Biology subjects

Lee, H. D.

Publications and source records attributed to Lee, H. D..

5 recordsLinked to original sources

bioRxiv2023

DARTsort: A modular drift tracking spike sorter for high-density multi-electrode probes

With the advent of high-density, multi-electrode probes, there has been a renewed interest in developing robust and scalable algorithms for spike sorting. Current spike sorting approaches, however, struggle to deal with noisy recordings and probe motion (drift). Here we introduce a modular and interpretable spike sorting pipeline, DARTsort (Drift Aware Registration and Tracking), that builds upon recent advances in denoising, spike localization, and drift estimation. DARTsort integrates a precise estimate of probe drift over time into a model of the spiking signal. This allows our method to be robust to drift across a variety of probe geometries. We show that our spike sorting algorithm outperforms a current state-of-the-art spike sorting algorithm, Kilosort 2.5, on simulated datasets with different drift types and noise levels. Open-source code can be found at https://github.com/cwindolf/dartsort.

neuroscience↗

bioRxiv2023

Development of a Synthetic Biosensor for Chemical Exchange MRI Utilizing In Silico Optimized Peptides

1Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging (MRI) has been identified as a novel alternative to classical diagnostic imaging. Over the last several decades, many studies have been conducted to determine possible CEST agents, such as endogenously expressed compounds or proteins, that can be utilized to produce contrast with minimally invasive procedures and reduced or non-existent levels of toxicity. In recent years there has been an increased interest in the generation of genetically engineered CEST contrast agents, typically based on existing proteins with CEST contrast or modified to produce CEST contrast. We have developed an in-silico method for the evolution of peptide sequences to optimize CEST contrast and showed that these peptides could be combined to create de novo biosensors for CEST MRI. A single protein, superCESTide 2.0, was designed to be 198 amino acids. SuperCESTide 2.0 was expressed in E. coli and purified with size-exclusion chromatography. The magnetic transfer ratio asymmetry (MTRasym) generated by superCESTide 2.0 was comparable to levels seen in previous CEST reporters, such as protamine sulfate (salmon protamine, SP), Poly-L-Lysine (PLL), and human protamine (hPRM1). This data shows that novel peptides with sequences optimized in silico for CEST contrast that utilizes a more comprehensive range of amino acids can still produce contrast when assembled into protein units expressed in complex living environments.

synthetic biology↗

bioRxiv2023

A Novel Protein for the Bioremediation of Gadolinium Waste

Several hundreds of tons of gadolinium-based contrast agents (GBCAs) are being dumped into the environment every year. Although macrocyclic GBCAs exhibit superior stability compared to their linear counterparts, we have found that the structural integrity of chelates are susceptible to ultraviolet light, regardless of configuration. In this study, we present a synthetic protein termed GLamouR that binds and reports gadolinium in an intensiometric manner. We then explore the extraction of gadolinium from GBCA-spiked artificial urine samples and investigate if the low picomolar concentrations reported in gadolinium-contaminated water sources pose a barrier for bioremediation. Based on promising results, we anticipate GLamouR can be used for detecting and mining REEs beyond gadolinium as well and hope to expand the biological toolbox for such applications.

bioengineering↗

bioRxiv2022

Reproducibility of in-vivo electrophysiological measurements in mice

Understanding brain function relies on the collective work of many labs generating reproducible results. However, reproducibility has not been systematically assessed within the context of electrophysiological recordings during cognitive behaviors. To address this, we formed a multi-lab collaboration using a shared, open-source behavioral task and experimental apparatus. Experimenters in ten laboratories repeatedly targeted Neuropixels probes to the same location (spanning secondary visual areas, hippocampus, and thalamus) in mice making decisions; this generated a total of 121 experimental replicates, a unique dataset for evaluating reproducibility of electrophysiology experiments. Despite standardizing both behavioral and electrophysiological procedures, some experimental outcomes were highly variable. A closer analysis uncovered that variability in electrode targeting hindered reproducibility, as did the limited statistical power of some routinely used electrophysiological analyses, such as single-neuron tests of modulation by individual task parameters. Reproducibility was enhanced by histological and electrophysiological quality-control criteria. Our observations suggest that data from systems neuroscience is vulnerable to a lack of reproducibility, but that across-lab standardization, including metrics we propose, can serve to mitigate this.

neuroscience↗

bioRxiv2021

Three-dimensional spike localization and improved motion correction for Neuropixels recordings

Neuropixels (NP) probes are dense linear multi-electrode arrays that have rapidly become essential tools for studying the electrophysiology of large neural populations. Unfortunately, a number of challenges remain in analyzing the large datasets output by these probes. Here we introduce several new methods for extracting useful spiking information from NP probes. First, we use a simple point neuron model, together with a neural-network denoiser, to efficiently map single spikes detected on the probe into three-dimensional localizations. Previous methods localized individual spikes in two dimensions only; we show that the new localization approach is significantly more robust and provides an improved feature set for clustering spikes according to neural identity ("spike sorting"). Next, we denoise the resulting three-dimensional point-cloud representation of the data, and show that the resulting 3D images can be accurately registered over time, leading to improved tracking of time-varying neural activity over the probe, and in turn, crisper estimates of neural clusters over time. Open source code is available at https://github.com/int-brain-lab/spikes_localization_registration.git.

neuroscience↗