<b>Endothelial cells differentiated from hiPSCs form aligned network structures in engineered neural tissue</b>

DOI

The data that support the findings of Endothelial cells differentiated from hiPSCs form aligned network structures in engineered neural tissuehiPSCs were differentiated to endothelial cells following the protocol developed by Hildebrandt et al., 2021 (https://pubmed.ncbi.nlm.nih.gov/33410098/) with the modification of culturing the hiPSC-derived cells on Geltrex-coated plates. Immunofluorescence data for differentiation monitoring and the resulting cells characterisation were collected using Opera Phenix confocal spinning disc microscope and analysed with Columbus Image Analysis Software v2.9.1. RT-PCR data from differentiation monitoring and resulting cell characterisation were collected using GoScript Reverse Transcriptase Kit (Promega) and Power SYBR Green PCR Master Mix using SimpliAmp Thermal Cycler and QuantStudio3 instrument, analysed by QuantStudio Design and Analysis Software v1.5.1 with hypoxanthine guanine phosphoribosyl transferase (HPRT1), ribosomal protein lateral stalk subunit P0 (RPLP0) and ribosomal protein S18 (RPS18) reference genes. Viability data of hiPSC-derived endothelial cells within engineered neural tissue (EngNT-EC) covers local viability, using ReadyProbes Cell Viability Imaging Kit Blue/Red, and whole construct viability, using Lactase Dehydrogenase (LDH) Assay Kit (Cytotoxicity) collected using a Zeiss LSM710 confocal microscope and a SpectraMAX M5 Multi-Mode Microplate Reader, respectively. Alignment of cells within EngNT-EC was captured by VolocityTM v6.5.1 analysis of 20 µm z-stacks taken using Zeiss LSM710 confocal microscope at predetermined positions along the construct stained with Rhodamine 110 Phalloidin and Hoechst 33342. Viability and alignment assays were performed 24 h after EngNT-EC construct formation. EngNT-EC were cultured for 2 or 4 days to allow for endothelial network formation. These samples were stained with Rhodamine 110 Phalloidin and Hoechst 33342, imaged on Zeiss LSM710 confocal microscope with 40 µm z-stacks taken at predetermined positions and analysed for angiogenic features using ImageJ plugin Angiogenesis Analyzer. EngNT-EC constructs were further analysed for lumen number from 10 µm sections stained with Rhodamine 110 Phalloidin and Hoechst 33342, imaged on Zeiss AxioLab A1 fluorescent microscope. Neurite length and alignment data were collected using ImageJ and VolocityTM v6.5.1, respectively, analysing the co-culture of neurons on the EngNT-EC, cultured for 1 or 4 days, and acellular constructs imaged using Zeiss LSM710 confocal microscope of samples stained with anti-betaIII tubulin and Hoechst 33342. Data collected between October 2022 and May 2024

Identifier
DOI https://doi.org/10.5522/04/27194268.v1
Related Identifier HasPart https://ndownloader.figshare.com/files/49694913
Related Identifier HasPart https://ndownloader.figshare.com/files/49694916
Related Identifier HasPart https://ndownloader.figshare.com/files/49694919
Related Identifier HasPart https://ndownloader.figshare.com/files/49694922
Related Identifier HasPart https://ndownloader.figshare.com/files/49694925
Related Identifier HasPart https://ndownloader.figshare.com/files/49694928
Related Identifier HasPart https://ndownloader.figshare.com/files/49694931
Related Identifier HasPart https://ndownloader.figshare.com/files/49694934
Related Identifier HasPart https://ndownloader.figshare.com/files/49694937
Related Identifier HasPart https://ndownloader.figshare.com/files/49694940
Related Identifier HasPart https://ndownloader.figshare.com/files/49694943
Related Identifier HasPart https://ndownloader.figshare.com/files/49694946
Metadata Access https://api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:figshare.com:article/27194268
Provenance
Creator Smith, Poppy; Phillips, James; Jat, Parmjit
Publisher University College London UCL
Contributor Figshare
Publication Year 2024
Rights https://creativecommons.org/publicdomain/zero/1.0/
OpenAccess true
Contact researchdatarepository(at)ucl.ac.uk
Representation
Language English
Resource Type Dataset
Discipline Construction Engineering and Architecture; Engineering; Engineering Sciences; Life Sciences; Medicine