👤 Margriet A Huisman

Lysosomes are implicated in a wide spectrum of human diseases, including monogenic lysosomal storage disorders (LSDs), age-associated neurodegeneration, and cancer. Profiling lysosomal content using tag-based lysosomal immunoprecipitation (LysoTagIP) in cell and animal models has substantially moved the field forward, but studying lysosomal dysfunction in patients remains challenging. Here, we report the development of the 'tagless LysoIP' method, designed to enable the rapid enrichment of lysosomes, via immunoprecipitation, using the endogenous integral lysosomal membrane protein TMEM192, directly from clinical samples and human cell lines (e.g., induced pluripotent stem cell-derived neurons). Isolated lysosomes were intact and suitable for subsequent multimodal omics analyses. To validate our approach, we applied the tagless LysoIP to enrich lysosomes from peripheral blood mononuclear cells derived from fresh blood of healthy donors and patients with CLN3 disease, an autosomal recessive neurodegenerative LSD. Metabolic profiling of isolated lysosomes revealed massive accumulation of glycerophosphodiesters (GPDs) in patients' lysosomes. Interestingly, a patient with a milder phenotype and genotype displayed lower accumulation of lysosomal GPDs, consistent with their potential role as disease biomarkers. Altogether, the tagless LysoIP provides a framework to study native lysosomes from patient samples, identify disease biomarkers, and discover human-relevant disease mechanisms. Show less

Fat depth (FD) and muscle depth (MD) are economically important traits and used to estimate carcass lean content (LMP), which is one of the main breeding objectives in pig breeding programmes. We assessed the genetic architectures of body composition traits for additive and dominance effects in commercial crossbred Piétrain pigs using both 50 K array and sequence genotypes. We first performed a genome-wide association study (GWAS) using single-marker association analysis with a false discovery rate of 0.1. Then, we estimated the additive and dominance effects of the most significant variant in the quantitative trait loci (QTL) regions. It was investigated whether the use of whole-genome sequence (WGS) will improve the QTL detection (both additive and dominance) with a higher power compared with lower density SNP arrays. Our results showed that more QTL regions were detected by WGS compared with 50 K array (n = 54 vs. n = 17). Of the novel associated regions associated with FD and LMP and detected by WGS, the most pronounced peak was on SSC13, situated at ~116-118, 121-127 and 129-134 Mbp. Additionally, we found that only additive effects contributed to the genetic architecture of the analysed traits and no significant dominance effects were found for the tested SNPs at QTL regions, regardless of panel density. The associated SNPs are located in or near several relevant candidate genes. Of these genes, GABRR2, GALR1, RNGTT, CDH20 and MC4R have been previously reported as being associated with fat deposition traits. However, the genes on SSC1 (ZNF292, ORC3, CNR1, SRSF12, MDN1, TSHZ1, RELCH and RNF152) and SSC18 (TTC26 and KIAA1549) have not been reported previously to our best knowledge. Our current findings provide insights into the genomic regions influencing composition traits in Piétrain pigs. Show less

Quantifying lymphocyte vacuolization in peripheral blood smears (PBSs) serves as a measure for disease severity in CLN3 disease-a lysosomal storage disorder of childhood-onset. However, thus far quantification methods are based on labor-intensive manual assessment of PBSs. As machine learning techniques like convolutional neural networks (CNNs) have been deployed quite successfully in detecting pathological features in PBSs, we explored whether these techniques could be utilized to automate quantification of lymphocyte vacuolization. Here, we present and validate a deep learning pipeline that automates quantification of lymphocyte vacuolization. By using two CNNs in succession, trained for cytoplasm-segmentation and vacuolization-detection, respectively, we obtained an excellent correlation with manual quantification of lymphocyte vacuolization ( Show less

The CLN3 disease spectrum ranges from a childhood-onset neurodegenerative disorder to a retina-only disease. Given the lack of metabolic disease severity markers, it may be difficult to provide adequate counseling, particularly when novel genetic variants are identified. In this study, we assessed whether lymphocyte vacuolization, a well-known yet poorly explored characteristic of CLN3 disease, could serve as a measure of disease severity. Peripheral blood obtained from healthy controls and CLN3 disease patients was used to assess lymphocyte vacuolization by (a) calculating the degree of vacuolization using light microscopy and (b) quantifying expression of lysosomal-associated membrane protein 1 (LAMP-1), using flow cytometry in lymphocyte subsets as well as a qualitative analysis using electron microscopy and ImageStream analysis. Quantifying lymphocyte vacuolization allowed to differentiate between CLN3 disease phenotypes ( Lymphocyte vacuolization serves as a proxy for CLN3 disease severity. Quantifying vacuolization may help interpretation of novel genetic variants and provide an individualized readout for upcoming therapies. Show less