Search for: All records

Abstract Motivation Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell membrane mostly relies on the mean-squared displacement plots, much information is lost when producing these plots from the trajectories. Here we employ deep learning to classify breast cancer cell types based on the trajectories of epidermal growth factor receptor (EGFR). Our model is an artificial neural network trained on the EGFR motions acquired from six breast cancer cell lines of varying invasiveness and receptor status: MCF7 (hormone receptor positive), BT474 (HER2-positive), SKBR3 (HER2-positive), MDA-MB-468 (triple negative, TN), MDA-MB-231 (TN) and BT549 (TN). Results The model successfully classified the trajectories within individual cell lines with 83% accuracy and predicted receptor status with 85% accuracy. To further validate the method, epithelial–mesenchymal transition (EMT) was induced in benign MCF10A cells, noninvasive MCF7 cancer cells and highly invasive MDA-MB-231 cancer cells, and EGFR trajectories from these cells were tested. As expected, after EMT induction, both MCF10A and MCF7 cells showed higher rates of classification as TN cells, but not the MDA-MB-231 cells. Whereas deep learning-based cancer cell classifications are primarily based on the optical transmission images of cell morphology and the fluorescence images of cell organelles or cytoskeletal structures, here we demonstrated an alternative way to classify cancer cells using a dynamic, biophysical feature that is readily accessible. Availability and implementation A python implementation of deep learning-based classification can be found at https://github.com/soonwoohong/Deep-learning-for-EGFR-trajectory-classification. Supplementary information Supplementary data are available at Bioinformatics online. 

Abstract Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular environment as the lifetime readings are not biased by fluorophore concentration or excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termedflimGANE(fluorescencelifetimeimaging based onGenerativeAdversarialNetworkEstimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is up to 2,800 times faster than the gold standard time-domain maximum likelihood estimation (TD_MLE) and thatflimGANEprovides a more accurate analysis of low-photon-count histograms in barcode identification, cellular structure visualization, Förster resonance energy transfer characterization, and metabolic state analysis in live cells. With its advantages in speed and reliability,flimGANEis particularly useful in fundamental biological research and clinical applications, where high-speed analysis is critical. 

Invented in 2010, NanoCluster Beacons (NCBs) (1) are an emerging class of turn-on probes that show unprecedented capabilities in single-nucleotide polymorphism (2) and DNA methylation (3) detection. As the activation colors of NCBs can be tuned by a near-by, guanine-rich activator strand, NCBs are versatile, multicolor probes suitable for multiplexed detection at low cost. Whereas a variety of NCB designs have been explored and reported, further diversification and optimization of NCBs require a full scan of the ligand composition space. However, the current methods rely on microarray and multi-well plate selection, which only screen tens to hundreds of activator sequences (4, 5). Here we take advantage of the next-generation-sequencing (NGS) platform for high-throughput, large-scale selection of activator strands. We first generated a ~104 activator sequence library on the Illumina MiSeq chip. Hybridizing this activator sequence library with a common nucleation sequence (which carried a nonfluorescent silver cluster) resulted in hundreds of MiSeq chip images with millions of bright spots (i.e. light-up polonies) of various intensities and colors. With a method termed Chip-Hybridized Associated Mapping Platform (CHAMP) (6), we were able to map these bright spots to the original DNA sequencing map, thus recovering the activator sequence behind each bright spot. After assigning an “activation score” to each “light-up polony”, we used a computational algorithm to select the best activator strands and validate these strands using the traditional in-solution preparation and fluorometer measurement method. By exploring a vast ligand composition space and observing the corresponding activation behaviors of silver clusters, we aim to elucidate the design rules of NCBs.