Biology subjects

Udgata, S.

Publications and source records attributed to Udgata, S..

2 recordsLinked to original sources

bioRxiv2024

Wide-field optical redox imaging with leading-edge detection for assessment of patient-derived cancer organoids

Patient-derived cancer organoids (PDCOs) are a valuable model to recapitulate human disease in culture with important implications for drug development. However, current methods for assessing PDCOs are limited. Label-free imaging methods are a promising tool to measure organoid level heterogeneity and rapidly screen drug response in PDCOs. The aim of this study was to assess and predict PDCO response to treatments based on mutational profiles using label-free wide-field optical redox imaging (WF ORI). WF ORI provides organoid-level measurements of treatment response without labels or additional reagents by measuring the autofluorescence intensity of the metabolic co-enzymes NAD(P)H and FAD. The optical redox ratio is defined as the fluorescence intensity of [NAD(P)H / NAD(P)H +FAD] which measures the oxidation-reduction state of PDCOs. We have implemented WF ORI and developed novel leading-edge analysis tools to maximize the sensitivity and reproducibility of treatment response measurements in colorectal PDCOs. Leading-edge analysis improves sensitivity to redox changes in treated PDCOs (G{Delta} = 1.462 vs G{Delta} = 1.233). Additionally, WF ORI resolves FOLFOX treatment effects across all PDCOs better than two-photon ORI, with [~]7X increase in effect size (G{Delta} = 1.462 vs G{Delta} = 0.189). WF ORI distinguishes metabolic differences based on driver mutations in CRC PDCOs identifying KRAS+PIK3CA double mutant PDCOs vs wildtype PDCOs with 80% accuracy and can identify treatment resistant mutations in mixed PDCO cultures (G{Delta} = 1.39). Overall, WF ORI enables rapid, sensitive, and reproducible measurements of treatment response and heterogeneity in colorectal PDCOs that will impact patient management, clinical trials, and preclinical drug development. Statement of SignificanceLabel-free wide-field optical redox imaging of patient-derived cancer organoids enables rapid, sensitive, and reproducible measurements of treatment response and heterogeneity that will impact patient management, clinical trials, and preclinical drug development.

bioengineering↗

bioRxiv2024

Cellpose as a reliable method for single-cell segmentation of autofluorescence microscopy images

Autofluorescence microscopy uses intrinsic sources of molecular contrast to provide cellular-level information without extrinsic labels. However, traditional cell segmentation tools are often optimized for high signal-to-noise ratio (SNR) images, such as fluorescently labeled cells, and unsurprisingly perform poorly on low SNR autofluorescence images. Therefore, new cell segmentation tools are needed for autofluorescence microscopy. Cellpose is a deep learning network that is generalizable across diverse cell microscopy images and automatically segments single cells to improve throughput and reduce inter-human biases. This study aims to validate Cellpose for autofluorescence imaging, specifically from multiphoton intensity images of NAD(P)H. Manually segmented nuclear masks of NAD(P)H images were used to train new Cellpose models. These models were applied to PANC-1 cells treated with metabolic inhibitors and patient-derived cancer organoids (across 9 patients) treated with chemotherapies. These datasets include co-registered fluorescence lifetime imaging microscopy (FLIM) of NAD(P)H and FAD, so fluorescence decay parameters and the optical redox ratio (ORR) were compared between masks generated by the new Cellpose model and manual segmentation. The Dice score between repeated manually segmented masks was significantly lower than that of repeated Cellpose masks (p<0.0001) indicating greater reproducibility between Cellpose masks. There was also a high correlation (R2>0.9) between Cellpose and manually segmented masks for the ORR, mean NAD(P)H lifetime, and mean FAD lifetime across 2D and 3D cell culture treatment conditions. Masks generated from Cellpose and manual segmentation also maintain similar means, variances, and effect sizes between treatments for the ORR and FLIM parameters. Overall, Cellpose provides a fast, reliable, reproducible, and accurate method to segment single cells in autofluorescence microscopy images such that functional changes in cells are accurately captured in both 2D and 3D culture.

bioengineering↗