scholarly journals Single-cell response of bacterial groups to light and other environmental factors in the Delaware Bay, USA

2011 ◽  
Vol 62 (3) ◽  
pp. 267-277 ◽  
Author(s):  
TRA Straza ◽  
DL Kirchman
Keyword(s):  
Environmental Factors ◽  
Single Cell ◽  
Cell Response ◽  
Delaware Bay ◽  
Bacterial Groups ◽  
Single Cell Response

scholarly journals 2P325 Analyzing the single cell response of macrophage to the antigen stimulation (I) Development of antigen-stimulation and analysis system

2005 ◽  
Vol 45 (supplement) ◽  
pp. S201
Author(s):  
H. Shindou ◽  
K. Orita ◽  
K. Matsumura ◽  
Y. Wakamoto ◽  
H. Ishimoto ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Response ◽  
Antigen Stimulation ◽  
Analysis System ◽  
Single Cell Response

scholarly journals Single cell response landscape of graded Nodal signaling in zebrafish explants

2021 ◽  
Author(s):  
Tao Cheng ◽  
Yanyi Xing ◽  
Yunfei Li ◽  
Cong Liu ◽  
Ying Huang ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Cell Response ◽  
Expression Patterns ◽  
Cell Types ◽  
Nodal Signaling ◽  
Animal Pole ◽  
Fate Decision ◽  
Single Cell Response

Nodal, as a morphogen, plays important roles in cell fate decision, pattern formation and organizer function. But because of the complex context in vivo and technology limitations, systematic studying of genes, cell types and patterns induced by Nodal alone is still missing. Here, by using a relatively simplified model, the zebrafish blastula animal pole explant avoiding additional instructive signals and prepatterns, we constructed a single cell response landscape of graded Nodal signaling, identified 105 Nodal immediate targets and depicted their expression patterns. Our results show that Nodal signaling is sufficient to induce anterior-posterior patterned axial mesoderm and head structure. Surprisingly, the endoderm induced by Nodal alone is mainly the anterior endoderm which gives rise to the pharyngeal pouch only, but not internal organs. Among the 105 Nodal targets, we identified 14 genes carrying varying levels of axis induction capability. Overall, our work provides new insights for understanding of the Nodal function and a valuable resource for future studies of patterning and morphogenesis induced by it.

scholarly journals Real-time single-cell response to stiffness

2010 ◽  
Vol 107 (38) ◽  
pp. 16518-16523 ◽  
Author(s):  
D. Mitrossilis ◽  
J. Fouchard ◽  
D. Pereira ◽  
F. Postic ◽  
A. Richert ◽  
...  
Keyword(s):  
Single Cell ◽  
Real Time ◽  
Cell Response ◽  
Single Cell Response

Single-Cell Response to the Rigidity of Semiconductor Nanomembranes on Compliant Substrates

2020 ◽  
Vol 12 (9) ◽  
pp. 10697-10705 ◽  
Author(s):  
Nadeem Abdul ◽  
Matthew N. Rush ◽  
Jiri Nohava ◽  
Ursula Amezcua ◽  
Andrew P. Shreve ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Response ◽  
Compliant Substrates ◽  
Single Cell Response

scholarly journals 2P326 Analyzing the single cell response of macrophage to the antigen stimulation (II) Processing of acquired information in the single cell

2005 ◽  
Vol 45 (supplement) ◽  
pp. S201
Author(s):  
K. Orita ◽  
H. Shindou ◽  
K. Matsumura ◽  
Y. Wakamoto ◽  
H. Ishimoto ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Response ◽  
Antigen Stimulation ◽  
Single Cell Response

scholarly journals Single Cell Response to Periodic Environmental Stimuli using a Microfluidic Bioreactor

2014 ◽  
Vol 106 (2) ◽  
pp. 225a
Author(s):  
Eric M. Johnson Chavarria ◽  
Utsav Agrawal ◽  
Melikhan Tanyeri ◽  
Thomas E. Kuhlman ◽  
Charles M. Schroeder
Keyword(s):  
Single Cell ◽  
Cell Response ◽  
Environmental Stimuli ◽  
Single Cell Response

scholarly journals Compositional perturbation autoencoder for single-cell response modeling

2021 ◽  
Author(s):  
Mohammad Lotfollahi ◽  
Anna Klimovskaia ◽  
Carlo De Donno ◽  
Yuge Ji ◽  
Ignacio L. Ibarra ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Response ◽  
In Silico ◽  
Linear Models ◽  
Cell Types ◽  
Drug Combinations ◽  
Genetic Interactions ◽  
Learning Approaches ◽  
Response Modeling ◽  
Single Cell Response

Recent advances in multiplexing single-cell transcriptomics across experiments are enabling the high throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible, so computational methods are needed to predict, interpret and prioritize perturbations. Here, we present the Compositional Perturbation Autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA encodes and learns transcriptional drug response across different cell types, doses, and drug combinations. The model produces easy-to-interpret embeddings for drugs and cell types, allowing drug similarity analysis and predictions for unseen dosages and drug combinations. We show CPA accurately models single-cell perturbations across compounds, dosages, species, and time. We further demonstrate that CPA predicts combinatorial genetic interactions of several types, implying it captures features that distinguish different interaction programs. Finally, we demonstrate CPA allows in-silico generation of 5,329 missing combinations (97.6% of all possibilities) with diverse genetic interactions. We envision our model will facilitate efficient experimental design by enabling in silico response prediction at the single-cell level.

Sign in / Sign up

Export Citation Format

Share Document