Biology subjects

Nichols, E.

Publications and source records attributed to Nichols, E..

3 recordsLinked to original sources

bioRxiv2022

A distinct subset of oligodendrocyte lineage cells interact with the developing dorsal root entry during its genesis

Oligodendrocytes are the myelinating cell of the CNS and are critical for the functionality of the nervous system. In the packed CNS, we know distinct profiles of oligodendrocytes are present. Here, we used intravital imaging in zebrafish to identify a distinct oligodendrocyte lineage cell (OLC) that resides on the dorsal root ganglia sensory neurons in the spinal cord. Our profiling of OLC cellular dynamics revealed a distinct cell cluster that interacts with peripheral sensory neurons at the dorsal root entry zone (DREZ). With pharmacological, physical and genetic manipulations, we show that the entry of dorsal root ganglia pioneer axons across the DREZ is important to produce sensory located oligodendrocyte lineage cells. These oligodendrocyte lineage cells on peripherally derived sensory neurons display distinct processes that are stable and do not express mbp. Upon their removal, sensory behavior related to the DRG neurons is abolished. Together, these data support the hypothesis that peripheral neurons at the DREZ can also impact oligodendrocyte development.

neuroscience↗

bioRxiv2021

Real-time Image Denoising of Mixed Poisson-Gaussian Noise in Fluorescence Microscopy Images using ImageJ

AO_SCPLOWBSTRACTC_SCPLOWFluorescence microscopy imaging speed is fundamentally limited by the measurement signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate, computational denoising techniques can be used to suppress noise. However, common techniques to estimate a denoised image from a single frame are either computationally expensive or rely on simple noise statistical models. These models assume Poisson or Gaussian noise statistics, which are not appropriate for many fluorescence microscopy applications that contain quantum shot noise and electronic Johnson-Nyquist noise, therefore a mixture of Poisson and Gaussian noise. In this paper, we show convolutional neural networks (CNNs) trained on mixed Poisson and Gaussian noise images to overcome the limitations of existing image denoising methods. The trained CNN is presented as an open-source ImageJ plugin that performs real-time image denoising (within tens of milliseconds) with superior performance (SNR improvement) compared to the conventional fluorescence microscopy denoising methods. The method is validated on external datasets with out-of-distribution noise, contrast, structure, and imaging modalities from the training data and consistently achieves high performance (> 8 dB) denoising in less time than other fluorescence microscopy denoising methods.

neuroscience↗

bioRxiv2021

Multiplex genomic recording of enhancer and signal transduction activity in mammalian cells

Measurements of gene expression and signal transduction activity are conventionally performed with methods that require either the destruction or live imaging of a biological sample within the timeframe of interest. Here we demonstrate an alternative paradigm, termed ENGRAM (ENhancer-driven Genomic Recording of transcriptional Activity in Multiplex), in which the activity and dynamics of multiple transcriptional reporters are stably recorded to DNA. ENGRAM is based on the prime editing-mediated insertion of signal- or enhancer-specific barcodes to a genomically encoded recording unit. We show how this strategy can be used to concurrently genomically record the relative activity of at least hundreds of enhancers with high fidelity, sensitivity and reproducibility. Leveraging synthetic enhancers that are responsive to specific signal transduction pathways, we further demonstrate time- and concentration-dependent genomic recording of Wnt, NF-{kappa}B, and Tet-On activity. Finally, by coupling ENGRAM to sequential genome editing, we show how serially occurring molecular events can potentially be ordered. Looking forward, we envision that multiplex, ENGRAM-based recording of the strength, duration and order of enhancer and signal transduction activities has broad potential for application in functional genomics, developmental biology and neuroscience.

genomics↗