Biology subjects

Roossien, D. H.

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

6 recordsLinked to original sources

bioRxiv2026

A transcriptomic and spatial map of serotonin autoreceptor expression in Drosophila

Serotonin is an evolutionarily ancient neurotransmitter that modulates an array of behaviors such as mood, sleep, and appetite across species. Serotonin acts primarily by binding to serotonin receptors, which are expressed in post-synaptic neurons (heteroreceptors) and serotonergic neurons themselves (autoreceptors). Serotonin autoreceptors modulate serotonergic tone, the foundational principles of which have been excellently demonstrated in vertebrate and invertebrate models. However, many aspects of the mechanisms and contexts in which this modulation occurs are still unclear. Drosophila melanogaster is a powerful model organism that can provide unique insights into autoreceptor function by the ability to perform precise spatial and temporal genetic manipulation with structural and functional readouts / behaviors of serotonin systems. However, a systematic characterization of serotonin autoreceptor expression in Drosophila has not been conducted. Here we use single-cell sequencing and genetic labeling to show that all five serotonin receptors are expressed in serotonergic neurons and map their expression at both the larval and adult stages of development. This is the first evidence of 5-HT2A and 5-HT7 expression in serotonergic neurons in any organism. Moreover, the unique combinations of autoreceptor expression in specific neuronal clusters will aid in the development of novel hypotheses for autoreceptor function, and demonstrates the utility of Drosophila as a model organism to study the function of serotonin autoreceptors.

neuroscience↗

bioRxiv2022

OPTIMIZED NEURON TRACING USING POST HOC REANALYSIS

Over the last decade, the advances in Brainbow labeling allowed labeling hundreds of neurons with distinct colors in the same field of view of a brain [1, 2]. Reconstruction (or "tracing") of the 3D structures of these images has been enabled by a growing set of software tools for automatic and manual annotation. It is common, however, to have errors introduced by heuristics used by tracing software, namely that they assume the "best" path is the highest intensity one, a more pertinent issue when dealing with multicolor microscope images. Here, we report nCorrect, an algorithm for correcting this error by reanalyzing previously created neuron traces to produce more physiologically-relevant ones. Specifically, we use a four dimensional minimization algorithm to identify a more-optimal reconstruction of the image, allowing us to better take advantage of existing manual tracing results. We define a new metric (hyperspectral cosine similarity) for describing the similarity of different neuron colors to each other. Our code is available in an open source license and forms the basis for future improved neuron tracing software.

neuroscience↗

bioRxiv2021

nGauge: Integrated and extensible neuron morphology analysis in Python

The study of neuron morphology requires robust and comprehensive methods to quantify the differences between neurons of different subtypes and animal species. Several software packages have been developed for the analysis of neuron tracing results stored in the standard SWC format. However, providing relatively simple quantifications and their non-extendable architecture prohibit their use for advanced data analysis and visualization. We developed nGauge, a Python toolkit to support the parsing and analysis of neuron morphology data. As an application programming interface (API), nGauge can be referenced by other popular open-source software to create custom informatics analysis pipelines and advanced visualizations. nGauge defines an extendable data structure that handles volumetric constructions (e.g. soma), in addition to the SWC linear reconstructions, while remaining light-weight. This greatly extends nGauges data compatibility.

neuroscience↗

bioRxiv2020

Bitbow: a digital format of Brainbow enables highly efficient neuronal lineage tracing and morphology reconstruction in single brains

Identifying the cellular origins and mapping the dendritic and axonal arbors of neurons have been century old quests to understand the heterogeneity among these brain cells. Classical chemical and genetic methods take advantage of light microscopy and sparse labeling to unambiguously, albeit inefficiently, trace a few neuronal lineages or reconstruct their morphologies in each sampled brain. To improve the analysis throughput, we designed Bitbow, a digital format of Brainbow which exponentially expands the color palette to provide tens of thousands of spectrally resolved unique labels. We generated transgenic Bitbow Drosophila lines, established statistical tools, and streamlined sample preparation, image processing and data analysis pipelines to allow conveniently mapping neural lineages, studying neuronal morphology and revealing neural network patterns with an unprecedented speed, scale and resolution.

neuroscience↗

bioRxiv2018

A Recurrent Neural Network Approach for Automated Neural Tracing in Multispectral 3D Images

Neuronal morphology reconstruction in fluorescence microscopy 3D images is essential for analyzing neuronal cell type and connectivity. Manual tracing of neurons in these images is time consuming and subjective. Automated tracing is highly desired yet is one of the foremost challenges in computational neuroscience. The multispectral labeling technique, Brainbow utilizes high dimensional spectral information to distinguish intermingled neuronal processes. It is particular interesting to develop new algorithms to include the spectral information into the tracing process. Recently, deep learning approaches achieved state-of-the-art in different computer vision and medical imaging applications. To benefit from the power of deep learning, in this paper, we propose an automated neural tracing approach in multispectral 3D Brainbow images based on recurrent neural net-work. We first adopt VBM4D approach to denoise multispectral 3D images. Then we generate cubes as training samples along the ground truth, manually traced paths. These cubes are the input to the recur-rent neural network. The proposed approach is simple and effective. The approach can be implemented with the deep learning toolbox Keras in 100 lines. Finally, to evaluate our approach, we computed the average and standard deviation of DIADEM metric from the ground truth results to our tracing results, and from our tracing results to the ground truth results. Extensive experimental results on the collected dataset demonstrate that the proposed approach performs well in Brainbow labeled mouse brain images.

neuroscience↗

bioRxiv2017

Neuronal tracing and analysis by multispectral tracing in densely labeled mouse brain

Accurate and complete neuronal wiring diagrams are necessary for understanding brain function at many scales from long-range interregional projections to microcircuits. Traditionally, light microscopy-based anatomical reconstructions use monochromatic labeling and therefore necessitate sparse labeling to eliminate tracing ambiguity between intermingled neurons. Consequently, our knowledge of neuronal morphology has largely been based on averaged estimations across many samples. Recently developed second-generation Brainbow tools promise to circumvent this limitation by revealing fine anatomical details of many unambiguously identifiable neurons in densely labeled samples. Yet, a means to quantify and analyze the information is currently lacking. Therefore, we developed nTracer, an ImageJ plugin capable of rapidly and accurately reconstructing whole-cell morphology of large neuronal populations in densely labeled brains.

neuroscience↗