@article{TEXTUAL,
      recid = {11007},
      author = {Patel, Parthiv and Drayman, Nir and Liu, Ping and Bilgic,  Mustafa and Tay, Savaş},
      title = {Computer vision reveals hidden variables underlying NF-ΚB  activation in single cells},
      journal = {Science Advances},
      address = {2021-10-22},
      number = {TEXTUAL},
      abstract = {Individual cells are heterogeneous when responding to  environmental cues. Under an external signal, certain cells  activate gene regulatory pathways, while others completely  ignore that signal. Mechanisms underlying cellular  heterogeneity are often inaccessible because experiments  needed to study molecular states destroy the very states  that we need to examine. Here, we developed an image-based  support vector machine learning model to uncover variables  controlling activation of the immune pathway nuclear factor  ΚB (NF-ΚB). Computer vision analysis predicts the identity  of cells that will respond to cytokine stimulation and  shows that activation is predetermined by minute amounts of  “leaky” NF-ΚB (p65:p50) localization to the nucleus.  Mechanistic modeling revealed that the ratio of NF-ΚB to  inhibitor of NF-ΚB predetermines leakiness and activation  probability of cells. While cells transition between  molecular states, they maintain their overall probabilities  for NF-ΚB activation. Our results demonstrate how computer  vision can find mechanisms behind heterogeneous single-cell  activation under proinflammatory stimuli.},
      url = {http://knowledge.uchicago.edu/record/11007},
}