While guided human cortical organoid (hCO) protocols reproducibly generate cortical cell types at one site, variability in hCO phenotypes across sites using a harmonized protocol has not yet been evaluated. To determine the cross-site reproducibility of hCO differentiation, three independent research groups assayed hCOs in multiple differentiation replicates from one induced pluripotent stem cell (iPSC) line using a harmonized miniaturized spinning bioreactor protocol across 3 months. hCOs were mostly cortical progenitor and neuronal cell types in reproducible proportions that were consistently organized in cortical wall-like buds. Cross-site differences were detected in hCO size and expression of metabolism and cellular stress genes. Variability in hCO phenotypes correlated with stem cell gene expression prior to differentiation and technical factors associated with seeding, suggesting iPSC quality and treatment are important for differentiation outcomes. Cross-site reproducibility of hCO cell type proportions and organization encourages future prospective meta-analytic studies modeling neurodevelopmental disorders in hCOs.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11411306PMC
http://dx.doi.org/10.1016/j.stemcr.2024.07.008DOI Listing

Publication Analysis

Top Keywords

cross-site reproducibility
12
human cortical
8
cell type
8
cell types
8
variability hco
8
hco phenotypes
8
reproducibility hco
8
stem cell
8
cell
6
hco
6

Similar Publications

Article Synopsis
  • Neuroscience researchers are leveraging Big Data to improve the reliability of findings by increasing sample sizes and addressing replication issues.
  • A study analyzed data from 53 studies with over 10,500 participants to connect scores from various auditory verbal learning tasks (AVLTs) while controlling for site-related effects.
  • The research successfully reduced score variance by 37% and developed an online tool to help researchers and clinicians convert memory scores across different tests, highlighting the benefit of global data harmonization in behavioral sciences.
View Article and Find Full Text PDF
Article Synopsis
  • This study evaluated an automated system for segmenting breast cancers in MRI scans and compared its effectiveness to that of radiologists across multiple clinical sites.
  • A 3D U-Net model was trained on a substantial dataset and validated against test data from different sites, showing similar performance between the AI and radiologists.
  • The findings indicate that the AI can match radiologists' segmentation accuracy and the code and model weights are shared publicly to encourage reproducibility in radiology AI research.
View Article and Find Full Text PDF

While guided human cortical organoid (hCO) protocols reproducibly generate cortical cell types at one site, variability in hCO phenotypes across sites using a harmonized protocol has not yet been evaluated. To determine the cross-site reproducibility of hCO differentiation, three independent research groups assayed hCOs in multiple differentiation replicates from one induced pluripotent stem cell (iPSC) line using a harmonized miniaturized spinning bioreactor protocol across 3 months. hCOs were mostly cortical progenitor and neuronal cell types in reproducible proportions that were consistently organized in cortical wall-like buds.

View Article and Find Full Text PDF

Three-dimensional simultaneous T1 and T2* relaxation times and quantitative susceptibility mapping at 3 T: A multicenter validation study.

Magn Reson Imaging

October 2024

Department of Radiology, Juntendo University, 1-2-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; Department of Health Data Science, Faculty of Health Data Science, Juntendo University, 6-8-1 Hinode, Urayasu, Chiba 279-0013, Japan.

Article Synopsis
  • The study assessed the repeatability of T1 and T2* relaxation times and quantitative susceptibility (χ) values using quantitative parameter mapping (QPM) across three different 3T MRI scanners at three sites.
  • Twelve healthy volunteers underwent three separate scans at each site, and various statistical analyses were used to measure consistency and variation.
  • Results showed high intra-site repeatability for all measured values (T1, T2*, and χ) and acceptable cross-site reproducibility, suggesting QPM can reliably support multisite studies in MRI research.
View Article and Find Full Text PDF

Background: Although electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed.

Methods: Patients with lung cancer, as well as healthy control and diseased control groups, were prospectively recruited from two referral centers between 2019 and 2022. Deep learning models for detecting lung cancer with eNose breathprint were developed using training cohort from one site and then tested on cohort from the other site.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!