Dataset Information

Semi-automated workflows to quantify AAV transduction in various brain areas and predict gene editing outcome for neurological disorders.

ABSTRACT: One obstacle to the development of gene therapies for the central nervous system is the lack of workflows for quantifying transduction efficiency in affected neural networks and ultimately predicting therapeutic potential. We integrated data from a brain cell atlas with 3D or 2D semi-automated quantification of transduced cells in segmented images to predict AAV transduction efficiency in multiple brain regions. We used this workflow to estimate the transduction efficiency of AAV2/rh.10 and AAV2.retro co-injection in the corticostriatal network affected in Huntington's disease. We then validated our pipeline in gene editing experiments targeting both human and mouse huntingtin genes in transgenic and wild-type mice, respectively. Our analysis predicted that 54% of striatal cells and 7% of cortical cells would be edited in highly transduced areas. Remarkably, in the treated animals, huntingtin gene inactivation reached 54.5% and 9.6%, respectively. These results demonstrate the power of this workflow to predict transduction efficiency and the therapeutic potential of gene therapies in the central nervous system.

SUBMITTER: Duarte F

PROVIDER: S-EPMC10120313 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Semi-automated workflows to quantify AAV transduction in various brain areas and predict gene editing outcome for neurological disorders.

Duarte Fábio F Ramosaj Mergim M Hasanovic Ed E Regio Sara S Sipion Melanie M Rey Maria M Déglon Nicole N

Molecular therapy. Methods & clinical development 20230327

One obstacle to the development of gene therapies for the central nervous system is the lack of workflows for quantifying transduction efficiency in affected neural networks and ultimately predicting therapeutic potential. We integrated data from a brain cell atlas with 3D or 2D semi-automated quantification of transduced cells in segmented images to predict AAV transduction efficiency in multiple brain regions. We used this workflow to estimate the transduction efficiency of AAV2/rh.10 and AAV2 ...[more]

PMID: 37090478

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Project description:BackgroundSurveillance of surgical site infections (SSIs) is essential for better prevention. We developed a screening method for SSIs in adults.MethodsThe training dataset included data from patients who underwent orthopedic surgeries (N = 1,090), colorectal surgeries (N = 817), and abdominal hysterectomies (N = 523) during 2015-2018. The gold standard for the validation of the screening tool was the presence of SSI as determined by a trained infection control practitioner, via manual full medical record review, using the US Center for Disease Control and Prevention criteria. Using multivariable regression models, we identified the correlates of SSI. Patients who had at least one of these correlates were classified as likely to having SSI and those who did not have any of the correlates were classified as unlikely to have SSI. We calculated the sensitivity and specificity of this tool compared to the gold standard and applied the tool to a validation dataset (N = 1,310, years 2019-2020).ResultsSSI was diagnosed by an infection control specialist in 8.2, 5.2, and 31.2% of the patients in the training dataset who underwent hysterectomies, orthopedic surgeries and colorectal surgeries, respectively, vs. 6.2, 6.6, and 25.5%, respectively, in the validation dataset. The correlates of SSI after abdominal hysterectomy were prolonged hospitalization, ordering wound or blood culture, emergency room visit and reoperation; in orthopedic surgery, emergency room visit, wound culture, reoperation, and documentation of SSI, and in colorectal surgeries prolonged hospitalization, readmission, and ordering wound or blood cultures. Area under the curve was >90%. The sensitivity and specificity (95% CI) of the screening tool were 98% (88-100) and 58% (53-62), for abdominal hysterectomy, 91% (81-96) and 82% (80-84) in orthopedic surgeries and 96% (90-98) and 62% (58-66) in colorectal surgeries. The corresponding values for the validation dataset were 89% (67-97) and 75% (69-80) in abdominal hysterectomy; 85% (72-93) and 83% (80-86) in orthopedic surgeries and 98% (93-99) and 59% (53-64) in colorectal surgeries. The number of files needed to be fully reviewed declined by 61-66.ConclusionThe presented semi-automated simple screening tool for SSI surveillance had good sensitivity and specificity and it has great potential of reducing workload and improving SSI surveillance.

Dataset Information

Semi-automated workflows to quantify AAV transduction in various brain areas and predict gene editing outcome for neurological disorders.

Publications

Semi-automated workflows to quantify AAV transduction in various brain areas and predict gene editing outcome for neurological disorders.

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