Biology subjects

Patel, N. K.

Publications and source records attributed to Patel, N. K..

4 recordsLinked to original sources

bioRxiv2025

Early cell cycle genes in cortical organoid progenitors predict interindividual variability in infant brain growth trajectories

Human induced pluripotent stem cell (iPSC) derived cortical organoids (hCOs) model neurogenesis on an individuals genetic background. The degree to which hCO phenotypes recapitulate the brain growth of the participants from which they were derived is not well established. We generated up to 3 iPSC clones from each of 18 participants in the Infant Brain Imaging Study, who have undergone longitudinal brain imaging during infancy. We identified consistent hCO morphology and cortical cell types across clones from the same participant. hCO cross-sectional area and production of cortical hem cells were associated with in vivo cortical growth rates. Cell cycle associated genes expression in early progenitors at the crux of fate decision trajectories were correlated with cortical growth rate from 6-12 months of age, and were enriched in microcephaly and neurodevelopmental disorder genes. Our data suggest the hCOs capture inter-individual variation in cortical cell types influencing infant cortical surface area expansion.

neuroscience↗

bioRxiv2025

Multifunctional bending magnet beamline with a capillary optic for X-ray fluorescence studies of metals in tissue sections

Scanning fluorescence X-ray microscopy lets one non-destructively and quantitatively map the distribution of most biologically-important metals in cells and tissues. For studies on large-scale tissues and organs, a spatial resolution of several micrometers is often sufficient; in this case, bending magnets at synchrotron light sources provide abundant X-ray flux. We describe here the use of bending magnet beamline 8-BM-B at the Advanced Photon Source (APS) with two distinct microscopy stations: a pre-existing one with Kirkpatrick-Baez (KB) mirror optics for slightly higher throughput and the ability to accommodate samples tens of centimeters across, and a new prototype station with an axially-symmetric, single-bounce, capillary optic with slightly less flux, but slightly higher fluence (which affects achievable resolution at low metal concentration) and higher spatial resolution. The KB station provides{delta} res = 10.5 {micro}m spatial resolution at a per-pixel exposure time of tdwell = 100 ms and a fluence per time of 5.8x 107 photons /({micro}m2 {middle dot}s), while the prototype capillary station provides{delta} res = 6.3 {micro}m at tdwell = 50 ms and a fluence per time of 6.1x 107 photons ({micro}m2 {middle dot}s). We used image power spectral density to estimate the achieved spatial resolution{delta} res from individually acquired images, with{delta} res depending-on the optic, the fluorescence signal strength of the sample being imaged, and the method used to process raw fluorescence spectral data.

biophysics↗

bioRxiv2023

Cross-site reproducibility of human cortical organoids reveals consistent cell type composition and architecture

BackgroundReproducibility of human cortical organoid (hCO) phenotypes remains a concern for modeling neurodevelopmental disorders. While guided hCO protocols reproducibly generate cortical cell types in multiple cell lines at one site, variability across sites using a harmonized protocol has not yet been evaluated. We present an hCO cross-site reproducibility study examining multiple phenotypes. MethodsThree independent research groups generated hCOs from one induced pluripotent stem cell (iPSC) line using a harmonized miniaturized spinning bioreactor protocol. scRNA-seq, 3D fluorescent imaging, phase contrast imaging, qPCR, and flow cytometry were used to characterize the 3 month differentiations across sites. ResultsIn all sites, hCOs were mostly cortical progenitor and neuronal cell types in reproducible proportions with moderate to high fidelity to the in vivo brain that were consistently organized in cortical wall-like buds. Cross-site differences were detected in hCO size and morphology. Differential gene expression showed differences in metabolism and cellular stress across sites. Although iPSC culture conditions were consistent and iPSCs remained undifferentiated, primed stem cell marker expression prior to differentiation correlated with cell type proportions in hCOs. ConclusionsWe identified hCO phenotypes that are reproducible across sites using a harmonized differentiation protocol. Previously described limitations of hCO models were also reproduced including off-target differentiations, necrotic cores, and cellular stress. Improving our understanding of how stem cell states influence early hCO cell types may increase reliability of hCO differentiations. Cross-site reproducibility of hCO cell type proportions and organization lays the foundation for future collaborative prospective meta-analytic studies modeling neurodevelopmental disorders in hCOs.

neuroscience↗

bioRxiv2021

Segmentor: A tool for manual refinement of 3D microscopy annotations

BackgroundRecent advances in tissue clearing techniques, combined with high-speed image acquisition through light sheet microscopy, enable rapid three-dimensional (3D) imaging of biological specimens, such as whole mouse brains, in a matter of hours. Quantitative analysis of such 3D images can help us understand how changes in brain structure lead to differences in behavior or cognition, but distinguishing features of interest, such as nuclei, from background can be challenging. Recent deep learning-based nuclear segmentation algorithms show great promise for automated segmentation, but require large numbers of manually and accurately labeled nuclei as training data. ResultsWe present Segmentor, an open-source tool for reliable, efficient, and user-friendly manual annotation and refinement of objects (e.g., nuclei) within 3D light sheet microscopy images. Segmentor employs a hybrid 2D-3D approach for visualizing and segmenting objects and contains features for automatic region splitting, designed specifically for streamlining the process of 3D segmentation of nuclei. We show that editing simultaneously in 2D and 3D using Segmentor significantly decreases time spent on manual annotations without affecting accuracy. ConclusionsSegmentor is a tool for increased efficiency of manual annotation and refinement of 3D objects that can be used to train deep learning segmentation algorithms, and is available at https://www.nucleininja.org/ and https://github.com/RENCI/Segmentor.

neuroscience↗