Relationships between cell-cell interactions, cAMP, and gene expression in a developmental mutant ofDictyostelium discoideum

1987 ◽  
Vol 12 (11) ◽  
pp. 1005-1012
Author(s):  
Donna M. Bozzone ◽  
Russel E. Kohnken ◽  
Edward A. Berger
Keyword(s):  
Gene Expression ◽  
Cell Interactions ◽  
Developmental Mutant ◽  
Cell Cell

scholarly journals Deciphering cell–cell interactions and communication from gene expression

2020 ◽  
Author(s):  
Erick Armingol ◽  
Adam Officer ◽  
Olivier Harismendy ◽  
Nathan E. Lewis
Keyword(s):  
Gene Expression ◽  
Cell Interactions ◽  
Cell Cell

scholarly journals Transcriptional signatures of cell-cell interactions are dependent on cellular context

2021 ◽  
Author(s):  
Brendan T Innes ◽  
Gary D Bader
Keyword(s):  
Gene Expression ◽  
Gene Expression Signature ◽  
Cell Interaction ◽  
Cell Interactions ◽  
Receptor Interaction ◽  
Perturbation Data ◽  
Cellular Context ◽  
Transcriptomics Data ◽  
Downstream Gene Expression ◽  
Cell Cell

Cell-cell interactions are often predicted from single-cell transcriptomics data based on observing receptor and corresponding ligand transcripts in cells. These predictions could theoretically be improved by inspecting the transcriptome of the receptor cell for evidence of gene expression changes in response to the ligand. It is commonly expected that a given receptor, in response to ligand activation, will have a characteristic downstream gene expression signature. However, this assumption has not been well tested. We used ligand perturbation data from both the high-throughput Connectivity Map resource and published transcriptomic assays of cell lines and purified cell populations to determine whether ligand signals have unique and generalizable transcriptional signatures across biological conditions. Most of the receptors we analyzed did not have such characteristic gene expression signatures - instead these signatures were highly dependent on cell type. Cell context is thus important when considering transcriptomic evidence of ligand signaling, which makes it challenging to build generalizable ligand-receptor interaction signatures to improve cell-cell interaction predictions.

scholarly journals Spatially Correlated Gene Expression in Bacterial Groups: The Role of Lineage History, Spatial Gradients, and Cell-Cell Interactions

Cell Systems ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 496-507.e6 ◽  
Author(s):  
Simon van Vliet ◽  
Alma Dal Co ◽  
Annina R. Winkler ◽  
Stefanie Spriewald ◽  
Bärbel Stecher ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Interactions ◽  
Spatially Correlated ◽  
Spatial Gradients ◽  
Bacterial Groups ◽  
Cell Cell

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.

Patterns of gene expression during sea urchin development, and the role of cell-cell interactions

2020 ◽  
pp. 85-85
Author(s):  
P.D. Kingsley ◽  
Q. Yang ◽  
D.L. Hurley ◽  
L.M. Angerer ◽  
R.C. Angerer
Keyword(s):  
Gene Expression ◽  
Sea Urchin ◽  
Cell Interactions ◽  
Cell Cell

Dependence of hepatocyte-specific gene expression on cell-cell interactions in primary culture.

1985 ◽  
Vol 4 (10) ◽  
pp. 2487-2491 ◽  
Author(s):  
J.M. Fraslin ◽  
B. Kneip ◽  
S. Vaulont ◽  
D. Glaise ◽  
A. Munnich ◽  
...  
Keyword(s):  
Gene Expression ◽  
Primary Culture ◽  
Cell Interactions ◽  
Specific Gene ◽  
Specific Gene Expression ◽  
Cell Cell
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