Biology subjects

Milbrandt, J. D.

Publications and source records attributed to Milbrandt, J. D..

3 recordsLinked to original sources

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

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↗