Biology subjects

Walker, L. A.

Publications and source records attributed to Walker, L. A..

At least 19 recordsLinked to original sources

bioRxiv2025

A parallelly distributed microscope and software system for scalable high-throughput multispectral 3D imaging

Recent advances in high throughput optical microscopy have achieved whole-organism scale imaging at diffraction-limited resolutions. Current microscopes, however, require making compromises between achieving the optimal resolution, imaging depth, multispectral capability, and data throughput due to limitations in optical design and data stream handling. We have created a parallel-line scanning confocal microscope (plSCM) which provides 1.1 Gigavoxels/second high speed imaging while achieving an optical resolution of [~]180 x 220 x 650 nm with 2 millimeters of imaging depth in 3 simultaneous spectral channels. To handle such a massive imaging data stream, we have engineered a scalable network-distributed image acquisition/processing framework (SNDiF), which allows continuous capture, real-time processing, and cluster storage of petabyte-scale single image datasets in days. Together, our parallelly distributed microscope and software system presents a general solution for large-scale high-throughput and high-resolution multicolor imaging which can operate for days at a time.

bioengineering↗

bioRxiv2024

A browser-based platform for storage, visualization, and analysis of large-scale 3D images in HPC environments

High-throughput microscopy necessitates 3D image storage, visualization, and analysis in terabyte to petabyte scales. Centralized high-performance computing (HPC) infrastructure provides the resources, but interfacing software is limited. We developed an extensible platform with a scalable image storage format (SISF) and content delivery network (CDN) to enable fast, random access to compressed data. Additionally, we built a cloud-based nTracer2 software to annotate neuron morphology from SISF images in a web browser.

bioinformatics↗

bioRxiv2024

Artifact-Minimized High-Ratio Image Compression with Preserved Analysis Fidelity

Recent advances in microscopy have pushed imaging data generation to an unprecedented scale. While scientists benefit from higher spatiotemporal resolutions and larger imaging volumes, the increasing data size presents significant storage, visualization, sharing, and analysis challenges. Lossless compression typically reduces the data size by <4 fold, whereas lossy compression trades smaller data size for the loss of a precise reconstruction of the original data. Here, we develop a novel quantization method and an artifact metric for automated compression parameter optimization that preserves information fidelity. We show that, when combined with the AV1 video codec, we achieve tens to ten thousand folds of data compression while introducing negligible visual defects or quantification errors in single-molecule localization and segmentation analyses. We developed an HDF5 filter with FFMPEG library support for convenient community adaptation. For instance, HDF5-enabled ImageJ plugins can now be seamlessly extended to support AV1 compression and visualization to handle terabyte-scale images.

bioinformatics↗

bioRxiv2023

Dynamic Microenvironmental Stiffness and Serum Composition Modulate Sex-Specific Pulmonary Artery Adventitial Fibroblast Activation

Respiratory diseases like pulmonary arterial hypertension (PAH) frequently exhibit sexual dimorphism. Female PAH patients are more susceptible to the disease but have increased survival rates. This phenomenon is known as the estrogen paradox, and the underlying mechanisms are not fully understood. During PAH progression in vivo, human pulmonary arterial adventitial fibroblasts (hPAAFs) differentiate into an activated phenotype. These cells produce excess, aberrant extracellular matrix proteins that stiffen the surrounding pulmonary arterial tissues. Here, we employed dynamic poly(ethylene glycol)-alpha methacrylate (PEGMA)-based biomaterials to study how the age and sex of human serum influenced hPAAF activation in response to microenvironmental stiffening in vitro. Results showed female and male cells responded differently to increases in microenvironmental stiffness and serum composition. Male hPAAFs were less activated than female cells on soft hydrogels and more responsive to increases in microenvironmental stiffness regardless of serum composition. Female hPAAF activation followed this pattern only when cultured in younger (age < 50) female serum or when older (age [≥] 50) female serum was supplemented with estradiol. Otherwise, female hPAAF activation was relatively high on both soft and stiffened hydrogels, with little difference in activation between the two conditions. Collectively, these results suggest that it may be possible to model the estrogen paradox observed in PAH in vitro and that it is critical for researchers to report cell sex and serum source when conducting in vitro experimentation.

bioengineering↗

bioRxiv2023

A Comparison of Lossless Compression Methods in Microscopy Data Storage Applications

Modern high-throughput microscopy methods such as light-sheet imaging and electron microscopy are capable of producing petabytes of data inside of a single experiment. Storage of these large images, however, is challenging because of the difficulty of moving, storing, and analyzing such vast amounts of data, which is often collected at very high data rates (>1GBps). In this report, we provide a comparison of the performance of several compression algorithms using a collection of published and unpublished datasets including confocal, fMOST, and pathology images. We also use simulated data to demonstrate the efficiency of each algorithm as image content or entropy increases. As a result of this work, we recommend the use of the BLOSC algorithm combined with ZSTD for various microscopy applications, as it produces the best compression ratio over a collection of conditions. CCS CONCEPTS* Applied computing [->] Bioinformatics; Imaging.

bioinformatics↗

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↗

bioRxiv2022

Profiling neurons surrounding subcellular-scale carbon fiber electrode tracts enables modeling recorded signals

Characterizing the relationship between neuron spiking and the signals electrodes record is vital to defining the neural circuits driving brain function and informing computational modeling. However, electrode biocompatibility and precisely localizing neurons around the electrodes are critical to defining this relationship. Here, we show the ability to localize post-explant recording tips of subcellular-scale carbon fiber electrodes and surrounding neurons. Immunostaining of astrocyte, microglia, and neuron markers confirmed improved tissue health. While neurons near implants were stretched, their number and distribution were similar to control, suggesting that these minimally invasive electrodes demonstrate the potential to sample naturalistic neural populations. This motivated prediction of the spikes produced by neurons nearest to the electrodes using a model fit with recorded electrophysiology. These simulations show the first direct evidence that neuron placement in the immediate vicinity of the recording site influences how many spike clusters can be reliably identified by spike sorting.

neuroscience↗

bioRxiv2021

Robust transcriptional profiling and identification of differentially expressed genes with low input RNA sequencing of adult hippocampal neural stem and progenitor populations

Multipotent neural stem cells (NSCs) are found in several isolated niches of the adult mammalian brain where they have unique potential to assist in tissue repair. Modern transcriptomics offer high-throughput methods for identifying disease or injury associated gene expression signatures in endogenous adult NSCs, but they require adaptation to accommodate the rarity of NSCs. Bulk RNA sequencing (RNAseq) of NSCs requires pooling several mice, which impedes application to labor-intensive injury models. Alternatively, single cell RNAseq can profile hundreds to thousands of cells from a single mouse and is increasingly used to study NSCs. The consequences of the low RNA input from a single NSC on downstream identification of differentially expressed genes (DEGs) remains largely unexplored. Here, to clarify the role that low RNA input plays in NSC DEG identification, we directly compared DEGs in an oxidative stress model of cultured NSCs by bulk and single cell sequencing. While both methods yielded DEGs that were replicable, single cell sequencing DEGs derived from genes with higher relative transcript counts compared to all detected genes and exhibited smaller fold changes than DEGs identified by bulk RNAseq. The loss of high fold-change DEGs in the single cell platform presents an important limitation for identifying disease-relevant genes. To facilitate identification of such genes, we determined an RNA-input threshold that enables transcriptional profiling of NSCs comparable to standard bulk sequencing and used it to establish a workflow for in vivo profiling of endogenous NSCs. We then applied this workflow to identify DEGs after lateral fluid percussion injury, a labor-intensive animal model of traumatic brain injury. Our work suggests that single cell RNA sequencing may underestimate the diversity of pathologic DEGs but population level transcriptomic analysis can be adapted to capture more of these DEGs with similar efficacy and diversity as standard bulk sequencing. Together, our data and workflow will be useful for investigators interested in understanding and manipulating adult hippocampal NSC responses to various stimuli.

neuroscience↗

bioRxiv2021

Monoclonal antibodies targeting surface exposed epitopes of Candida albicans cell wall proteins confer in vivo protection in an infection model

MAb based immunotherapies targeting systemic and deep-seated fungal infections are still in their early stages of development with currently no licensed antifungal mAbs available for patients at risk. The cell wall glycoproteins of Candida albicans are of particular interest as potential targets for therapeutic antibody generation due to their extracellular location and key involvement in fungal pathogenesis. Here we describe the generation of recombinant human antibodies specifically targeting two key cell wall proteins (CWPs) in C. albicans - Utr2 and Pga31. These antibodies were isolated from a phage display antibody library using peptide antigens representing the surface exposed regions of CWPs expressed at elevated levels during in vivo infection. Reformatted human-mouse chimeric mAbs preferentially recognised C. albicans hyphal forms compared to yeast cells and an increased binding was observed when the cells were grown in the presence of the antifungal agent caspofungin. In J774.1 macrophage interaction assays, mAb pre-treatment resulted in a faster engulfment of C. albicans cells suggesting a role of the CWP antibodies as opsonising agents during phagocyte recruitment. Finally, in a series of clinically predictive, mouse models of systemic candidiasis, our lead mAb achieved an improved survival (83%) and several log reduction of fungal burden in the kidneys, similar to levels achieved for the fungicidal drug caspofungin, and superior to any anti-Candida mAb therapeutic efficacy reported to date.

microbiology↗

bioRxiv2021

Prolactin and the regulation of parental care and helping behavior in cooperatively breeding white-browed sparrow weaver societies

In many cooperatively breeding species non-breeding individuals help to rear the offspring of breeders. The physiological mechanisms that regulate such cooperative helping behavior are poorly understood, but may have been co-opted, during the evolution of cooperative breeding, from pre-existing mechanisms that regulated parental care. Key among these may be a role for prolactin. Here we investigate whether natural variation in circulating prolactin levels predicts both parental and helper contributions to nestling provisioning in cooperatively breeding white-browed sparrow weavers, Plocepasser mahali. In sparrow weaver groups, a single dominant pair monopolize reproduction and non-breeding subordinates help with nestling feeding. We show that: (i) among parents, dominant females feed nestlings at higher rates, make longer provisioning visits, and have higher prolactin levels than dominant males; and (ii) among subordinates, engaged in cooperative helping behavior, those within their natal groups feed nestlings at higher rates and have higher prolactin levels than immigrants. Accordingly, continuous variation in prolactin levels positively predicts nestling-provisioning rates and mean provisioning visit durations when all bird classes are combined. These relationships are principally driven by differences among bird classes in both circulating prolactin levels and provisioning traits. The more limited within-class variation in prolactin and provisioning traits were not evidently correlated, highlighting a likely role for additional mechanisms in the fine-scale regulation of care. Our findings broadly support the hypothesis that parental care and cooperative helping behavior are regulated by a common underlying mechanism and highlight the need for experimentation to now establish the causality of any role for prolactin.

animal behavior and cognition↗

bioRxiv2021

Teaching Python for Data Science: Collaborative development of a modular & interactive curriculum

We are bioinformatics trainees at the University of Michigan who started a local chapter of Girls Who Code to provide a fun and supportive environment for high school women to learn the power of coding. Our goal was to cover basic coding topics and data science concepts through live coding and hands-on practice. However, we could not find a resource that exactly met our needs. Therefore, over the past three years, we have developed a curriculum and instructional format using Jupyter notebooks to effectively teach introductory Python for data science. This method, inspired by The Carpentries organization, uses bite-sized lessons followed by independent practice time to reinforce coding concepts, and culminates in a data science capstone project using real-world data. We believe our open curriculum is a valuable resource to the wider education community and hope that educators will use and improve our lessons, practice problems, and teaching best practices. Anyone can contribute to our educational materials on GitHub.

scientific communication and education↗

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

A multi-informatic description of neural differentiation in the Drosophila type-II neuroblast lineages

To adopt terminal neural fates, neural progenitors respond to a combination of molecular factors, the interplay between which remains difficult to untangle. In this work, we interrogated the type-II neuroblast lineages in the developing Drosophila brain using targeted single-cell mRNA sequencing. We used pseudotime analysis to group cells by their differentiation state and subsequently used marker gene analysis to identify genes that may play a role in neural fate specification. To aid the candidate gene selection process, we created MiCV, a scRNA-seq data visualization web tool that integrates results from the multifaceted analysis, displays the co-expression pattern of multiple genes, and provides an automated gene function curating feature. In situ profiling of these mRNAs further recovered the spatial information lacking in the single-cell data. Combining prior knowledge, in silico analysis, and in situ evidence, we characterize the molecular landscape of the developing Drosophila type-II neuroblast lineages and provide a roadmap for navigating the more complex brains.

neuroscience↗

bioRxiv2020

Unsupervised Neural Tracing in Densely Labeled Multispectral Brainbow Images

Reconstructing neuron morphology is central to uncovering the complexity of the nervous system. That is because the morphology of a neuron essentially provides the physical constraints to its intrinsic electrophysiological properties and its connectivity. Recent advances in imaging technologies generated large quantities of high-resolution 3D images of neurons in the brain. Furthermore, the multispectral labeling technology, Brainbow permits unambiguous differentiation of neighboring neurons in a densely labeled brain, therefore enables for the first time the possibility of studying the connectivity between many neurons from a light microscopy image. However, lack of reliable automated neuron morphology reconstruction makes data analysis the bottleneck of extracting rich informatics in neuroscience. Supervoxel-based neuron segmentation methods have been proposed to solve this problem, however, the use of previous approaches has been impeded by the large numbers of errors which arise in the final segmentation. In this paper, we present a novel unsupervised approach to trace neurons from multispectral Brainbow images, which prevents segmentation errors and tracing continuity errors using two innovations. First, we formulate a Gaussian mixture model-based clustering strategy to improve the separation of segmented color channels that provides accurate skeletonization results for the following steps. Next, a skeleton graph approach is proposed to allow the identification and correction of discontinuities in the neuron tree topology. We find that these innovations allow our approach to outperform current state-of-the-art approaches, which results in more accurate neuron tracing as a tree representation close to human expert annotation.

neuroscience↗

bioRxiv2020

Isoform-specific regulation of HCN4 channels by a family of endoplasmic reticulum proteins

Ion channels in excitable cells function in macromolecular complexes in which auxiliary proteins modulate the biophysical properties of the pore-forming subunits. Hyperpolarization-activated, cyclic nucleotide-sensitive HCN4 channels are critical determinants of membrane excitability in cells throughout the body, including thalamocortical neurons and cardiac pacemaker cells. We previously showed that the properties of HCN4 channels differ dramatically in different cell types, possibly due to the endogenous expression of auxiliary proteins. Here, we report the discovery of a family of endoplasmic reticulum transmembrane proteins that interact with and modulate HCN4. Lymphoid-restricted membrane protein (LRMP, Jaw1) and inositol trisphosphate receptor-associated guanylate kinase substrate (IRAG, Mrvi1, Jaw1L) are homologous proteins with small ER luminal domains and large cytoplasmic domains. Despite their homology, LRMP and IRAG have distinct effects on HCN4. LRMP is a loss-of-function modulator that inhibits the canonical depolarizing shift in the voltage-dependence of HCN4 activation in response to binding of cAMP. In contrast, IRAG causes a gain of HCN4 function by depolarizing the basal voltage-dependence of activation in the absence of cAMP. The mechanisms of action of LRMP and IRAG are novel; they are independent of trafficking and cAMP binding, and they are specific to the HCN4 isoform. We also found that IRAG is highly expressed in the mouse sinoatrial node where computer modeling predicts that its presence increases HCN4 availability. Our results suggest important roles for LRMP and IRAG in regulation of cellular excitability and as tools for advancing mechanistic understanding of HCN4 channel function. Significance statementThe pore-forming subunits of ion channels are regulated by auxiliary interacting proteins. Hyperpolarization-activated cyclic nucleotide-sensitive isoform 4 (HCN4) channels are critical determinants of electrical excitability in many types of cells including neurons and cardiac pacemaker cells. Here we report the discovery of two novel HCN4 regulatory proteins. Despite their homology, the two proteins -- lymphoid-restricted membrane protein (LRMP) and inositol trisphosphate receptor-associated guanylate kinase substrate (IRAG) -- have opposing effects on HCN4, causing loss- and gain-of-function, respectively. LRMP and IRAG are expected to play critical roles in regulation of physiological processes ranging from wakefulness to heart rate through their modulation of HCN4 channel function.

physiology↗

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↗

bioRxiv2020

Light microscopy based approach for mapping connectivity with molecular specificity

Mapping neuroanatomy is a foundational goal towards understanding brain function. Electron microscopy (EM) has been the gold standard for connectivity analysis because nanoscale resolution is necessary to unambiguously resolve chemical and electrical synapses. However, molecular information that specifies cell types is often lost in EM reconstructions. To address this, we devised a light microscopy approach for connectivity analysis of defined cell types called spectral connectomics. We combined multicolor genetic labeling (Brainbow) of neurons with a multi-round immunostaining Expansion Microscopy (miriEx) strategy to simultaneously interrogate morphology, molecular markers, and connectivity in the same brain section. We applied our multimodal profiling strategy to directly link inhibitory neuron cell types with their network morphologies. Furthermore, we showed that correlative Brainbow and endogenous synaptic machinery immunostaining can be used to define putative synaptic connections between spectrally unique neurons, as well as map putative inhibitory and excitatory inputs. We envision that spectral connectomics can be applied routinely in neurobiology labs to gain insights into normal and pathophysiological neuroanatomy across multiple animals and time points.

neuroscience↗

bioRxiv2020

Maturation of human induced pluripotent stem cell-derived cardiomyocytes for modeling hypertrophic cardiomyopathy

RationaleHuman induced pluripotent stem cell derived cardiomyocytes (hiPSC-CMs) are a powerful platform for biomedical research. However, they are immature, which is a barrier to modeling adult-onset cardiovascular disease. ObjectiveWe sought to develop a simple method which could drive cultured hiPSC-CMs towards maturity across a number of phenotypes. Methods and resultsCells were cultured in fatty acid-based media and plated on micropatterned surfaces to promote alignment and elongation. These cells display many characteristics of adult human cardiomyocytes, including elongated cell morphology, enhanced maturity of sarcomeric structures, metabolic behavior, and increased myofibril contractile force. Most notably, hiPSC-CMs cultured under optimal maturity-inducing conditions recapitulate the pathological hypertrophy caused by either a pro-hypertrophic agent or genetic mutations. ConclusionsThe more mature hiPSC-CMs produced by the methods described here will serve as a useful in vitro platform for characterizing cardiovascular disease.

cell biology↗