Biology subjects

Buchser, W. J.

Publications and source records attributed to Buchser, W. J..

6 recordsLinked to original sources

bioRxiv2025

Subtle Cellular Phenotypes Inform Pathological and Benign Genetic Mutants in the Iduronidase-2 Sulfatase Gene

As with most enzyme-storage disorders, DNA testing at an early age is critical for identifying genetic variants and their impact on disease burden. Still, most variants in genes such as Iduronate-2-sulfatase, IDS, are Variants of Uncertain Significance (VUS). In patients presenting with Hunter Syndrome, clinical testing for IDS enzyme activity has been the mainstay to determine whether a variant is likely damaging. However, these enzyme assays are unable to predict disease severity or neuronal toxicity and may be missing many aspects of IDS pathology. In this study, we have developed an image-based assay using genome-engineered cells with IDS mutations to identify if a specific mutation causes lysosomal and membrane disruptions that characterize the disease. Specifically, we generate twelve mutant cell lines and document both the biochemical changes in IDS activity and the reproducible phenotypic differences therein. Next, we examine two patient-derived cell lines and find the same phenotypic differences in these lines compared to parental controls. The phenotypic changes are measured on a specific scale, which we term the PathScoreLC. To determine whether the observed changes are specific to IDS, we reintroduce a recombinant version of the IDS enzyme (rhIDS) to rescue both the biochemical and phenotypic changes of these cells. Interestingly, even after 120 hours, rescue is present, but not all the cells return to normal, which may also reflect the pathological state. Finally, we examine the gene expression differences and find that recombinant enzyme is not sufficient to induce transcriptional changes in the mutant lines at the time points studied. Overall, these cell-based lysosomal and membrane phenotypes may be key to quickly and accurately profiling clinical variants in the IDS gene.

cell biology↗

bioRxiv2024

Biophysical Simulation Enables Multi-Scale Segmentation and Atlas Mapping for Top-Down Spatial Omics of the Nervous System

Spatial omics (SO) has produced high-definition mapping of subcellular molecules (like transcripts or proteins) within tissue samples. Mapping transcripts to anatomical regions requires segmentation, but even segmenting nuclei remains challenging for tissues like nerve cross sections, let alone for larger regions such as within the spinal cord. Neural networks could address this but need extensive human annotations--a bottleneck. We present SiDoLa-NS (Simulate, Dont Label - Nervous System), an image-driven (top-down) approach to SO analysis in the nervous system. We utilize biophysical properties of tissue architectures to design synthetic images mimicking tissue samples. With these in silico samples, we train supervised instance segmentation convolutional neural networks (CNNs) for nucleus segmentation, achieving precision and F1-scores > 0.95. We take this a step further with generalizable CNNs that can identify macroscopic tissue structures in the mouse brain (mAP50 = 0.869), spinal cord (mAP50 = 0.96), and pig sciatic nerve (mAP50 = 0.995). Short SummaryThe SiDoLa-NS micro-, meso-, and macro-scale models are generalizable, supervised CNNs for neuronal segmentation in cell to tissue-level contexts. SiDoLa-NS is novel in its combination of three core ideas: it is top-down (image first), trained solely on synthetic images, and it is multi-scale. The tool is validated on brain, spinal cord, and sciatic nerve for advanced segmentation tasks.

neuroscience↗

bioRxiv2023

Classification of iPSC-Derived Cultures Using Convolutional Neural Networks to Identify Single Differentiated Neurons for Isolation or Measurement

Understanding neurodegenerative disease pathology depends on a close examination of neurons and their processes. However, image-based single-cell analyses of neurons often require laborious and time-consuming manual classification tasks. Here, we present a machine learning approach leveraging convolutional neural network (CNN) classifiers that have the capability to accurately identify various classes of neuronal images, including single neurons. We developed the Single Neuron Identification Model 20-Class (SNIM20) which was trained on a dataset of induced pluripotent stem cell (iPSC)-derived motor neurons, containing over 12,000 images from 20 distinct classes. SNIM20 is built in TensorFlow and trained on images of differentiated iPSC cultures stained for nuclei and microtubules. This classifier demonstrated high predictive accuracy (AUC = 0.99) for distinguishing single neurons. Additionally, the 2-stage training framework can be used more broadly for cellular classification tasks. A variation was successfully trained on images of a human osteosarcoma cell line (U2OS) for single-cell classification (AUC = 0.99). While this framework was primarily designed for single-cell microraft-based identification and capture, it also works with cells in standard plate formats. We additionally explore the impact of specific fluorescent channels and brightfield images, class groupings, and transfer learning on the quality of the classification. This framework can both assist in high throughput neuronal or cellular identification and be used to train a custom classifier for the users needs.

neuroscience↗

bioRxiv2023

Pathogenic Morphological Signatures of Perturbations in Mitochondrial-Related Genes Revealed by Pooled Imaging Assay

Mutations in mitochondrial-related genes underlie numerous neurodegenerative diseases, yet the significance of most variants remains uncertain concerning disease phenotypes. Several thousand genes have been shown to regulate mitochondria in eukaryotic cells, but which of these genes are necessary for proper mitochondrial dynamics? We investigated the degree of morphological disruptions in mitochondrial gene-silenced cells to understand the magnitude of genetic contribution to properly functioning mitochondria and to identify pathogenic variants. We analyzed 5,835 gRNAs in a high dimensional phenotypic dataset produced by the image-based pooled analysis platform Raft-Seq. Using the MFN2-mutant cell phenotype, we identified several genes, including TMEM11, TIMM8A, and three NADH Ubiquinone proteins, as crucial for normal mitochondrial morphology in human U2OS cells. Additionally, we found several missense and UTR variants within the genes SLC25A19 and ATAD3A as drivers of mitochondrial aggregation. By examining multiple features instead of a single readout, this analysis was powered to detect genes which had morphological signatures aligned with MFN2-mutant phenotypes. Reanalysis with anomaly detection revealed other critical genes, including APOOL, MCEE, NIT, PHB, and SLC16A7, which perturb mitochondrial network morphology in a manner divergent from MFN2. These studies offer insights into the molecular basis for mitochondrial dysfunction, setting the stage for new genomic diagnostics and therapeutic discovery.

genetics↗

bioRxiv2021

Mitochondrial Phenotypes Distinguish Pathogenic MFN2 Mutations by Pooled Functional Genomics Screen

Most human genetic variation is classified as VUS - variants of uncertain significance. While advances in genome editing have allowed innovation in pooled screening platforms, many screens deal with relatively simple readouts (viability, fluorescence) and cannot identify the complex cellular phenotypes that underlie most human diseases. In this paper, we present a generalizable functional genomics platform that combines high-content imaging, machine learning, and microraft isolation in a new method termed "Raft-Seq". We highlight the efficacy of our platform by showing its ability to distinguish pathogenic point mutations of the mitochondrial regulator MFN2, even when the cellular phenotype is subtle. We also show that our platform achieves its efficacy using multiple cellular features, which can be configured on-the-fly. Raft-Seq enables a new way to perform pooled screening on sets of mutations in biologically relevant cells, with the ability to physically capture any cell with a perturbed phenotype and expand it clonally, directly from the primary screen. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=121 SRC="FIGDIR/small/434746v2_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@c774b4org.highwire.dtl.DTLVardef@bee63corg.highwire.dtl.DTLVardef@fd7099org.highwire.dtl.DTLVardef@eb7d87_HPS_FORMAT_FIGEXP M_FIG C_FIG Here, we address the need to evaluate the impact of numerous genetic variants. This manuscript depicts the methods of using machine learning on a biologically relevant phenotype to predict specific point mutations, followed by physically capturing those mutated cells.

genomics↗

bioRxiv2020

Modulating Intra-Nuclear LC3 with Small Molecules Rescues Cells from a Docetaxel-Induced Phenotype

Nucleus-associated autophagy has been described as a cellular metabolic response by which nuclear material is actively degraded. This degradation occurs after stress, such as nuclear damage or the onset of tumorigenesis. Here we describe a nucleus-associated autophagic process distinct from other forms of selective autophagy in human cell lines. We found that although nuclear localization of MAP1LC3B (LC3) is not dependent on particular nuclear importins, knockdown of nuclear importins, which causes nuclear stress, can induce a nuclear autophagic response. Our characterization of this autophagic phenomenon was facilitated by chemical modulation of the process via two compounds discovered previously in a high content analysis. These small molecules bidirectionally regulate nuclear LC3 in human renal, pancreatic, and bladder cell lines. One molecule (NSC31762 or DTEP) enhances nuclear LC3 puncta and increases lysosomal targeting of LC3. This compound also decreases the nuclear envelope protein LaminB1. Another molecule (NSC279895 or DIHI) reduces the nuclear localization of LC3. Finally, we applied these chemical tools in the setting of mitotic-disruptor induced nuclear stress. The compound DIHI, shown to reduce nuclear autophagic puncta, diminished the mitotic disruptor effect. These new tools will allow for deeper exploration of nucleus-associated autophagies, and could serve as proof-of-principle in guiding new therapies for diseases involving nuclear stress.

molecular biology↗