@article {69848,
	title = {A Bayesian Model to Count the Number of Two-State Emitters in a Diffraction Limited Spot.},
	journal = {Nano Lett},
	year = {2025},
	month = {2025 Apr 04},
	abstract = {<p>We address the problem of inferring the number of independently blinking fluorescent light emitters, when only their combined intensity contributions can be observed. This problem occurs regularly in light microscopy of objects smaller than the diffraction limit, where one wishes to count the number of fluorescently labeled subunits. Our proposed solution directly models the photophysics of the system, as well as the blinking kinetics of the fluorescent emitters as a fully differentiable hidden Markov model, estimating a posterior distribution of the total number of emitters. We show that our model is more accurate and increases the range of countable subunits by a factor of 2 compared to current state-of-the-art methods. Furthermore, we demonstrate that our model can be used to investigate the effect of blinking kinetics on counting ability and therefore can inform optimal experimental conditions.</p>
},
	issn = {1530-6992},
	doi = {10.1021/acs.nanolett.4c06304},
	url = {https://doi.org/10.1021/acs.nanolett.4c06304},
	author = {Hillsley, Alexander and Stein, Johannes and Tillberg, Paul W and Stern, David L and Funke, Jan}
}
@article {69694,
	title = {Spatial single-cell Organellomics reveals nutrient dependent hepatocyte heterogeneity and predicts pathophysiological status in vivo},
	journal = {bioRxiv},
	year = {2024},
	month = {2024 Dec 08},
	abstract = {<p>Cellular heterogeneity within complex tissues and organs is essential to coordinate biological processes across biological scales. The effect of local cues and tissue microenvironments on cell heterogeneity has been mainly studied at the transcriptional level. However, it is within the subcellular scale - the organelles - that lays the machinery to conduct most metabolic reactions and maintain cells alive, ensuring proper tissue function. How changes in subcellular organization under different microenvironments define the functional diversity of cells within organs remains largely unexplored. Here we determine how organelles adapt to different microenvironments using the mouse liver as model system, in combination with computational approaches and machine-learning. To understand organelle adaptation in response to changing nutritional conditions, we analyzed 3D fluorescent microscopy volumes of liver samples labeled to simultaneously visualize mitochondria, peroxisomes, and lipid droplets from mice subjected to different diets: a control diet, a high-fat diet, and a control diet plus fasting. A Cellpose based pipeline was implemented for cell and organelle segmentation, which allowed us to measure 100 different organelle metrics and helped us define subcellular architectures in liver samples at the single cell level. Our results showed that hepatocytes display distinct subcellular architectures within different regions of the liver-close to the central vein, in the middle region, and near the portal vein- and across the various diet groups, thus reflecting their adaptation to specific nutritional inputs. Principal component analysis and clustering of hepatocytes based on organelle signatures revealed 12 different hepatocyte categories within the different experimental groups, highlighting a reduction in hepatocyte heterogeneity under nutritional perturbations. Finally, using single cell organelle signatures exclusively, we generated machine learning models that were able to predict with high accuracy different hepatocyte categories, diet groups, and the stages of MASLD. Our results demonstrate how organelle signatures can be used as hallmarks to define hepatocyte heterogeneity and their adaptation to different nutritional conditions. In the future, our strategy, which combines subcellular resolution imaging of liver volumes and machine learning, could help establish protocols to better define and predict liver disease progression.</p>
},
	doi = {10.1101/2024.12.06.627285},
	url = {https://doi.org/10.1101/2024.12.06.627285},
	author = {Hillsley, Alexander and Adhikari, Raghabendra and Johnson, Alana Dowdell and Espinosa-Medina, Isabel and Funke, Jan and Feliciano, Daniel}
}
