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Lcachn 20xipc

Manufactured by Olympus

The LCACHN 20XIPC is a high-performance liquid chromatography (HPLC) system designed for laboratory use. It features a 20X integrated programmable controller (IPC) that allows for precise control and automation of the chromatographic process. The core function of this product is to facilitate the separation, identification, and quantification of various chemical compounds in a sample.

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2 protocols using lcachn 20xipc

1

Quantitative Immunofluorescence Imaging of MSCs

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All stained MSC samples were imaged using an inverted microscope (Etaluma LS720, Lumaview 720/600-Series software) with a 20 × phase contrast objective (Olympus, LCACHN 20XIPC) that allowed the acquisition of both phase contrast and fluorescence images. Approximately 600–900 images for each channel (i.e., phase contrast, 405 nm, 488 nm, and 594 nm) were obtained with a field of view 380μ×380μ m. To conduct the time-lapse experiment, the same microscope and software was used in an incubator to image MSCs every 2 min over a period of 48 h (Videos S1, S2). While all of our selected antibodies have been previously validated, we also examined the non-specific binding by measuring the fluorescent intensity in samples that were only stained with secondary antibodies. Prior to further analyses, the background of the fluorescent data was evaluated and subtracted, following typical quantitative immunofluorescent microscopy procedures.
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2

Single-cell Imaging and AI Prediction

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All stained samples were imaged with an inverted fluorescent microscope (Etaluma LS720, Lumaview 720/600-series software) using a 20× objective (Olympus, LCACHN 20 XIPC). For each imaging channel (i.e., phase-contrast, 405 nm, 488 nm, and 597 nm), approximately 600 images were acquired with a field of view 0.38 mm × 0.38 mm. To perform the single-cell measurement, polygon selection tool in Fiji ImageJ was used to outline the single cells of immunofluorescence and the AI-predicted images. The cells were outlined manually to ensure the accuracy of outlining. For each tested condition, we analyzed 30 cells. Overall pixel-intensity is then used to analyze and evaluate prediction accuracy of the AI model.
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