to study cancer-immune cell communication across the whole tumor section without tissue dissociation is important to understand molecular mechanisms of cancer immunotherapy and drug targets. Current experimental methods such as immunohistochemistry allow researchers to investigate a small number of cells or a limited number of ligand-receptor pairs at tissue scale with limited cellular resolution. In this work, we developed a powerful experimental and analytical pipeline that allows for the genome-wide discovery and targeted validation of cellular communication. By profiling thousands of genes, spatial transcriptomic and single-cell RNA sequencing data show genes that are possibly involved in interactions. The expression of the candidate genes could be visualized by single-molecule in situ hybridization and droplet digital PCR. We developed a computational pipeline called STRISH that enables us to quantitatively model cell-cell interactions by automatically scanning for local expression of RNAscope data to recapitulate an interaction landscape across the whole tissue. Furthermore, we showed the strong correlation of microscopic RNAscope imaging data analyzed by STRISH with the gene expression values measured by droplet digital PCR. We validated the unique ability of this approach to discover new cell-cell interactions in situ through analysis of two types of cancer, basal cell carcinoma and squamous cell carcinoma. We expect that the approach described here will help to discover and validate ligand receptor interactions in different biological contexts such as immune-cancer cell interactions within a tumor.

scholarly journals Examining Cell-Cell Interactions in the Kidney Using AFM Single-Cell Force Spectroscopy

2019 ◽  
pp. 189-201 ◽  
Author(s):  
Eleftherios Siamantouras ◽  
Claire E. Hills ◽  
Kuo-Kang Liu ◽  
Paul E. Squires
Keyword(s):  
Single Cell ◽  
Force Spectroscopy ◽  
Cell Interactions ◽  
Cell Force ◽  
Cell Cell

Immunomodulatory stromal cell interactions in the eye

2020 ◽  
Vol 12 (538) ◽  
pp. eabb5668
Author(s):  
Gerald P. Morris
Keyword(s):  
Single Cell ◽  
Stromal Cell ◽  
Ocular Inflammation ◽  
Cell Interactions ◽  
Single Cell Sequencing ◽  
Cell Cell

Single-cell sequencing of eye stroma reveals cell-cell interactions essential for suppressing ocular inflammation.

scholarly journals Modeling heterogeneous tumor growth dynamics and cell–cell interactions at single-cell and cell-population resolution

2019 ◽  
Vol 17 ◽  
pp. 24-34 ◽  
Author(s):  
Leonard A. Harris ◽  
Samantha Beik ◽  
Patricia M.M. Ozawa ◽  
Lizandra Jimenez ◽  
Alissa M. Weaver
Keyword(s):  
Tumor Growth ◽  
Single Cell ◽  
Cell Population ◽  
Growth Dynamics ◽  
Cell Interactions ◽  
Heterogeneous Tumor ◽  
Cell Cell

scholarly journals Studying 3D cell cultures in a microfluidic droplet array under multiple time-resolved conditions

2018 ◽  
Author(s):  
Raphaël F.-X. Tomasi ◽  
Sébastien Sart ◽  
Tiphaine Champetier ◽  
Charles N. Baroud
Keyword(s):  
Single Cell ◽  
Cell Cultures ◽  
Single Cell Analysis ◽  
Cell Interactions ◽  
Multiple Time ◽  
Time Resolved ◽  
Cell Functions ◽  
Wide Range ◽  
Cell Cell

The relevance of traditional cell cultures to cellular behavior in vivo is limited, since the two-dimensional (2D) format does not appropriately reproduce the microenvironment that regulates cell functions. In this context, spheroids are an appealing 3D cell culture format to complement standard techniques, by combining a high level of biological relevance with simple production protocols. However the methods for spheroid manipulation are still labor intensive, which severely limits the complexity of operations that can be performed on statistically relevant numbers of individual spheroids. Here we show how to apply hundreds of different conditions on spheroids in a single microfluidic chip, where each spheroid is produced and immobilized in an anchored droplet. By using asymmetric anchor shapes, a second drop can be merged with the spheroid-containing drop at a later time. This time-delayed merging uniquely enables two classes of applications that we demonstrate: (1) the initiation of cell-cell interactions on demand, either for building micro-tissues within the device or for observing antagonistic cell-cell interactions with applications in immuno-therapy or host-pathogen interactions, (2) a detailed dose-response curve obtained by exposing an array of hepatocyte-like spheroids to droplets containing a wide range of acetaminophen concentrations. The integrated microfluidic format allows time-resolved measurements of the response of hundreds of spheroids with a single-cell resolution. The data shows an internally regulated evolution of each spheroid, in addition to a heterogeneity of the responses to the drug that the single-cell analysis correlates with the initial presence and location of dead cells within each spheroid.

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.

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