Biology subjects

Weiss, P.

Publications and source records attributed to Weiss, P..

3 recordsLinked to original sources

bioRxiv2025

MyoFuse: A fully AI-based workflow for automated quantification of skeletal muscle cell fusion in vitro

BackgroundThe myogenic fusion index (FI) is commonly used in skeletal muscle cell culture to assess the ability of myoblasts to form myotubes, as the ratio of myoblast nuclei fused with myotubes over the total number of myoblasts. The manual quantification of the FI from 2D microscopy images is tedious and biased, thus several automated methods have been developed. However, they still face challenges such as efficient nucleus segmentation and classification of fused and isolated myoblast nuclei. Here, we developed a novel workflow entirely based on AI for fully automated and unbiased quantification of the FI. ResultsUsing current methods, we show that myoblast nuclei located above or below myotubes can significantly corrupt accurate FI computation. To circumvent this issue, we developed MyoFuse which enables an accurate and high-throughput segmentation and classification of myonuclei. It comprises a nuclei segmentation step using Cellpose, followed by a classification network trained with Svetlana. MyoFuse demonstrated strong accuracy when tested against manual annotation in mouse C2C12 and human primary myotubes. The trained classifier is able to differentiate myotube nuclei from myoblast nuclei based on myotube cytoplasm staining only. Experimental comparisons also highlighted that the previously developed methods lead to a significant overestimation of the FI. ConclusionIn summary, we underscore the lack of accuracy of traditional methods for automated FI quantification. MyoFuse enables a direct and accurate segmentation of nuclei even in nuclei clusters frequently observed in myotubes. This workflow thus offers a new and more reliable method to evaluate the FI. It also limits the selection bias by processing large images.

cell biology↗

bioRxiv2024

Anabolic lipid metabolism regulates adipose type 2 innate lymphoid cell differentiation to maintain metabolic health

Group 2 innate lymphoid cells (ILC2) residing in the adipose tissue play an important role in maintaining the metabolic health and energy balance of the organisms. In obesity ILC2 numbers are reduced and their function is impaired, leading to the progression of metabolic inflammation. However, which events impact on ILC2 biology in the adipose tissue in obesity remains unresolved. Here, we find that high fat diet (HFD)-induced obesity in mice results in the metabolic reprogramming of adipose ILC2, impairing mitochondrial function and the expression of the enzyme Acetyl-CoA carboxylase 1 (ACC1). Investigating a possible connection between ACC1 and obesity-induced changes in ILC2, we show that fatty acids directly reduce the expression of ACC1, while pharmacological inhibition of ACC1 diminishes mitochondrial function and ILC2 metabolism. Furthermore, deletion of ACC1 in ILC2 phenocopies the overall reduction and functional impairment of ILC2 observed in obesity, which ultimately leads to increased triglycerides in circulation, adipose tissue hypertrophy and inflammation, even in the absence of HFD. Through single-cell RNA sequencing analysis we uncover that HFD-feeding or deletion of ACC1 results in the accumulation of undifferentiated ILC2 and ILC progenitors in the adipose tissue, suggesting that ACC1 may primarily regulate the maturation of ILC2. Together, these results reveal that obesity could predominately impair adipose ILC2 differentiation and activation by impacting on the expression of ACC1, rather than inducing cell death through lipid overload and lipotoxicity.

immunology↗

bioRxiv2019

Quantifying Molecular Bias in DNA Data Storage

DNA has recently emerged as an attractive medium for future digital data storage because of its extremely high information density and potential longevity. Recent work has shown promising results in developing proof-of-principle prototype systems. However, very uneven (biased) sequencing coverage distributions have been reported, which indicates inefficiencies in the storage process and points to optimization opportunities. These deviations from the average coverage in oligonucleotide copy distribution result in sequence drop-out and make error-free data retrieval from DNA more challenging. The uneven copy distribution was believed to stem from the underlying molecular processes, but the interplay between these molecular processes and the copy number distribution has been poorly understood until now. In this paper, we use millions of unique sequences from a DNA-based digital data archival system to study the oligonucleotide copy unevenness problem and show that two important sources of bias are the synthesis process and the Polymerase Chain Reaction (PCR) process. By mapping the sequencing coverage of a large complex oligonucleotide pool back to its spatial distribution on the synthesis chip, we find that significant bias comes from array-based oligonucleotide synthesis. We also find that PCR stochasticity is another main driver of oligonucleotide copy variation. Based on these findings, we develop a statistical model for each molecular process as well as the overall process and compare the predicted bias with our experimental data. We further use our model to explore the trade-offs between synthesis bias, storage physical density and sequencing redundancy, providing insights for engineering efficient, robust DNA data storage systems.

synthetic biology↗