time lapse microscopy  (Olympus)

Journal: bioRxiv

Article Title: High-content live-cell time-lapse imaging predicts cells about to die via apoptosis

doi: 10.1101/2025.10.23.684203

Figure Lengend Snippet: (A) The platemap showing replicates of ten different doses of staurosporine, an apoptosis-inducing compound. We imaged each well at four fields of view (FOVs) at three z slices, each 3um apart. Images were acquired every 30 minutes for 360 minutes (six hours), capturing Hoechst, ChromaLIVE 488_yellow, ChromaLIVE 488_red, and ChromaLIVE 561. We fixed cells at six hours and stained for AnnexinV, an apoptotic marker, and Hoechst. (B) Our HCLTI pipeline for processing time-lapse microscopy images. (C) Representative images of tracked HeLa single cells across time for varying doses of staurosporine. Cyan represents nuclei, yellow represents ChromaLive 561, and Magenta represents ChromaLive 488 at both its emission channels for visualization purposes only. Scale bars = 10 um.

Article Snippet: – We then applied the Live Cell Painting assay (ChromaLIVE TM ) , and performed live-cell time-lapse spinning disk confocal microscopy (Yokogawa CQ1), acquiring images per field of view (FOV) every 30 minutes in three z stacks for six hours.

Techniques: Staining, Marker, Time-lapse Microscopy

Journal: bioRxiv

Article Title: High-content live-cell time-lapse imaging predicts cells about to die via apoptosis

doi: 10.1101/2025.10.23.684203

Figure Lengend Snippet:

Article Snippet: – We then applied the Live Cell Painting assay (ChromaLIVE TM ) , and performed live-cell time-lapse spinning disk confocal microscopy (Yokogawa CQ1), acquiring images per field of view (FOV) every 30 minutes in three z stacks for six hours.

Techniques: Imaging, Staining

Journal: bioRxiv

Article Title: Data-driven simulations elucidate how lymphocyte motility behaviors drive cell-cell interactions within germinal centers

doi: 10.1101/2025.08.05.668700

Figure Lengend Snippet: From the intravital microscopy data, we segmented and tracked 3D trajectories for individual GCBs and Tfhs. Each trajectory comprised the (x,y,z) coordinates of a single cell in 20 consecutive frames acquired at 30-second intervals. For each experimental trajectory, we decomposed the trajectory into primary, secondary, and tertiary axes of movement. For the 3D trajectory, we quantified statistical features of the angular distribution. For the 3D trajectory and the trajectory decomposition, we quantified the following features: net distance and progressivity; statistical features of the displacement distribution; and the mean squared displacement at intervals of one, two, and three frames (30 seconds, 1-minute, and 1.5-minutes, respectively). After extracting these features, we projected the multi-dimensional feature space into a UMAP embedding and applied unsupervised clustering to identify motility behaviors. We then examined the behavior of each motility cluster. Finally, we eliminated possible outlier trajectories, and repeated step on the cleaned dataset.

Article Snippet: To extract single-cell trajectories from each intravital time-lapse microscopy image set, IMARIS software was used to segment cells and track individual trajectories.

Techniques: Intravital Microscopy

Journal: bioRxiv

Article Title: Data-driven simulations elucidate how lymphocyte motility behaviors drive cell-cell interactions within germinal centers

doi: 10.1101/2025.08.05.668700

Figure Lengend Snippet: A , Bar plot representing fractional volume of segmented and tracked cell types within each intravital microscopy experiment. Segmented cells refer to cells that were tracked for 5 continuous frames. Tracked cells refer to cells that were tracked for 20 continuous frames. B, Measured GCB-Tfh interactions in each intravital microscopy experiment. We measured interactions by processing the metric of volumetric overlap ratio between each tracked cell and any labelled cell type (which could have also been tracked for 20 frames or segmented for a minimum of 5 frames, see Methods ). C, Scatter plot of simulation predictions against experimental measurements for total Tfh interactions per GCB. Vertical errorbars represent the standard deviation from 100 simulation repetitions. Line indicates a linear regression with the intercept fixed at zero. D, Scatter plots of simulation predictions against experimental measurements for total GCB interactions per Tfh. Vertical errorbars represent the standard deviation from 100 simulation repetitions. Line indicates a linear regression with the intercept fixed at zero. E, Bar plot representing the log 2 fold change between simulation predictions and experimental measurements for total Tfh interactions per GCB (blue) or total Tfh interactions per GCB (red). ‘RMSE’: root mean squared error between average simulation predictions and experimental measurements, ‘r’: r-value for Spearman correlation, ‘p’: p -value for Spearman correlation.

Article Snippet: To extract single-cell trajectories from each intravital time-lapse microscopy image set, IMARIS software was used to segment cells and track individual trajectories.

Techniques: Intravital Microscopy, Standard Deviation