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Bead-Jet printing of high-density mesenchymal stem cell-laden Matrigel beads augments skeletal muscle and hair follicle regeneration [MSC]

ABSTRACT: Bead-Jet printing of high-density mesenchymal stem cell-laden Matrigel beads augments skeletal muscle and hair follicle regeneration [MSC]

PROVIDER: PRJNA810031 | ENA |

REPOSITORIES: ENA

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Bead-Jet printing of high-density mesenchymal stem cell-laden Matrigel beads augments skeletal muscle and hair follicle regeneration

Project description:Bead-Jet printing of high-density mesenchymal stem cell-laden Matrigel beads augments skeletal muscle and hair follicle regeneration

| PRJNA810023 | ENA

Bead-Jet printing of high-density mesenchymal stem cell-laden Matrigel beads augments skeletal muscle and hair follicle regeneration [skeletal muscle]

Project description:Bead-Jet printing of high-density mesenchymal stem cell-laden Matrigel beads augments skeletal muscle and hair follicle regeneration [skeletal muscle]

| PRJNA810028 | ENA

Bead-Jet printing of high-density mesenchymal stem cell-laden Matrigel beads augments skeletal muscle and hair follicle regeneration [MSC]

Project description:We reported high-density encapsulation of MSCs in Matrigel beads, augmenting MSCs function, including increasing MSCs proliferation, migration, and extracellular vesicles (EVs) production via elevated PI3K-AKT pathway activity.

2022-02-28 | GSE197355 | GEO

Bead-Jet printing of high-density mesenchymal stem cell-laden Matrigel beads augments skeletal muscle and hair follicle regeneration [skeletal muscle]

Project description:We reported high-density encapsulation of MSCs in Matrigel beads, distributed in sparse pattern, augmenting mouse volumetric skeletal muscle tissue with reduced fibrosis.

2022-02-28 | GSE197356 | GEO

Mesenchymal stem cell-laden matrigel microspheres printing enables large skin wound regeneration

Project description:High cell density Matrigel beads (HDB) were printinto onto large skin wounds (10 mm and 15 mm). Compared with acellular beads printing, HDB group could achieve more efficient injury regeneration.

2022-10-12 | GSE214868 | GEO

Bead-Jet printing of high-density mesenchymal stem cell-laden Matrigel beads augments skeletal muscle and hair follicle regeneration

Project description:This SuperSeries is composed of the SubSeries listed below.

2022-02-28 | GSE197358 | GEO

Comparison of small RNA Profiles of extracellular vesicles (EVs) derived from three mesenchymal stem cell-laden Matrigel composites

Project description:To investigate the difference of small RNA profiles of EVs derived from mesenchymal stem cell-laden Matrigel composites: high cell density beads (HDB) , low cell density beads (LDB) , and high cell density bulk ge (HDG)

2022-10-17 | GSE215294 | GEO

Scalable Microfluidics for Single Cell RNA Printing and Sequencing

Project description:Single cell transcriptomics has emerged as a powerful approach to dissecting phenotypic heterogeneity in complex, unsynchronized cellular populations. However, many important biological questions demand quantitative analysis of large numbers of individual cells. Hence, new tools are urgently needed for efficient, inexpensive, and parallel manipulation of RNA from individual cells. We report a simple microfluidic platform for trapping single cell lysates in sealed, picoliter microwells capable of “printing” RNA on glass or capturing RNA on polymer beads. To demonstrate the utility of our system for single cell transcriptomics, we developed a highly scalable technology for genome-wide, single cell RNA-Seq. The current implementation of our device is pipette-operated, profiles hundreds of individual cells in parallel with library preparation costs of ~$0.10-$0.20/cell, and includes five lanes for simultaneous experiments. We anticipate that this system will ultimately serve as a general platform for large-scale single cell transcriptomics, compatible with both imaging and sequencing readouts.!Series_type = Expression profiling by high throughput sequencing A microfluidic device that pairs sequence-barcoded mRNA capture beads with individual cells was used to barcode cDNA from individual cells which was then pre-amplified by in vitro transcription in a pool and converted into an Illumina RNA-Seq library. Libraries were generated from ~600 individual cells in parallel and extensive analysis was done on 396 cells from the U87 and MCF10a cell lines and from ~500 individual cells with extensive analysis on 247 cells from the U87 and WI-38 cell lines. Sequencing was done on the 3'-end of the transcript molecules. The first read contains cell-identifying barcodes that were present on the capture bead and the second read contains a unique molecular identifier (UMI) barcode, a lane-identifying barcode, and then the sequence of the transcript.

2015-05-21 | E-GEOD-66357 | biostudies-arrayexpress

Optocoder: computational decoding of spatially indexed bead arrays

Project description:Spatial transcriptomics technologies that can quantify gene expression in space are transforming contemporary biology research. Some of such methods use spatially barcoded bead arrays that are optically sequenced by a microscopy setup to detect bead barcodes in space which can be consecutively matched to cell barcodes from the respective single cell sequencing experiment. To have good quality barcodes and a high number of barcode matches in space, robust and efficient computational pipelines are needed to process raw microscopy images and call the bases of bead barcodes accurately. Here, we present Optocoder, a computational pipeline that takes raw optical sequencing microscopy images as input and outputs bead barcodes in space. Optocoder efficiently aligns images, detects beads, and corrects for confounding factors of the fluorescence signal such as crosstalk and phasing before base calling. Furthermore, we implement a machine learning pipeline that is trained using the signal from the beads that match to illumina barcodes in order to predict non-matching bead barcodes which can boost up the number of barcode matches. We benchmark Optocoder using data from an in-house spatial transcriptomics platform as well as data from the Slide-seq method and we show that it can efficiently process both datasets with minimal modification.

2022-06-24 | GSE193472 | GEO

Scalable Microfluidics for Single Cell RNA Printing and Sequencing

2015-05-21 | GSE66357 | GEO

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