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1.
Sci Rep ; 11(1): 12921, 2021 06 21.
Artículo en Inglés | MEDLINE | ID: mdl-34155235

RESUMEN

Chimeric antigen receptor (CAR) T cells are engineered cells used in cancer therapy and are studied to treat infectious diseases. Trafficking and persistence of CAR T cells is an important requirement for efficacy to target cancer. Here, we describe a CAR RNA FISH histo-cytometry platform combined with a random reaction seed image analysis algorithm to quantitate spatial distribution and in vivo functional activity of a CAR T cell population at a single cell resolution for preclinical models. In situ, CAR T cell exhibited a heterogenous effector gene expression and this was related to the distance from tumor cells, allowing a quantitative assessment of the potential in vivo effectiveness. The platform offers the potential to study immune functions of genetically engineered cells in situ with their target cells in tissues with high statistical power and thus, can serve as an important tool for preclinical assessment of CAR T cell effectiveness.


Asunto(s)
Heterogeneidad Genética , Hibridación Fluorescente in Situ , ARN/genética , Receptores de Antígenos de Linfocitos T/genética , Receptores Quiméricos de Antígenos/genética , Linfocitos T/metabolismo , Animales , Células Cultivadas , Modelos Animales de Enfermedad , Técnica del Anticuerpo Fluorescente , Humanos , Inmunohistoquímica , Inmunofenotipificación , Inmunoterapia Adoptiva , Hibridación Fluorescente in Situ/métodos , Ratones , Neoplasias/genética , Neoplasias/inmunología , Neoplasias/terapia , Especificidad de Órganos/genética , Reacción en Cadena de la Polimerasa , Receptores de Antígenos de Linfocitos T/metabolismo , Receptores Quiméricos de Antígenos/metabolismo , Linfocitos T/inmunología , Ensayos Antitumor por Modelo de Xenoinjerto
2.
Mol Ther Methods Clin Dev ; 20: 258-275, 2021 Mar 12.
Artículo en Inglés | MEDLINE | ID: mdl-33473359

RESUMEN

Chronic hepatitis B virus (HBV) infection is a major public health problem. New treatment approaches are needed because current treatments do not target covalently closed circular DNA (cccDNA), the template for HBV replication, and rarely clear the virus. We harnessed adeno-associated virus (AAV) vectors and CRISPR-Staphylococcus aureus (Sa)Cas9 to edit the HBV genome in liver-humanized FRG mice chronically infected with HBV and receiving entecavir. Gene editing was detected in livers of five of eight HBV-specific AAV-SaCas9-treated mice, but not control mice, and mice with detectable HBV gene editing showed higher levels of SaCas9 delivery to HBV+ human hepatocytes than those without gene editing. HBV-specific AAV-SaCas9 therapy significantly improved survival of human hepatocytes, showed a trend toward decreasing total liver HBV DNA and cccDNA, and was well tolerated. This work provides evidence for the feasibility and safety of in vivo gene editing for chronic HBV infections, and it suggests that with further optimization, this approach may offer a plausible way to treat or even cure chronic HBV infections.

3.
Sci Rep ; 9(1): 2908, 2019 02 27.
Artículo en Inglés | MEDLINE | ID: mdl-30814668

RESUMEN

Conventional deterministic algorithms (i.e., skeletonization and edge-detection) lack robustness and sensitivity to reliably detect the neurite elongation and branching of low signal-to-noise-ratio microscopy images. Neurite outgrowth experiments produce an enormous number of images that require automated measurement; however, the tracking of neurites is easily lost in the automated process due to the intrinsic variability of neurites (either axon or dendrite) under stimuli. We have developed a stochastic random-reaction-seed (RRS) method to identify neurite elongation and branching accurately and automatically. The random-seeding algorithm of RRS is based on the hidden-Markov-model (HMM) to offer a robust enough way for tracing arbitrary neurite structures, while the reaction-seeding algorithm of RRS secures the efficiency of random seeding. It is noteworthy that RRS is capable of tracing a whole neurite branch by only one initial seed, so that RRS is proficient at quantifying extensive amounts of neurite outgrowth images with noisy background in microfluidic devices of biomedical engineering fields. The method also showed notable performance for reconstructing of net-like structures, and thus is expected to be proficient for biomedical feature extractions in a wide range of applications, such as retinal vessel segmentation and cell membrane profiling in spurious-edge-tissues.


Asunto(s)
Procesamiento de Imagen Asistido por Computador/métodos , Neuritas/fisiología , Enfermedades del Sistema Nervioso Periférico/diagnóstico por imagen , Algoritmos , Automatización de Laboratorios , Humanos , Cadenas de Markov , Microfluídica/tendencias , Microscopía , Proyección Neuronal
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