Biology subjects

Huelsz-Prince, G.

Publications and source records attributed to Huelsz-Prince, G..

5 recordsLinked to original sources

bioRxiv2026

SMIM1 drives an alternative iron uptake pathway

Iron is essential for numerous biological processes, and its abundance is critical in human cells to prevent anemia or toxic overload. Although transferrin receptor-mediated iron uptake is the canonical route, human cells also possess an unstudied alternative pathway. To reveal this missing mechanism, we developed a large-scale, gain-of-function approach to assign genes to a catalogue of quantitative cellular traits. Applying this genetic strategy, we identified SMIM1, the antigen responsible for the rare Vel blood group and a transmembrane protein with unknown function, as a key regulator of iron homeostasis. We found that SMIM1 expression completely suppresses the iron deficiency caused by transferrin receptor loss. Mechanistically, SMIM1 mediates iron uptake through clathrin-dependent endocytosis of hemoglobin (Hb). SMIM1 directly binds Hb via its extracellular HKCK motif, leading to its transport to lysosomes and subsequent iron release. In the human population, genetic variants in SMIM1 influence Hb levels and erythrocyte traits. SMIM1-deficient mice display iron deficiency in early erythroid precursors in response to erythropoietic stress, underscoring the physiological relevance of this process.

cell biology↗

bioRxiv2022

Following cell type transitions in space and time by combining live-cell tracking and endpoint cell identity in intestinal organoids

Elucidating the dynamics of cellular differentiation in space and time is key to advancing organoid biology and technology. Direct visualization of differentiation patterns is challenging, however, in part because of the difficulty of simultaneously detecting all relevant cell types by fluorescence imaging. Here we present TypeTracker, which determines the differentiation trajectories of all cells within a region of interest, including their type transitions, growth, divisions, and changes in spatial organization. We show how the lineage tree topology, as determined by automated cell tracking, allows for the retrospective identification of cell types across multiple generations. The data revealed various surprising aspects of intestinal organoid differentiation, including symmetric fate adoption by sister cells, cell type abundance regulated by division, and type commitment of cells occurring prior to their spatial reorganization. Our method can be applied broadly to study other organoid systems and to screen for compounds that affect differentiation programs.

biophysics↗

bioRxiv2022

Mother cells control daughter cell proliferation in intestinal organoids to minimize proliferation fluctuations

During renewal of the intestine, cells are continuously generated by proliferation. Proliferation and differentiation must be tightly balanced, as any bias towards proliferation results in uncontrolled exponential growth. Yet, the inherently stochastic nature of cells raises the question how such fluctuations are limited. We used time-lapse microscopy to track all cells in crypts of growing mouse intestinal organoids for multiple generations, allowing full reconstruction of the underlying lineage dynamics in space and time. Proliferative behavior was highly symmetric between sister cells, with both sisters either jointly ceasing or continuing proliferation. Simulations revealed that such symmetric proliferative behavior minimizes cell number fluctuations, explaining our observation that proliferating cell number remained constant even as crypts increased in size considerably. Proliferative symmetry did not reflect positional symmetry, but rather lineage control through the mother cell. Our results indicate a concrete mechanism to balance proliferation and differentiation with minimal fluctuations, that may be broadly relevant for other tissues.

cell biology↗

bioRxiv2020

OrganoidTracker: efficient cell tracking using machine learning and manual error correction

Time-lapse microscopy is routinely used to follow cells within organoids, allowing direct study of division and differentiation patterns. There is an increasing interest in cell tracking in organoids, which makes it possible to study their growth and homeostasis at the single-cell level. As tracking these cells by hand is prohibitively time consuming, automation using a computer program is required. Unfortunately, organoids have a high cell density and fast cell movement, which makes automated cell tracking difficult. In this work, a semi-automated cell tracker has been developed. To detect the nuclei, we use a machine learning approach based on a convolutional neural network. To form cell trajectories, we link detections at different time points together using a min-cost flow solver. The tracker raises warnings for situations with likely errors. Rapid changes in nucleus volume and position are reported for manual review, as well as cases where nuclei divide, appear and disappear. When the warning system is adjusted such that virtually error-free lineage trees can be obtained, still less than 2% of all detected nuclei positions are marked for manual analysis. This provides an enormous speed boost over manual cell tracking, while still providing tracking data of the same quality as manual tracking.

bioinformatics↗

bioRxiv2018

Long-term expanding human airway organoids for disease modelling.

Organoids are self-organizing 3D structures grown from stem cells that recapitulate essential aspects of organ structure and function. Here we describe a method to establish long-term-expanding human airway organoids from broncho-alveolar biopsies or lavage material. The pseudostratified airway organoid epithelium consists of basal cells, functional multi-ciliated cells, mucus-producing goblet cells, and CC10-secreting club cells. Airway organoids derived from cystic fibrosis (CF) patients allow assessment of CFTR function in an organoid swelling assay. Organoid culture conditions also allow gene editing as well as the derivation of various types of lung cancer organoids. Respiratory syncytial virus (RSV) infection recapitulated central disease features and dramatically increases organoid cell motility, found to be driven by the non-structural viral NS2 protein. We conclude that human airway organoids represent versatile models for the in vitro study of hereditary, malignant, and infectious pulmonary disease.

cell biology↗