Biology subjects

Linsley, J. W.

Publications and source records attributed to Linsley, J. W..

5 recordsLinked to original sources

bioRxiv2023

A deep-learning-based screening platform in aging-relevant human motor neurons to identify therapeutic compounds for amyotrophic lateral sclerosis

Age is the primary risk factor for most neurodegenerative diseases including amyo-trophic lateral sclerosis (ALS). Despite the clear importance of age on the development and progression of ALS, age is rarely considered a factor in cellular or animal models of ALS. The recent advent of direct reprogramming methodologies, whereby somatic cells such as fibroblasts can be converted into neurons while retaining the age signature of the host, has facilitated the study of age-relevant human cells in vitro for the first time. Despite the promise of this technology, age is a complex, multidimensional, and dy-namic phenotype that makes the interpretation of the interplay between age and dis-ease a great challenge. To circumvent these challenges, we developed a screening platform for directly reprogrammed neurons in an age-relevant human cellular model to better model and identify disease-modifying targets and pathways for ALS. This plat-form uses deep learning image analysis to screen compounds for efficacy against ALS-associated cellular phenotypes and efficiently classifies ALS-like phenotypic signatures as well as predicts the age of the original fibroblast donor. Notably, our work identified NCB-0846, a TNIK/MAP4K7 inhibitor, as a novel compound with the ability to revert ALS-like phenotypes. Moreover, we show that chronic dosing of NCB-0846 in the SOD1G93A mouse model of ALS significantly reduced serum neurofilament-L levels. Our findings establish the power of a platform that combines direct reprogramming of human cells and deep learning as a powerful tool for combatting aging and age-related diseases.

neuroscience↗

bioRxiv2022

Stac1 Regulates Sensory Stimulus Induced Escape Locomotion

The stac family of genes are expressed by several cell types including neurons and muscles in a wide variety of animals. In vertebrates, stac3 encodes an adaptor protein specifically expressed by skeletal muscle that regulates L-type calcium channels (CaChs) and excitation-contraction coupling. The function of Stac proteins expressed by neurons in the vertebrate CNS, however, is unclear. To better understand neuronal Stac proteins, we identified the stac1 gene in zebrafish. stac1 is expressed selectively in the embryonic CNS including in Kolmer-Agduhr (KA) neurons, the cerebral fluid-contacting neurons (CSF-cNs) in the spinal cord. Previously CSF-cNs in the spinal cord were implicated in locomotion by zebrafish larvae. Thus, expression of stac1 by CSF-cNs and the regulation of CaChs by Stac3 suggest the hypothesis that Stac1 may be important for normal locomotion by zebrafish embryos. We tested to see if optogenetic activation of CSF-cNs was sufficient to induced swimming in embryos as it is in larvae. Indeed, optogenetic activation of CSF-cNs in embryos induced swimming in embryos. Next, we generated stac1-/- null embryos and found that both mechanosensory and noxious stimulus-induced swimming were decreased. We further found that zebrafish embryos respond more vigorously to tactile stimulation in the light compared to the dark. Interestingly, light enhancement of touch-induced swimming was eliminated in stac1 mutants. Thus, Stac1 regulates escape locomotion in zebrafish embryos perhaps by regulating the activity of CSF-cNs. SIGNIFICANCE STATEMENTThe stac genes are a small family of genes found in neurons and muscle in both vertebrates and invertebrates. Stac3 is a muscle protein that controls excitation-contraction coupling via regulation of L-type calcium channels and in humans a STAC3 mutation is responsible for a congenital myopathy. The function of neural Stac proteins, however, is unknown in vertebrates. The findings of this report show that neural stac1 is expressed by cerebral fluid-contacting neurons (CSF-cNs) in the spinal cord of zebrafish embryos and that it is necessary for normal sensory stimulus induced escape swimming. To our knowledge this is the first demonstration of a function for stac genes in neurons in the vertebrate nervous system.

neuroscience↗

bioRxiv2020

Single cell tracking based on Voronoi partition via stable matching

Live-cell imaging is an important technique to study cell migration and proliferation as well as image-based profiling of drug perturbations over time. To gain biological insights from live-cell imaging data, it is necessary to identify individual cells, follow them over time and extract quantitative information. However, since often biological experiment does not allow the high temporal resolution to reduce excessive levels of illumination or minimize unnecessary oversampling to monitor long-term dynamics, it is still a challenging task to obtain good tracking results with coarsely sampled imaging data. To address this problem, we consider cell tracking problem as "stable matching problem" and propose a robust tracking method based on Voronoi partition which adapts parameters that need to be set according to the spatio-temporal characteristics of live cell imaging data such as cell population and migration. We demonstrate the performance improvement provided by the proposed method using numerical simulations and compare its performance with proximity-based tracking and nearest neighbor-based tracking.

systems biology↗

bioRxiv2020

Super-human cell death detection with biomarker-optimized neural networks

Cell death is an essential process in biology that must be accounted for in live microscopy experiments. Nevertheless, cell death is difficult to detect without perturbing experiments with stains, dyes or biosensors that can bias experimental outcomes, lead to inconsistent results, and reduce the number of processes that can be simultaneously labelled. These additional steps also make live microscopy difficult to scale for high-throughput screening because of the cost, labor, and analysis they entail. We address this fundamental limitation of live microscopy with biomarker-optimized convolutional neural networks (BO-CNN): computer vision models trained with a ground truth biosensor that detect live cells with superhuman, 96% accuracy more than 100 times faster than previous methods. Our models learn to identify important morphological characteristics associated with cell vitality without human input or additional perturbations, and to generalize to other imaging modalities and cell types for which they have no specialized training. We demonstrate that we can interpret decisions from BO-CNN models to gain biological insight into the patterns they use to achieve superhuman accuracy. The BO-CNN approach is broadly useful for live microscopy, and affords a powerful new paradigm for advancing the state of high-throughput imaging in a variety of contexts.

cell biology↗

bioRxiv2019

Genetically encoded cell-death indicators (GEDI) to detect an early irreversible commitment to neurodegeneration

Cell death is a critical process that occurs normally in health and disease. However, its study is limited due to available technologies that only detect very late stages in the process or specific death mechanisms. Here, we report the development of a new fluorescent biosensor called genetically encoded death indicator (GEDI). GEDI specifically detects an intracellular Ca2+ level that cells achieve early in the cell death process and marks a stage at which cells are irreversibly committed to die. The time-resolved nature of GEDI delineates a binary demarcation of cell life and death in real time, reformulating the definition of cell death. We demonstrate that GEDI acutely and accurately reports death of rodent and human neurons in vitro, and show GEDI enables a novel automated imaging platform for single cell detection of neuronal death in vivo in zebrafish larvae. With a quantitative pseudo-ratiometric signal, GEDI facilitates high-throughput analysis of cell death in time lapse imaging analysis, providing the necessary resolution and scale to identify early factors leading to cell death in studies of neurodegeneration.

neuroscience↗