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1.
J Vis Exp ; (184)2022 06 16.
Article in English | MEDLINE | ID: mdl-35781280

ABSTRACT

Cell-matrix interactions mediate complex physiological processes through biochemical, mechanical, and geometrical cues, influencing pathological changes and therapeutic responses. Accounting for matrix effects earlier in the drug development pipeline is expected to increase the likelihood of clinical success of novel therapeutics. Biomaterial-based strategies recapitulating specific tissue microenvironments in 3D cell culture exist but integrating these with the 2D culture methods primarily used for drug screening has been challenging. Thus, the protocol presented here details the development of methods for 3D culture within miniaturized biomaterial matrices in a multi-well plate format to facilitate integration with existing drug screening pipelines and conventional assays for cell viability. Since the matrix features critical for preserving clinically relevant phenotypes in cultured cells are expected to be highly tissue- and disease-specific, combinatorial screening of matrix parameters will be necessary to identify appropriate conditions for specific applications. The methods described here use a miniaturized culture format to assess cancer cell responses to orthogonal variation of matrix mechanics and ligand presentation. Specifically, this study demonstrates the use of this platform to investigate the effects of matrix parameters on the responses of patient-derived glioblastoma (GBM) cells to chemotherapy.


Subject(s)
Glioblastoma , Hydrogels , Biocompatible Materials/pharmacology , Cell Survival , Cells, Cultured , Glioblastoma/drug therapy , Humans , Hydrogels/pharmacology , Tumor Microenvironment
2.
Lab Chip ; 20(23): 4404-4412, 2020 11 24.
Article in English | MEDLINE | ID: mdl-32808619

ABSTRACT

We report a field-portable and cost-effective imaging flow cytometer that uses deep learning and holography to accurately detect Giardia lamblia cysts in water samples at a volumetric throughput of 100 mL h-1. This flow cytometer uses lens free color holographic imaging to capture and reconstruct phase and intensity images of microscopic objects in a continuously flowing sample, and automatically identifies Giardia lamblia cysts in real-time without the use of any labels or fluorophores. The imaging flow cytometer is housed in an environmentally-sealed enclosure with dimensions of 19 cm × 19 cm × 16 cm and weighs 1.6 kg. We demonstrate that this portable imaging flow cytometer coupled to a laptop computer can detect and quantify, in real-time, low levels of Giardia contamination (e.g., <10 cysts per 50 mL) in both freshwater and seawater samples. The field-portable and label-free nature of this method has the potential to allow rapid and automated screening of drinking water supplies in resource limited settings in order to detect waterborne parasites and monitor the integrity of the filters used for water treatment.


Subject(s)
Cysts , Deep Learning , Giardia lamblia , Holography , Flow Cytometry , Humans
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