Biology subjects

Heigl, T.

Publications and source records attributed to Heigl, T..

3 recordsLinked to original sources

bioRxiv2026

Gene Supplementation of MYO7A or activation of Myo7b for treatment of Usher syndrome 1B

Mutations in MYO7A result in the most severe subtype of Usher syndrome, the leading genetic cause of deafblindness. The large size of MYO7A requires dual adeno-associated virus (AAV) vectors for gene transfer or alternative methods to treat retinal defects. Here, we evaluated two treatment approaches: i) Supplementation of the human MYO7A gene via dual mRNA trans-splicing AAVs, and ii) CRISPR/Cas-mediated activation of the related murine Myo7b gene. Upon MYO7A supplementation, the transgenic MYO7A transcript and protein were expressed and correctly localized in retinal pigment epithelial (RPE) and photoreceptors of mice, pigs, and human retinal organoids. In RPE-and photoreceptor-specific Myo7a knockout mice, we could restore MYO7A expression and localization of melanosomes in RPE cells to wild-type levels. Myo7b activation led to partial restoration of melanosome localization, and the localization of MYO7B protein was largely comparable to MYO7A. These findings indicate that both approaches are in principle suitable for the therapy of Usher syndrome.

molecular biology↗

bioRxiv2024

CRISPRa-mediated activation of genes associated with inherited retinal dystrophies in acutely isolated human cells for diagnostic purposes

Many patients suffering from inherited diseases do not receive a genetic diagnosis and are therefore excluded as candidates for treatments, such as gene therapies. Analyzing disease-related gene transcripts from patient cells would improve detection of mutations that have been missed or misinterpreted in terms of pathogenicity during routine genome sequencing. However, the analysis of transcripts is complicated by the fact that a biopsy of the affected tissue is often not appropriate, and many disease-associated genes are not expressed in tissues or cells that can be easily obtained from patients. Here, using CRISPR/Cas-mediated transcriptional activation (CRISPRa) we developed a robust and efficient approach to activate genes in skin-derived fibroblasts and in freshly isolated peripheral blood mononuclear cells (PBMCs) from healthy individuals. This approach was successfully applied to blood samples from patients with inherited retinal dystrophies (IRD). We were able to efficiently activate several IRD-linked genes and detect the corresponding transcripts using different diagnostically relevant methods such as RT-qPCR, RT-PCR and long- and short-read RNA sequencing. The detection and analysis of known and unknown mRNA isoforms demonstrates the potential of CRISPRa-mediated transcriptional activation in PBMCs. These results will contribute to ceasing the critical gap in the genetic diagnosis of patients with IRD or other inherited diseases. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/625601v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@60e2b8org.highwire.dtl.DTLVardef@c4c742org.highwire.dtl.DTLVardef@f5c28dorg.highwire.dtl.DTLVardef@b7cf11_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗

bioRxiv2023

Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages

Single-cell RNA-seq (scRNA-seq) studies have profiled over 100 million human cells across diseases, developmental stages, and perturbations to date. A singular view of this vast and growing expression landscape could help reveal novel associations between cell states and diseases, discover cell states in unexpected tissue contexts, and relate in vivo cells to in vitro models. However, these require a common, scalable representation of cell profiles from across the body, a general measure of their similarity, and an efficient way to query these data. Here, we present SCimilarity, a metric learning framework to learn and search a unified and interpretable representation that annotates cell types and instantaneously queries for a cell state across tens of millions of profiles. We demonstrate SCimilarity on a 22.7 million cell corpus assembled across 399 published scRNA-seq studies, showing accurate integration, annotation and querying. We experimentally validated SCimilarity by querying across tissues for a macrophage subset originally identified in interstitial lung disease, and showing that cells with similar profiles are found in other fibrotic diseases, tissues, and a 3D hydrogel system, which we then repurposed to yield this cell state in vitro. SCimilarity serves as a foundational model for single cell gene expression data and enables researchers to query for similar cellular states across the entire human body, providing a powerful tool for generating novel biological insights from the growing Human Cell Atlas.

bioinformatics↗