Search bioRxiv⌕ Search

Biology subjects

Harmel, C.

Publications and source records attributed to Harmel, C..

2 recordsLinked to original sources

Multimodal spatiotemporal phenotyping of human organoid development

Organoids generated from human pluripotent stem cells (PSCs) provide experimental systems to study development and disease. However, we lack quantitative spatiotemporal descriptions of organoid development that incorporate measurements across different molecular modalities. Here we focus on the retina and use a single-cell multimodal approach to reconstruct human retinal organoid development. We establish an experimental and computational pipeline to generate multiplexed spatial protein maps over a retinal organoid time course and primary adult human retina, registering protein expression features at the population, cellular, and subcellular levels. We develop an analytical toolkit to segment nuclei, identify local and global tissue units, infer morphology trajectories, and analyze cell neighborhoods from multiplexed imaging data. We use this toolkit to visualize progenitor and neuron location, the spatial arrangements of extracellular and subcellular components, and global patterning in each organoid and primary tissue. In addition, we generate a single-cell transcriptome and chromatin accessibility time course dataset and infer a gene regulatory network underlying organoid development. We then integrate genomic data with spatially segmented nuclei into a multi-modal atlas enabling virtual exploration of retinal organoid development. We visualize molecular, cellular, and regulatory dynamics during organoid lamination, and identify regulons associated with neuronal differentiation and maintenance. We use the integrated atlas to explore retinal ganglion cell (RGC) spatial neighborhoods, highlighting pathways involved in RGC cell death. Finally, we show that mosaic CRISPR/Cas genetic perturbations in retinal organoids provide insight into cell fate regulation. Altogether, our work is a major advance toward a virtual human retinal organoid, and provides new directions for how to approach disorders of the visual system. More broadly, our approaches can be adapted to many organoid systems.

systems biology↗

Inferring cell motility in complex environments with incomplete tracking data

Cell motility has important influence on cell interactions and functionality for various biological aspects. Deciphering these dynamics often relies on live-cell microscopy measurements, which partly have to deal with limitations that could impair a reliable quantification of their motility. Especially given complex environments and tissue structures, limited observation periods, cells moving in and out of focus and impaired calibration of observation axes often lead to loss of cell tracks and insufficient tracking of motility within several dimensions. However, a reliable quantification of cell motility dynamics is essential when aiming at extrapolating the observed dynamics in order to understand cell population dynamics at larger temporal and spatial scales using appropriate simulation environments. To analyze how incomplete observations affect interpretation and parameterization of cell motility, we combined experimental observations with computational models. Studying individual cell dynamics within 3D collagen environments, we found that the gradual loss of cell tracks leads to an underestimation of several motility parameters with the effect dependent on the collagen density. By extending the automated fitting strategy FitMultiCell to account for cell track loss, we show that we are able to retrieve the actual cell dynamics and, thus, to reliably parameterize cell motility from such incomplete data. Applying our approach to the analysis of CD4+ T cells within 3D collagen environments that were infected with HIV-1, we could show that despite a considerable loss of cell tracks, the data still contained sufficient information to compare individual cell motilities by inferring and simulating their dynamics. Thereby, the analysis allowed us to disentangle the effect of HIV-1 infection and collagen density on individual cell motility. Our extended FitMultiCell-approach presented here provides a solution for the elimination of artifacts from cell track data analysis to robustly infer cell motility dynamics.

systems biology↗