Dataset Information

Spectral reading of optical resonance-encoded cells in microfluidics.

ABSTRACT: The ability to label individual cells is useful for single-cell-level studies of complex cellular interactions and heterogeneity. Optically readable cell labeling is attractive as it can be investigated non-invasively and repeatedly at high speeds. Here, we demonstrate the feasibility of large-scale cell barcoding and identification using fluorescent polystyrene microbeads loaded into cells. Intracellular beads with different diameters in a range of 5 to 12 ?m generate spectrally distinguished features or barcodes. A microfluidic chip was used to measure fluorescence resonance peaks emitted from individual cells. An algorithm comparing the peak wavelengths to a reference barcode library allowed barcode identification with high accuracy. This work provides a guideline to increase the number of unique identifiers and reduce various false-positive and false-negative errors.

SUBMITTER: Humar M

PROVIDER: S-EPMC5555601 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Spectral reading of optical resonance-encoded cells in microfluidics.

Humar Matjaž M Upadhya Avinash A Yun Seok Hyun SH

Lab on a chip 20170801 16

The ability to label individual cells is useful for single-cell-level studies of complex cellular interactions and heterogeneity. Optically readable cell labeling is attractive as it can be investigated non-invasively and repeatedly at high speeds. Here, we demonstrate the feasibility of large-scale cell barcoding and identification using fluorescent polystyrene microbeads loaded into cells. Intracellular beads with different diameters in a range of 5 to 12 μm generate spectrally distinguished f ...[more]

PMID: 28686280

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Project description:PurposeTo explore, at a high field strength of 7T, the performance of various fat spectral models on the quantification of triglyceride composition and proton density fat fraction (PDFF) using chemical-shift encoded MRI (CSE-MRI).MethodsMR data was acquired from CSE-MRI experiments for various fatty materials, including oil and butter samples and in vivo brown and white adipose mouse tissues. Triglyceride composition and PDFF were estimated using various a priori 6- or 9-peak fat spectral models. To serve as references, NMR spectroscopy experiments were conducted to obtain material specific fat spectral models and triglyceride composition estimates for the same fatty materials. Results obtained using the spectroscopy derived material specific models were compared to results obtained using various published fat spectral models.ResultsUsing a 6-peak fat spectral model to quantify triglyceride composition may lead to large biases at high field strengths. When using a 9-peak model, triglyceride composition estimations vary greatly depending on the relative amplitudes of the chosen a priori spectral model, while PDFF estimations show small variations across spectral models. Material specific spectroscopy derived spectral models produce estimations that better correlate with NMR spectroscopy estimations in comparison to those obtained using non-material specific models.ConclusionAt a high field strength of 7T, a material specific 9-peak fat spectral model, opposed to a widely accepted or generic human liver model, is necessary to accurately quantify triglyceride composition when using CSE-MRI estimation methods that assume the spectral model to be known as a priori information. CSE-MRI allows for the quantification of the spatial distribution of triglyceride composition for certain in vivo applications. Additionally, PDFF quantification is shown to be independent of the chosen a priori spectral model, which agrees with previously reported results obtained at lower field strengths (e.g. 3T).

Dataset Information

Spectral reading of optical resonance-encoded cells in microfluidics.

Publications

Spectral reading of optical resonance-encoded cells in microfluidics.

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