Biology subjects

Arnould, A.

Publications and source records attributed to Arnould, A..

3 recordsLinked to original sources

bioRxiv2025

Systemic Factors Affect Bone Health in SMA Type II Patients and a Mouse Model of SMA

Spinal muscular atrophy (SMA) is a rare developmental disorder affecting multiple tissues. Among the non-central nervous system tissues implicated in SMA is the skeletal system, including bone and cartilage. Low bone mineral density, increased numbers of fractures of the long bones and vertebra, hip pain, and scoliosis have been reported across the spectrum of SMA patients. While lack of ambulation likely contributes significantly to bone pathology, SMA patients have markedly lower bone density compared to other non-ambulatory patients with debilitating diseases such as Duchenne muscular dystrophy, suggesting that there is a cell-intrinsic contribution of SMN to bone homeostasis and function. Mouse models of SMA have also confirmed the presence of bone and cartilage phenotypes. These alterations frequently persist post-treatment. Recent advancements in therapeutic strategies, approved by both the FDA and the EMA, have represented a leap forward in the management of SMA. However, treatment gaps remain. Post-treatment, patients frequently face continued challenges with scoliosis, bone fractures, and persistent muscle weakness--conditions that underscore the urgent need for more comprehensive therapeutic strategies with combination therapies that can support skeletal health. To date, no molecular map exists of the changes that occur in SMA patient bone and cartilage, impeding the ability of finding targeted therapies. To address this clinical need, we profiled the transcriptome of the vertebral bone and cartilage in a cohort of 11 Type II SMA patients who were undergoing surgery for scoliosis correction and compared them to 7 idiopathic scoliosis and 2 DMD controls. Additionally, we characterized the skeletal health of a mouse model of type I SMA. We find that multisystemic factors including liver and muscle health affect the underlying SMA bone pathology. Specifically, we detect alterations in the balance between osteoclasts and osteoblasts, changes in PPAR{gamma} signaling, mitochondrial oxidative phosphorylation and fatty acid beta-oxidation, and alterations in the muscle-derived factor Irisin that play a role in overall SMA bone pathology.

genomics↗

bioRxiv2024

COLLAGE: COnsensus aLignment of muLtiplexing imAGEs

Multiplexed immunohistochemistry (mIHC) enables the high-dimensional single-cell interrogation of pathological tissue samples. mIHC is commonly based on the collection of high-resolution images from repeated staining cycles of the same tissue sample. Images of individual cycles typically consist of smaller tiles that need to be stitched into larger composite images, while images from serial rounds require alignment in a shared set of coordinates to enable pixel-perfect data integration. Current algorithms for stitching and registration require solving a single large puzzle consisting of billions of pixels making them computationally expensive but moreover forcing them to introduce errors to close the puzzle, which significantly impact the downstream results and the single-cell profiles. Here, we present the development and evaluation of COLLAGE (COnsensus aLignment of muLtiplexing imAGEs), an innovative stitching and registration method that leverages on the complementarity of these two steps in a divide and conquer approach: in contrast to other algorithms, COLLAGE breaks the process down into thousands of small puzzles, enabling extensive parallelisation and not forcing errors in its solution. Because COLLAGE also includes AlgnQC, a novel deep-learning-based evaluation metric of registration quality, the quality of the resulting image stacks is consistently maximised, while images with errors are flagged in an automated way. COLLAGE is available via www.disscovery.org. O_FIG O_LINKSMALLFIG WIDTH=148 HEIGHT=200 SRC="FIGDIR/small/603557v1_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@18d0f5borg.highwire.dtl.DTLVardef@1eb2756org.highwire.dtl.DTLVardef@163bb95org.highwire.dtl.DTLVardef@b04453_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

QUAL-IF-AI: Quality Control of Immunofluorescence Images using Artificial Intelligence

Fluorescent imaging has revolutionized biomedical research, enabling the study of intricate cellular processes. Multiplex immunofluorescent imaging has extended this capability, permitting the simultaneous detection of multiple markers within a single tissue section. However, these images are susceptible to a myriad of undesired artifacts, which compromise the accuracy of downstream analyses. Manual artifact removal is impractical given the large number of images generated in these experiments, necessitating automated solutions. Here, we present QUAL-IF-AI, a multi-step deep learning-based tool for automated artifact identification and management. We demonstrate the utility of QUAL-IF-AI in detecting four of the most common types of artifacts in fluorescent imaging: air bubbles, tissue folds, external artifacts, and out-of-focus areas. We show how QUAL-IF-AI outperforms state-of-the-art methodologies in a variety of multiplexing platforms achieving over 85% of classification accuracy and more than 0.6 Intersection over Union (IoU) across all artifact types. In summary, this work presents an automated, accessible, and reliable tool for artifact detection and management in fluorescent microscopy, facilitating precise analysis of multiplexed immunofluorescence images.

bioinformatics↗