Biology subjects

Ferrante, J.

Publications and source records attributed to Ferrante, J..

3 recordsLinked to original sources

bioRxiv2022

A phenotypic screening platform for chronic pain therapeutics using all-optical electrophysiology

Chronic pain associated with osteoarthritis (OA) remains an intractable problem with few effective treatment options. New approaches are needed to model the disease biology and to drive discovery of therapeutics. Here, we present an in vitro model of OA pain, where dorsal root ganglion (DRG) sensory neurons were sensitized by a defined mixture of disease-relevant inflammatory mediators, here called Sensitizing PAin Reagent Composition or SPARC. OA-SPARC components showed synergistic or additive effects when applied in combination and induced pain phenotypes in vivo. To measure the effect of OA-SPARC on neural firing in a scalable format for drug discovery, we used a custom system for high throughput all-optical electrophysiology. This system enabled light-based membrane voltage recordings from hundreds of neurons in parallel with single cell resolution and a throughput of up to 500,000 neurons per day, with patch clamp-like single action potential resolution. A computational framework was developed to construct a multiparameter OA-SPARC neuronal phenotype and to quantitatively assess phenotype reversal by candidate pharmacology with different mechanisms of action. We screened ~3000 approved drugs and mechanistically focused compounds, yielding data from over 1.2 million individual neurons with detailed assessment of both functional OA-SPARC phenotype rescue and orthogonal "off-target" effects. Analysis of confirmed hits revealed diverse potential analgesic mechanisms including well-known ion channel modulators as well as less characterized mechanisms including MEK inhibitors and tyrosine kinase modulators, providing validation of the platform for pain drug discovery.

neuroscience↗

bioRxiv2022

Rapastinel Accelerates Loss of Withdrawal Signs after Repeated Morphine and Blunts Relapse to Conditioned Place Preference

The purpose of the present study was to evaluate the efficacy of rapastinel, an allosteric modulator of NMDA receptor function, to accelerate the loss of opioid withdrawal symptoms and blunt or prevent relapse to morphine conditioned place preference (CPP) in rats. Two studies were conducted. In study 1, adult and adolescent male and female rats were treated with increasing doses of morphine (5 mg/kg, bid to 25 mg/kg bid) for 5 days. On day 6 animals were treated with naloxone (1 mg/kg) and withdrawal was assessed. They were then treated with saline or rapastinel (5 mg/kg) on days 6 and 8, and withdrawal assessed on day 9. Rapastinel treated animals exhibited significantly lower levels of withdrawal signs on day 9. No sex or age differences were observed. In Study 2, CPP for morphine was established in adult rats (males and females) by 4 daily pairings with saline and morphine (am/pm alternation). They were tested for CPP on day 5, and then treated with rapastinel (5 mg/kg) or saline daily on days 6-10 of extinction. On day 11 they received a final dose of rapastinel or saline followed by extinction. On day 12, animals received 1 mg/kg of morphine and were tested for relapse. Rapastinel did not affect extinction of CPP, but rapastinel-treated animals spent significantly less time in the previously morphine-paired side than saline-treated animals during the relapse trial. These findings of accelerated loss of withdrawal signs and blunted relapse to CPP suggest that rapastinel could provide an adjunctive therapy for opioid dependence during initiation of pharmacotherapy for opioid dependence.

pharmacology and toxicology↗

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↗