Effects and avoidance of photoconversion-induced artifacts in confocal and STED microscopy.

Fluorescence microscopy is limited by photoconversion due to continuous illumination, which results in not only photobleaching but also conversion of fluorescent molecules into species of different spectral properties through photoblueing. Here, we determined different fluorescence parameters of photoconverted products for various fluorophores under standard confocal and stimulated emission depletion (STED) microscopy conditions. We observed changes in both fluorescence spectra and lifetimes that can cause artifacts in quantitative measurements, which can be avoided by using exchangeable dyes.

Estrogen receptor activation remodels TEAD1 gene expression to alleviate hepatic steatosis.

Sex-based differences in obesity-related hepatic malignancies suggest the protective roles of estrogen. Using a preclinical model, we dissected estrogen receptor (ER) isoform-driven molecular responses in high-fat diet (HFD)-induced liver diseases of male and female mice treated with or without an estrogen agonist by integrating liver multi-omics data. We found that selective ER activation recovers HFD-induced molecular and physiological liver phenotypes. HFD and systemic ER activation altered core liver pathways, beyond lipid metabolism, that are consistent between mice and primates. By including patient cohort data, we uncovered that ER-regulated enhancers govern central regulatory and metabolic genes with clinical significance in metabolic dysfunction-associated steatotic liver disease (MASLD) patients, including the transcription factor TEAD1. TEAD1 expression increased in MASLD patients, and its downregulation by short interfering RNA reduced intracellular lipid content. Subsequent TEAD small molecule inhibition improved steatosis in primary human hepatocyte spheroids by suppressing lipogenic pathways. Thus, TEAD1 emerged as a new therapeutic candidate whose inhibition ameliorates hepatic steatosis.

Neural network informed photon filtering reduces fluorescence correlation spectroscopy artifacts.

Fluorescence correlation spectroscopy (FCS) techniques are well-established tools to investigate molecular dynamics in confocal and super-resolution microscopy. In practice, users often need to handle a variety of sample- or hardware-related artifacts, an example being peak artifacts created by bright, slow-moving clusters. Approaches to address peak artifacts exist, but measurements suffering from severe artifacts are typically nonanalyzable. Here, we trained a one-dimensional U-Net to automatically identify peak artifacts in fluorescence time series and then analyzed the purified, nonartifactual fluctuations by time-series editing. We show that, in samples with peak artifacts, the transit time and particle number distributions can be restored in simulations and validated the approach in two independent biological experiments. We propose that it is adaptable for other FCS artifacts, such as detector dropout, membrane movement, or photobleaching. In conclusion, this simulation-based, automated, open-source pipeline makes measurements analyzable that previously had to be discarded and extends every FCS user's experimental toolbox.