Fluorescence live-cell microscopy is one of the most frequently used techniques to study dynamic processes in organoids. However, it is often limited by laborious fluorescent reporter engineering, limited numbers of fluorescence channels, and adverse phototoxicity and protein overexpression effects. Label-free imaging is a promising alternative but not yet established for 3D cultures. Here, we introduce LabelFreeTracker, a label-free machine-learning-based method to visualize the nuclei and membranes in bright-field images of 3D mouse intestinal organoids. The approach uses U-Net neural networks trained on the bright-field transmitted light and fluorescence images of mouse intestinal organoids as obtained by standard confocal microscopy. LabelFreeTracker frees up fluorescence channels to study fluorescent reporters and allows (semi-)automated quantification of cell movement, cell shape and volume changes, proliferation, differentiation, and lineage trees. This method greatly simplifies live-cell imaging of tissue dynamics and will accelerate screening of patient-derived organoids, for which reporter engineering is not feasible.@en

The ability to detect specific nucleic acid sequences allows for a wide range of applications such as the identification of pathogens, clinical diagnostics, and genotyping. CRISPR-Cas proteins Cas12a and Cas13a are RNA-guided endonucleases that bind and cleave specific DNA and RNA sequences, respectively. After recognition of a target sequence, both enzymes activate indiscriminate nucleic acid cleavage, which has been exploited for sequence-specific molecular diagnostics of nucleic acids. Here, we present a label-free detection approach that uses a readout based on solution turbidity caused by liquid-liquid phase separation (LLPS). Our approach relies on the fact that the LLPS of oppositely charged polymers requires polymers to be longer than a critical length. This length dependence is predicted by the Voorn-Overbeek model, which we describe in detail and validate experimentally in mixtures of polynucleotides and polycations. We show that the turbidity resulting from LLPS can be used to detect the presence of specific nucleic acid sequences by employing the programmable CRISPR-nucleases Cas12a and Cas13a. Because LLPS of polynucleotides and polycations causes solutions to become turbid, the detection of specific nucleic acid sequences can be observed with the naked eye. We furthermore demonstrate that there is an optimal polynucleotide concentration for detection. Finally, we provide a theoretical prediction that hints towards possible improvements of an LLPS-based detection assay. The deployment of LLPS complements CRISPR-based molecular diagnostic applications and facilitates easy and low-cost nucleotide sequence detection.

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