Biology subjects

Zhou, L. Y.

Publications and source records attributed to Zhou, L. Y..

3 recordsLinked to original sources

bioRxiv2022

Hedgehog signaling acts as cell fate determinant during adult tissue repair

Successful muscle regeneration relies on the interplay of multiple cell populations. However, the signals required for this coordinated intercellular crosstalk remain largely unknown. Here, we describe how the Hedgehog (Hh) signaling pathway controls the fate of fibro/adipogenic progenitors (FAPs), the cellular origin of intramuscular fat (IMAT) and fibrotic scar tissue. Using conditional mutagenesis and pharmacological Hh modulators in vivo and in vitro, we identify DHH as the key ligand that acts as a potent adipogenic brake by preventing the adipogenic differentiation of FAPs. Hh signaling also impacts muscle regeneration, albeit indirectly through induction of myogenic factors in FAPs. Our results also indicate that ectopic and sustained Hh activation forces FAPs to adopt a fibrogenic fate resulting in widespread fibrosis. In this work, we reveal novel post-developmental functions of Hh signaling in balancing tissue regeneration and fatty fibrosis. Moreover, they provide the exciting possibility that mis-regulation of the Hh pathway with age and disease could be a major driver of pathological IMAT formation.

developmental biology↗

bioRxiv2022

A guide to examining intramuscular fat formation and its cellular origin in skeletal muscle

Fibro-adipogenic progenitors (FAPs) are mesenchymal stromal cells that play a crucial role during skeletal muscle homeostasis and regeneration. FAPs build and maintain the extracellular matrix that acts as a molecular myofiber scaffold. In addition, FAPs are indispensable for myofiber regeneration as they secrete a multitude of beneficial factors sensed by the muscle stem cells (MuSCs). In diseased states, however, FAPs are the cellular origin of intramuscular fat and fibrotic scar tissue. This fatty fibrosis is a hallmark of sarcopenia and neuromuscular diseases, such as Duchenne Muscular Dystrophy. One significant barrier in determining why and how FAPs differentiate into intramuscular fat is effective preservation of adipocytes, especially in frozen tissue sections. Conventional methods of skeletal muscle tissue processing, such as snap-freezing, do not properly preserve the morphology of individual adipocytes, thereby preventing accurate visualization and quantification. Here, we describe a protocol that provides robust preservation of adipocyte morphology in skeletal muscle sections allowing visualization, imaging, and quantification of intramuscular fat. We also outline how to process a portion of muscle tissue for RT-qPCR, enabling users to confirm observed changes in fat formation by viewing differences in expression of adipogenic genes. Additionally, we will describe how our protocol can be adopted to visualize adipocytes by whole mount immunofluorescence of muscle samples. Finally, we will outline how to combine this protocol with genetic lineage tracing of Pdgfr-expressing FAPs to study the adipogenic conversion of FAPs. Our protocol consistently yields high-resolution and morphologically accurate immunofluorescent images of adipocytes that, along with confirmation by RT-qPCR, allows for robust, rigorous, and reproducible visualization and quantification of intramuscular fat. Together, our analysis pipeline is the first step to improve our understanding of how FAPs differentiate into intramuscular fat and provides a framework to validate novel interventions to prevent fat formation.

developmental biology↗

bioRxiv2021

Prioritizing candidate peptides for cancer vaccines by PEPPRMINT: a statistical model to predict peptide presentation by HLA-I proteins

Recent development of cancer immunotherapy has opened unprecedented avenues to eliminate tumor cells using the human immune system. Cancer vaccines composed of neoantigens, or peptides unique to tumor cells due to somatic mutations, have emerged as a promising approach to activate or strengthen the immune response against cancer. A key step to identifying neoantigens is computationally predicting which somatically mutated peptides are presented on the cell surface by a human leukocyte antigen (HLA). Computational prediction relies on large amounts of high-quality training data, such as mass spectrometry data of peptides presented by one of several HLAs in living cells. We developed a complete pipeline to prioritize neoantigens for cancer vaccines. A key step of our pipeline is PEPPRMINT (PEPtide PResentation using a MIxture model and Neural neTwork), a model designed to exploit mass spectrometry data to predict peptide presentation by HLAs. We applied our pipeline to DNA sequencing data of 60 melanoma patients and identified a group of neoantigens that were more immunogenic in tumor cells than in normal cells. Additionally, the neoantigen burden estimated by PEPPRMINT was significantly associated with activity of the immune system, suggesting these neoantigens could induce an immune response.

genomics↗