Biology subjects

Hillman, E.

Publications and source records attributed to Hillman, E..

3 recordsLinked to original sources

bioRxiv2025

Endothelial Type I Interferon signaling modulates the vascular response to ischemic brain injury

Vascular normalization [stabilization of aberrant angiogenesis and restoration of blood-brain barrier (BBB)] is critical for reducing long-term secondary sequelae after ischemic stroke. How immune and developmental signaling pathways coordinate these processes is poorly understood. Here we identify a unique brain endothelial cell (BEC) type one interferon (IFN1) signature in human and mouse ischemic stroke tissue. By leveraging two clinically-relevant murine ischemic stroke models, single-cell transcriptomics, and BBB functional assays, we find that deletion of endothelial IFN1 receptor (Ifnar1) exacerbates post-stroke BBB disruption and expands a BEC population expressing angiogenic and immature BBB markers. Conversely, IFN{beta} administration after stroke reduces acute BBB disruption. Activation of IFN1 signaling in BECs in vitro reduces vascular endothelial growth factor (VEGF) signaling to promote junctional stabilization, enhance barrier properties, and suppress angiogenic features. Thus, endogenous endothelial IFN1 signaling modulates BBB dysfunction and angiogenesis to promote vascular normalization after ischemic brain injury.

neuroscience↗

bioRxiv2019

Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data

Widefield calcium imaging enables recording of large-scale neural activity across the mouse dorsal cortex. In order to examine the relationship of these neural signals to the resulting behavior, it is critical to demix the recordings into meaningful spatial and temporal components that can be mapped onto well-defined brain regions. However, no current tools satisfactorily extract the activity of the different brain regions in individual mice in a data-driven manner, while taking into account mouse-specific and preparation-specific differences. Here, we introduce Localized semi-Nonnegative Matrix Factorization (LocaNMF), a method that efficiently decomposes widefield video data and allows us to directly compare activity across multiple mice by outputting mouse-specific localized functional regions that are significantly more interpretable than more traditional decomposition techniques. Moreover, it provides a natural subspace to directly compare correlation maps and neural dynamics across different behaviors, mice, and experimental conditions, and enables identification of task- and movement-related brain regions.

neuroscience↗

bioRxiv2018

Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data

Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution. The resulting datasets are quite large (with scales of TB/hour in some cases), which has presented a barrier to routine open sharing of this data, slowing progress in reproducible research. State of the art methods for analyzing this data are based on non-negative matrix factorization (NMF); these approaches solve a non-convex optimization problem, and are highly effective when good initializations are available, but can break down e.g. in low-SNR settings where common initialization approaches fail.\n\nHere we introduce an improved approach to compressing and denoising functional imaging data. The method is based on a spatially-localized penalized matrix decomposition (PMD) of the data to separate (low-dimensional) signal from (temporally-uncorrelated) noise. This approach can be applied in parallel on local spatial patches and is therefore highly scalable, does not impose non-negativity constraints or require stringent identifiability assumptions (leading to significantly more robust results compared to NMF), and estimates all parameters directly from the data, so no hand-tuning is required. We have applied the method to a wide range of functional imaging data (including one-photon, two-photon, three-photon, widefield, somatic, axonal, dendritic, calcium, and voltage imaging datasets): in all cases, we observe ~2-4x increases in SNR and compression rates of 20-300x with minimal visible loss of signal, with no adjustment of hyperparameters; this in turn facilitates the process of demixing the observed activity into contributions from individual neurons. We focus on two challenging applications: dendritic calcium imaging data and voltage imaging data in the context of optogenetic stimulation. In both cases, we show that our new approach leads to faster and much more robust extraction of activity from the video data.

neuroscience↗