Biology subjects

Clark, N. A.

Publications and source records attributed to Clark, N. A..

5 recordsLinked to original sources

bioRxiv2025

A toolbox for navigating and analyzing the spatiotemporal properties of retinal waves

The precise development of the visual system is driven by retinal waves, which are bursts of spontaneous activity that propagate across retinal neurons in a wave-like fashion. In mice, retinal waves begin embryonically and continue until eye opening at the end of the second postnatal week. During this time, the mechanisms for generating and propagating retinal waves are changing, thus causing retinal waves to exhibit highly dynamic spatiotemporal properties from one day to the next. Critically, the spatiotemporal properties of retinal waves have been shown to instruct the development of the visual system, including eye-specific segregation, retinotopic mapping, direction selectivity, and potentially retinal vascularization. Currently, there is no method for the automatic detection and high-throughput quantitative analysis of the spatiotemporal properties of retinal waves. To overcome this barrier, we developed WaveMiner, an automated, high-throughput toolbox for detecting, segregating, and quantifying retinal waves from microelectrode-array (MEA) and calcium-imaging recordings. After first validating our toolbox, we use it to uncover novel dynamic spatiotemporal properties of retinal waves in the first two postnatal weeks. We also use this toolbox to analyze ultra long physiological recordings, revealing that waves exhibit both stable and dynamic spatiotemporal properties on an hourly basis. Finally, we demonstrate that this toolbox can detect waves in the presence of pharmacological agents that increase the baseline firing of neurons, enabling the discovery of novel factors that perturb the spatiotemporal properties of retinal waves and visual development. In summary, WaveMiner is a platform to standardize the detection and quantification of retinal waves across recording modalities and experimental conditions, enabling novel discoveries about their dynamic spatiotemporal properties and the factors that govern them.

neuroscience↗

bioRxiv2024

Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action

Treatments for tuberculosis remain lengthy, motivating a search for new drugs with novel mechanisms of action. However, it remains challenging to elucidate the direct targets of a drug, and even more so, to determine which disrupted cellular processes lead to bacterial killing. We developed a computational tool, DECIPHAER (DEcoding Cross-modal Information of PHarmacologies via AutoEncodeRs), to select the important correlated transcriptional and morphological responses of Mycobacterium tuberculosis to drug treatments. By finding a reduced feature space from these measurements, DECIPHAER highlighted essential features of Mtb cellular damage such as phosphosugar stress and inhibition of translation and DNA replication. After training, DECIPHAER provides cell-death-relevant insight into single-modal datasets, enabling interrogation of drug treatment responses for which transcriptional data are unavailable. Using morphological data alone with DECIPHAER, we discovered that respiration inhibition by the poly-pharmacological drugs, SQ109 and BM212, can influence cell death more than their effects on the cell wall. This study demonstrates that DECIPHAER can extract the critical shared information from multi-modal measurements to identify cell death-relevant mechanisms of TB drugs.

microbiology↗

bioRxiv2024

Development and Organization of the Retinal Orientation Selectivity Map

Orientation or axial selectivity, the property of neurons in the visual system to respond preferentially to certain angles of a visual stimuli, plays a pivotal role in our understanding of visual perception and information processing. This computation is performed as early as the retina, and although much work has established the cellular mechanisms of retinal orientation selectivity, how this computation is organized across the retina is unknown. Using a large dataset collected across the mouse retina, we demonstrate functional organization rules of retinal orientation selectivity. First, we identify three major functional classes of retinal cells that are orientation selective and match previous descriptions. Second, we show that one orientation is predominantly represented in the retina and that this predominant orientation changes as a function of retinal location. Third, we demonstrate that neural activity plays little role on the organization of retinal orientation selectivity. Lastly, we use in silico modeling followed by validation experiments to demonstrate that the overrepresented orientation aligns along concentric axes. These results demonstrate that, similar to direction selectivity, orientation selectivity is organized in a functional map as early as the retina. One Sentence SummaryDevelopment and organization of retinal orientation selectivity

neuroscience↗

bioRxiv2022

Integrative signatures of signaling pathway response increase robustness and accuracy of pathway predictions

MotivationAberrant cell signaling is known to drive progression of cancer and many other diseases. The study of signaling pathways within cells is central to identifying drugs that seek to modulate these pathways. Expression of pathway genes (i.e. genes that code for pathway proteins) correlates poorly with signaling pathway activity, making prediction of signaling pathway activity changes based on transcriptional disease signatures a challenging problem. Pathway architecture and response also varies across cell lines, which reflects how drug response varies across a patient population. ResultsHere, we present a transcriptional footprinting framework for predicting changes in activity of signaling pathway by integrating transcriptional signatures of genetic perturbations of pathway genes over a diverse set of cell lines into a integrative Pathway Activity Signature (iPAS). We use an unsupervised multi-task learning approach to create pathway signatures across 12 cell lines using genetic loss of function data from the LINCS project. We also use supervised learning to construct an optimal predictor based on the ensemble of 12 cell line signatures. Our methods achieve a sizeable increase in performance, as measured by prediction of pathways targeted by LINCS chemical perturbagens. AvailabilityOpen source R package iPAS is available at https://github.com/uc-bd2k/iPAS. Contactmedvedm@ucmail.uc.edu Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2019

Connecting omics signatures of diseases, drugs, and mechanisms of actions with iLINCS

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Among these, iLINCS is unique in scope and versatility of the data provided and the analytics facilitated. iLINCS (http://ilincs.org) is an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates analysis of user-submitted omics signatures of diseases and cellular perturbations in the context of a large compendium of pre-computed signatures (>200,000), as well as mining and re-analysis of the large collection of omics datasets (>12,000), pre-computed signatures, and their connections. Analytics workflows driven by user-friendly interfaces enable users with only conceptual understanding of the analysis strategy to execute sophisticated analyses of omics signatures, such as systems biology analyses and interpretation of signatures, mechanism of action analysis, and signature-driven drug repositioning. In summary, iLINCS workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures.

bioinformatics↗