Biology subjects

Jackson, C. R.

Publications and source records attributed to Jackson, C. R..

3 recordsLinked to original sources

bioRxiv2026

Defining a Midgestational Window for In Utero Genome Editing of the Fetal Murine Cortex

Congenital disorders of cortical development arise from genetic lesions that disrupt neurogenesis and neuronal migration. Unfortunately, tools to model or correct these defects before birth are limited. Here we establish a platform for systemic in utero gene delivery and genome editing in the mouse cortex at midgestation. By microdissecting a uterine window over the vitelline vein at embryonic day 12.5 (E12.5), we achieve fetal circulation access, enabling robust AAV-mediated transduction of the central nervous system (CNS) while reducing off-target expression in peripheral organs. Barcoded capsid screens reveal that AAV9 exhibits developmental stage-dependent tropism, with higher CNS penetrance and lower liver transduction at E12.5 than at E15.5. Leveraging this window, we provide a proof-of-concept of efficient cortical editing, using Cre-lox and CRISPR/Cas9 strategies to recapitulate prenatal reeler-like cortical misordering phenotypes following Reln knockout. We further use homology-directed repair to demonstrate precise genome modification, epitope-tagging the endogenous Reln and Actb loci, and installing a human-derived pathogenic allele of PDHA1. Importantly, we show that edited cells span neural progenitors and differentiated neurons across the cortex and hippocampus. These results define a permissive midgestational window for prenatal genome editing, providing a platform for functional modeling of congenital CNS disorders and exploration of early therapeutic interventions with minimized peripheral exposure.

neuroscience↗

bioRxiv2020

PathFlow-MixMatch for Whole Slide Image Registration: An Investigation of a Segment-Based Scalable Image Registration Method

Image registration involves finding the best alignment between different images of the same object. In these tasks, the object in question is viewed differently in each of the images (e.g. different rotation or light conditions, etc.). In digital pathology, image registration aligns correspondent regions of tissue from different stereotactic viewpoints (e.g. subsequent deeper sections of the same tissue). These comparisons are important for histological analysis and can facilitate previously unavailable manipulations, such as 3D tissue reconstruction and cell-level alignment of immunohistochemical (IHC) and special stains. Several benchmarks have been established for evaluating image registration techniques for histological tissue; however, little work has evaluated the impact of scaling registration techniques to Giga-Pixel Whole Slide Images (WSI), which are large enough for significant memory limitations, and contain recurrent patterns and deformations that hinder traditional alignment algorithms. Furthermore, as tissue sections often contain multiple, discrete, smaller tissue fragments, it is unnecessary to align an entire image when the bulk of the image is background whitespace and tissue fragments orientations are often agnostic of each other. We present a methodology for circumventing large-scale image registration issues in histopathology and accompanying software. By removing background pixels, parsing the slide into discrete tissue segments, and matching, orienting and registering smaller segment pairs, we recovered registrations with lower Target Registration Error (TRE) when compared to utilizing the unmanipulated WSI. We tested our technique by having a pathologist annotate landmarks from 13 pairs of differently stained liver biopsy slides, performing WSI and segment-based registration techniques, and comparing overall TRE. Preliminary results demonstrate superior performance of registering segment pairs versus registering WSI (difference of median TRE of 44 pixels, p<0.001). Segment matching within WSI is an effective solution for histology image registration but requires further testing and validation to ensure its viability for stain translation and 3D histology analysis.

pathology↗

bioRxiv2020

Preliminary Evaluation of the Utility of Deep Generative Histopathology Image Translation at a Mid-Sized NCI Cancer Center

Evaluation of a tissue biopsy is often required for the diagnosis and prognostic staging of a disease. Recent efforts have sought to accurately quantitate the distribution of tissue features and morphology in digitized images of histological tissue sections, Whole Slide Images (WSI). Generative modeling techniques present a unique opportunity to produce training data that can both augment these models and translate histologic data across different intra-and-inter-institutional processing procedures, provide cost-effective ways to perform computational chemical stains (synthetic stains) on tissue, and facilitate the creation of diagnostic aid algorithms. A critical evaluation and understanding of these technologies is vital for their incorporation into a clinical workflow. We illustrate several potential use cases of these techniques for the calculation of nuclear to cytoplasm ratio, synthetic SOX10 immunohistochemistry (IHC, sIHC) staining to delineate cell lineage, and the conversion of hematoxylin and eosin (H&E) stain to trichome stain for the staging of liver fibrosis.

pathology↗