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Flotte, T.

Publications and source records attributed to Flotte, T..

2 recordsLinked to original sources

A Compact Base Editor Rescues AATD-associated Liver and Lung Disease in Mouse Models

Alpha-1 antitrypsin deficiency (AATD) is commonly caused by a G-to-A mutation in the SERPINA1 gene (the PiZ mutation). The mutant PiZ AAT protein is sequestered in hepatocytes, causing lung emphysema due to insufficient AAT protein to inhibit neutrophil elastase in the lung. Here we show that a compact adenine base editor (ABE) with an evolved Cas9 nickase derived from Neisseria meningitidis (eNme2.C) can be packaged in a single AAV and correct the PiZ mutation in mouse models of AATD. An all-in-one eNme2.C-TadA8e/guide 2 plasmid achieved approximately 20% on-target editing in PiZ reporter cells. TadA9e, which has a narrower editing window than TadA8e, reduced bystander editing without significantly affecting the on-target edit. In PiZ transgenic mice, eNme2.C-TadA9e AAV showed approximately 23% editing efficiency after 8 weeks and reduced liver disease burden in treated mice. In a new AAT-null;PiZ transgenic mouse model, ABE restored serum levels of AAT to beyond the 570 g/mL therapeutic level. Moreover, ABE treatment was able to significantly correct lung functions in AAT-null;PiZ animals with emphysema. This study demonstrates the feasibility of an eNme2.C-based ABE in a single AAV to treat both AATD-associated liver and lung disease.

bioengineering↗

Robust Hierarchical Density Estimation and Regression for Re-stained Histological Whole Slide Image Co-registration

For many disease conditions, tissue samples are colored with multiple dyes and stains to add contrast and location information for specific proteins to accurately identify and diagnose disease. This presents a computational challenge for digital pathology, as whole-slide images (WSIs) need to be properly overlaid (i.e. registered) to identify co-localized features. Traditional image registration methods sometimes fail due to the high variation of cell density and insufficient texture information in WSIs - particularly at high magnifications. In this paper, we proposed a robust image registration strategy to align re-stained WSIs precisely and efficiently. This method is applied to 30 pairs of immunohistochemical (IHC) stains and their hematoxylin and eosin (H&E) counterparts. Our approach advances the existing methods in three key ways. First, we introduce refinements to existing image registration methods. Second, we present an effective weighting strategy using kernel density estimation to mitigate registration errors. Third, we account for the linear relationship across WSI levels to improve accuracy. Our experiments show significant decreases in registration errors when on matching IHC and H&E pairs, enabling subcellular-level analysis on stained and re-stained histological images. We also provide a tool to allow users to develop their own registration benchmarking experiments.

bioinformatics↗