Elena Bernardis

Stretch-activated ion channel Piezo1 directs lineage choice in human neural stem cells

Significance Stem cells make lineage-choice decisions based on a combination of internal and external signals, including mechanical cues from the surrounding environment. Here we show that Piezo1, an ion channel opened by membrane tension, plays an important role in transducing matrix mechanical information to intracellular pathways affecting differentiation in neural stem cells. Piezo1 activity influences whether neural stem cells differentiate along a neuronal or astrocytic lineage. One of the barriers to successful neural stem cell transplantation therapy for neurological disorders lies in directing the fate of transplanted cells. Pharmacological agents aimed at modulating Piezo1 activity may be useful in directing the fate of transplanted neural stem cells toward the desired lineage. Neural stem cells are multipotent cells with the ability to differentiate into neurons, astrocytes, and oligodendrocytes. Lineage specification is strongly sensitive to the mechanical properties of the cellular environment. However, molecular pathways transducing matrix mechanical cues to intracellular signaling pathways linked to lineage specification remain unclear. We found that the mechanically gated ion channel Piezo1 is expressed by brain-derived human neural stem/progenitor cells and is responsible for a mechanically induced ionic current. Piezo1 activity triggered by traction forces elicited influx of Ca2+, a known modulator of differentiation, in a substrate-stiffness–dependent manner. Inhibition of channel activity by the pharmacological inhibitor GsMTx-4 or by siRNA-mediated Piezo1 knockdown suppressed neurogenesis and enhanced astrogenesis. Piezo1 knockdown also reduced the nuclear localization of the mechanoreactive transcriptional coactivator Yes-associated protein. We propose that the mechanically gated ion channel Piezo1 is an important determinant of mechanosensitive lineage choice in neural stem cells and may play similar roles in other multipotent stem cells.

Finding dots: Segmentation as popping out regions from boundaries

Many applications need to segment out all small round regions in an image. This task of finding dots can be viewed as a region segmentation problem where the dots form one region and the areas between dots form the other. We formulate it as a graph cuts problem with two types of grouping cues: short-range attraction based on feature similarity and long-range repulsion based on feature dissimilarity. The feature we use is a pixel-centric relational representation that encodes local convexity: Pixels inside the dots and outside the dots become sinks and sources of the feature vector. Normalized cuts on both attraction and repulsion pop out all the dots in a single binary segmentation. Our experiments show that our method is more accurate and robust than state-of-art segmentation algorithms on four categories of microscopic images. It can also detect textons in natural scene images with the same set of parameters.

Pop out many small structures from a very large microscopic image

In medical research, many applications require counting and measuring small regions in a large image. Extracting these regions poses a dilemma in terms of segmentation granularity due to fine structures and segmentation complexity due to large image sizes. We propose a constrained spectral graph partitioning framework to address the former while also reducing the segmentation complexity associated with the latter. The final segmentation is obtained from a set of patch segmentations, each independently derived subject to stitching constraints between neighboring patches. Individual segmentation is based on local pairwise cues designed to pop out all cells simultaneously from their common background, while the constraints are derived from mutual agreement analysis on patch segmentations from a previous round of segmentation. Our results demonstrate that the constrained segmentation not only stitches solutions seamlessly along overlapping patch borders but also refines the segmentation in the patch interiors.

Temporal shape analysis via the spectral signature

In this paper, we adapt spectral signatures for capturing morphological changes over time. Advanced techniques for capturing temporal shape changes frequently rely on first registering the sequence of shapes and then analyzing the corresponding set of high dimensional deformation maps. Instead, we propose a simple encoding motivated by the observation that small shape deformations lead to minor refinements in the spectral signature composed of the eigenvalues of the Laplace operator. The proposed encoding does not require registration, since spectral signatures are invariant to pose changes. We apply our representation to the shapes of the ventricles extracted from 22 cine MR scans of healthy controls and Tetralogy of Fallot patients. We then measure the accuracy score of our encoding by training a linear classifier, which outperforms the same classifier based on volumetric measurements.

Extracting evolving pathologies via spectral clustering

A bottleneck in the analysis of longitudinal MR scans with white matter brain lesions is the temporally consistent segmentation of the pathology. We identify pathologies in 3D+t(ime) within a spectral graph clustering framework. Our clustering approach simultaneously segments and tracks the evolving lesions by identifying characteristic image patterns at each time-point and voxel correspondences across time-points. For each 3D image, our method constructs a graph where weights between nodes capture the likeliness of two voxels belonging to the same region. Based on these weights, we then establish rough correspondences between graph nodes at different time-points along estimated pathology evolution directions. We combine the graphs by aligning the weights to a reference time-point, thus integrating temporal information across the 3D images, and formulate the 3D+t segmentation problem as a binary partitioning of this graph. The resulting segmentation is very robust to local intensity fluctuations and yields better results than segmentations generated for each time-point.

Robust Segmentation by Cutting across a Stack of Gamma Transformed Images

Medical image segmentation appears to be governed by the global intensity level and should be robust to local intensity fluctuation. We develop an efficient spectral graph method which seeks the best segmentation on a stack of gamma transformed versions of the original image. Each gamma image produces two types of grouping cues operating at different ranges: Short-range attraction pulls pixels towards region centers, while long-range repulsion pushes pixels away from region boundaries. With rough pixel correspondence between gamma images, we obtain an aligned cue stack for the original image. Our experimental results demonstrate that cutting across the entire gamma stack delivers more accurate segmentations than commonly used watershed algorithms.

Structural-Flow Trajectories for Unravelling 3D Tubular Bundles

We cast segmentation of 3D tubular structures in a bundle as partitioning of structural-flow trajectories. Traditional 3D segmentation algorithms aggregate local pixel correlations incrementally along a 3D stack. In contrast, structural-flow trajectories establish long range pixel correspondences and their affinities propagate grouping cues across the entire volume simultaneously, from informative to non-informative places. Segmentation by trajectory clustring recovers from persistent ambiguities caused by faint boundaries or low contrast, common in medical images. Trajectories are computed by linking successive registration fields, each one registering pairs of consecutive slices of the 3D stack. We show our method effectively unravels densely packed tubular structures, without any supervision or 3D shape priors, outperforming previous 2D and 3D segmentation algorithms.

Segmentation subject to stitching constraints: finding many small structures in a large image

Extracting numerous cells in a large microscopic image is often required in medical research. The challenge is to reduce the segmentation complexity on a large image without losing the fine segmentation granularity of small structures. We propose a constrained spectral graph partitioning approach where the segmentation of the entire image is obtained from a set of patch segmentations, independently derived but subject to stitching constraints between neighboring patches. The constraints come from mutual agreement analysis on patch segmentations from a previous round. Our experimental results demonstrate that the constrained segmentation not only stitches solutions seamlessly along overlapping patch borders but also refines the segmentation in the patch interiors.

Structural correspondence as a contour grouping problem

We present a novel viewpoint which approaches the structural correspondence across an image stack in the 3D space as solving a contour grouping problem. Finding 3D cellular tubes becomes finding closed contours. We derive grouping cues between cells in adjacent slices based on their ability to relate in the 3D space. Those that form a long 3D tube in the space become the most salient contour, while those of shorter lengths become less salient. In the spectral graph-theoretical framework for contour grouping, such a separation by the contour length is reflected in complex eigenvectors of different magnitudes, from which these 3D tubes of varying lengths can thus be extracted, obviating the need for identifying missing correspondences.

Pediatric severity of alopecia tool

The Severity of Alopecia Tool serves as a tool for alopecia research and a clinical guideline for following progression of disease. The original Severity of Alopecia Tool score does not take into account pediatric age groups. As new clinical trials for alopecia areata include more children, a more accurate tool should be available for this population. By collecting images from patients 2‐21 years of age and aligning the hair‐bearing regions of the scalp, we created an adaptation of the Severity of Alopecia Tool for scoring hair loss percentage of the top, parietal, and occipital scalp in individuals 2‐5, 6‐11, and 12‐21 years of age.