NG2-Glia Transiently Overcome Their Homeostatic Network and Contribute to Wound Closure After Brain Injury.

In the adult brain, NG2-glia represent a cell population that responds to injury. To further investigate if, how and why NG2-glia are recruited to the injury site, we analyzed in detail the long-term reaction of NG2-glia after a lesion by time-lapse two-photon in vivo microscopy. Live imaging over several weeks of GFP-labeled NG2-glia in the stab wounded cerebral cortex revealed their fast and heterogeneous reaction, including proliferation, migration, polarization, hypertrophy, or a mixed response, while a small subset of cells remained unresponsive. At the peak of the reaction, 2-4 days after the injury, NG2-glia accumulated around and within the lesion core, overcoming the homeostatic control of their density, which normalized back to physiological conditions only 4 weeks after the insult. Genetic ablation of proliferating NG2-glia demonstrated that this accumulation contributed beneficially to wound closure. Thus, NG2-glia show a fast response to traumatic brain injury (TBI) and participate in tissue repair.

Image analysis of neural stem cell division patterns in the zebrafish brain.

Proliferating stem cells in the adult body are the source of constant regeneration. In the brain, neural stem cells (NSCs) divide to maintain the stem cell population and generate neural progenitor cells that eventually replenish mature neurons and glial cells. How much spatial coordination of NSC division and differentiation is present in a functional brain is an open question. To quantify the patterns of stem cell divisions, one has to (i) identify the pool of NSCs that have the ability to divide, (ii) determine NSCs that divide within a given time window, and (iii) analyze the degree of spatial coordination. Here, we present a bioimage informatics pipeline that automatically identifies GFP expressing NSCs in three-dimensional image stacks of zebrafish brain from whole-mount preparations. We exploit the fact that NSCs in the zebrafish hemispheres are located on a two-dimensional surface and identify between 1,500 and 2,500 NSCs in six brain hemispheres. We then determine the position of dividing NSCs in the hemisphere by EdU incorporation into cells undergoing S-phase and calculate all pairwise NSC distances with three alternative metrics. Finally, we fit a probabilistic model to the observed spatial patterns that accounts for the non-homogeneous distribution of NSCs. We find a weak positive coordination between dividing NSCs irrespective of the metric and conclude that neither strong inhibitory nor strong attractive signals drive NSC divisions in the adult zebrafish brain. © 2017 International Society for Advancement of Cytometry.

Prospective identification of hematopoietic lineage choice by deep learning.

Differentiation alters molecular properties of stem and progenitor cells, leading to changes in their shape and movement characteristics. We present a deep neural network that prospectively predicts lineage choice in differentiating primary hematopoietic progenitors using image patches from brightfield microscopy and cellular movement. Surprisingly, lineage choice can be detected up to three generations before conventional molecular markers are observable. Our approach allows identification of cells with differentially expressed lineage-specifying genes without molecular labeling.

Twist1 induces distinct cell states depending on TGFBR1-activation.

Basic helix-loop-helix transcription factor Twist1 is a master regulator of Epithelial-Mesenchymal Transition (EMT), a cellular program implicated in different stages of development as well as metastatic dissemination of carcinomas. Here, we show that Twist1 requires TGF-beta type-I receptor (TGFBR1)-activation to bind an enhancer region of downstream effector ZEB1, thereby inducing ZEB1 transcription and EMT. When TGFBR1-phosphorylation is inhibited, Twist1 generates a distinct cell state characterized by collective invasion, simultaneous proliferation and expression of endothelial markers. By contrast, TGFBR1-activation directs Twist1 to induce stable mesenchymal transdifferentiation through EMT, thereby generating cells that display single-cell invasion, but lose their proliferative capacity. In conclusion, preventing Twist1-induced EMT by inhibiting TGFß-signaling does not generally block acquisition of invasion, but switches mode from single-cell/non-proliferative to collective/proliferative. Together, these data reveal that transient Twist1-activation induces distinct cell states depending on signaling context and caution against the use of TGFß-inhibitors as a therapeutic strategy to target invasiveness.

An automatic method for robust and fast cell detection in bright field images from high-throughput microscopy.

BACKGROUND: In recent years, high-throughput microscopy has emerged as a powerful tool to analyze cellular dynamics in an unprecedentedly high resolved manner. The amount of data that is generated, for example in long-term time-lapse microscopy experiments, requires automated methods for processing and analysis. Available software frameworks are well suited for high-throughput processing of fluorescence images, but they often do not perform well on bright field image data that varies considerably between laboratories, setups, and even single experiments. RESULTS: In this contribution, we present a fully automated image processing pipeline that is able to robustly segment and analyze cells with ellipsoid morphology from bright field microscopy in a high-throughput, yet time efficient manner. The pipeline comprises two steps: (i) Image acquisition is adjusted to obtain optimal bright field image quality for automatic processing. (ii) A concatenation of fast performing image processing algorithms robustly identifies single cells in each image. We applied the method to a time-lapse movie consisting of ∼315,000 images of differentiating hematopoietic stem cells over 6 days. We evaluated the accuracy of our method by comparing the number of identified cells with manual counts. Our method is able to segment images with varying cell density and different cell types without parameter adjustment and clearly outperforms a standard approach. By computing population doubling times, we were able to identify three growth phases in the stem cell population throughout the whole movie, and validated our result with cell cycle times from single cell tracking. CONCLUSIONS: Our method allows fully automated processing and analysis of high-throughput bright field microscopy data. The robustness of cell detection and fast computation time will support the analysis of high-content screening experiments, on-line analysis of time-lapse experiments as well as development of methods to automatically track single-cell genealogies.

Live imaging of astrocyte responses to acute injury reveals selective juxtavascular proliferation.

Astrocytes are thought to have important roles after brain injury, but their behavior has largely been inferred from postmortem analysis. To examine the mechanisms that recruit astrocytes to sites of injury, we used in vivo two-photon laser-scanning microscopy to follow the response of GFP-labeled astrocytes in the adult mouse cerebral cortex over several weeks after acute injury. Live imaging revealed a marked heterogeneity in the reaction of individual astrocytes, with one subset retaining their initial morphology, another directing their processes toward the lesion, and a distinct subset located at juxtavascular sites proliferating. Although no astrocytes actively migrated toward the injury site, selective proliferation of juxtavascular astrocytes was observed after the introduction of a lesion and was still the case, even though the extent was reduced, after astrocyte-specific deletion of the RhoGTPase Cdc42. Thus, astrocyte recruitment after injury relies solely on proliferation in a specific niche.

PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes.

In recent years, microRNAs have been shown to play important roles in physiological as well as malignant processes. The PhenomiR database http://mips.helmholtz-muenchen.de/phenomir provides data from 542 studies that investigate deregulation of microRNA expression in diseases and biological processes as a systematic, manually curated resource. Using the PhenomiR dataset, we could demonstrate that, depending on disease type, independent information from cell culture studies contrasts with conclusions drawn from patient studies.