Scientific Publications

Authors: Sheng-Chieh Hsu; Nian-Ping Wu; Yi-Ching Lu; Yunn-Hwa Ma

https://doi.org/10.3390/pharmaceutics14081523, 2022

Published in: Pharmaceutics

Tags: magnetic nanoparticles; endocytosis; epigallocatechin gallate; 67LR; Akt

Authors: A.A. Zhikhoreva, A.V. Belashov, T.N. Belyaeva, A.V. Salova, I.K. Litvinov, E.S. Kornilova, I.V. Semenova, O.S. Vasyutinskii

https://doi.org/10.1016/j.pdpdt.2022.102973, 2022

Published in: Photodiagnosis and Photodynamic Therapy

Tags: photodynamic treatment, malignant cells, fluorescent analysis, digital holographic microscopy, phase images

Authors: Johanna M. Buschhaus, Shrila Rajendran, Brock A. Humphries, Alyssa C. Cutter, Ayşe J. Muñiz, Nicholas G. Ciavattone, Alexander M. Buschhaus, Tatiana Cañeque, Zeribe C. Nwosu, Debashis Sahoo, Avinash S. Bevoor, Yatrik M. Shah, Costas A. Lyssiotis, Pradipta Ghosh, Max S. Wicha, Raphaël Rodriguez, Gary D. Luker

https://doi.org/10.1038/s41388-022-02385-9, 2022

Published in: Oncogene

Tags: breast cancer, stem cells, iron, lysosome, drug resistance, estrogen-targeted therapy

Authors: Yuqi Wang, Lingpu Zhang, Yan Liu, Linlin Tang, Juan He, Xiaqing Sun, Muhsin H. Younis, Daxiang Cui, Haihua Xiao, Dong Gao, Xiang-Yang Kong, Weibo Cai, Jie Song

https://doi.org/10.1002/adhm.202201178, 2022

Published in: Advanced Healthcare Materials

Tags: macrophages; antitumor; drug delivery; immunology; immunotherapy; nanomedicine

In this application note, we showcase some of the potential uses of the Smart Lipid Droplet Assay (SLDA) including quantifying signs of aging in human skin cells (case study 1); investigating LD dynamics during foam macrophage formation (case study 2); examining LD dry mass dynamics in cancer cells (case study 3), and dissecting the details of LD biogenesis (case study 4).

In this Technical Note, we introduce the Smart Lipid Droplet Assay (SLDA) and showcase its output in multiple cell types at varying confluences and across different field of view sizes. We then compare the performance of the SLDA against the fluorescence marker LipidSpot 610, before finishing with a case study where we use the SLDA to quantify how oleic acid (OA) addition changes LD dynamics in pre-adipocyte cells.

In this document, we demonstrate how the CX-A can be used in drug screening. We go through the whole process from image acquisition to data analysis and interpretation. As a case study, we chose to test what extent the art of observing influences the outcome of our experiments; something too often ignored when running live cell experiments. We measured the effects on cell health of four different cell treatments (1) label-free imaging and vehicle; (2) label-free imaging and addition of Mitotracker; (3) fluorescence imaging at low intensity regime and addition of Mitotracker; (4) fluorescence imaging at high intensity regime and addition of Mitotracker, using EA.

This technical note explores these challenges, and presents Nanolive’s cutting edge, AI-assisted solution, the Live T Cell Assay. It also includes a full use case study where the Live T Cell Assay is used to quantify the effect a bispecific antibody has on T cell and cancer cell dynamics.

Nanolive’s label-free technology makes it possible to image cells for long periods of time, at high temporal resolution. The quantity and complexity of the images generated allows us to visualize biological processes in unprecedented detail, but also magnifies the challenges associated with image analysis. Manual image registration and analysis is impossible and so computer-aided processing must be used to harness data complexity. In this technical note, we introduce the key elements involved in cell segmentation, which are essential to understand the novelty of EVE Analytics (EA), Nanolive’s software solution for quantitative cell analysis. We then evaluate the performance of EA segmentation against fluorescence-based segmentation and compare how metrics produced by both approaches differ.

The non-invasive nature of label-free imaging means cells can be continuously monitored, over infinite periods of time, which means kinetic EC50 values can also be calculated. Time-dependent EC50 values provide information about drug stability; whether a drug’s potency increases or decreases over time. In this application note we show how to calculate kinetic EC50 values from data directly output from Nanolive’s image analysis software, EVE Analytics.