Biology subjects

So, H.

Publications and source records attributed to So, H..

2 recordsLinked to original sources

bioRxiv2026

Spatial transcriptomic analysis of myositis muscles reveals novel molecular insights of MDA5+ dermatomyositis

Idiopathic inflammatory myopathies (IIM), also known as myositis, are a rare and heterogeneous group of autoimmune disorders characterized by chronic inflammation of skeletal muscle. Other organs are also frequently affected, such as skin, joints, and lungs, leading to morbidity and even mortality. Anti-MDA5 autoantibody-positive dermatomyositis (MDA5+ DM) is a unique subtype with a high mortality rate but the underlying molecular mechanisms remain incompletely understood. In this study, we profiled the spatial transcriptome of a cohort of healthy controls, MDA5+, and MDA5- muscles and found a distinct transcriptomic profile of the MDA5+ muscles with a low percentage of stressed myofibers but increased immune cell infiltration. Niche analysis revealed a distinct microenvironment in the MDA5+ muscles with the endothelial cell (EC) niche showing increased inflammation, dampened oxygen transport function, and a unique enrichment of the Type 1 interferon response. Cell-cell communication and pathway activity analysis uncovered that hypoxia, TGF-{beta}, NF-{kappa}B, and TNF- signaling were enriched in the MDA5+ EC niche. Altogether, our results support a vasculopathy model whereby blood vessels exhibit a strong inflammatory response and impaired oxygen transport function, leading to vasculopathy and perivascular immune cell infiltration in MDA5+ muscles.

immunology↗

bioRxiv2019

Multi-ATOM: Ultrahigh-throughput single-cell quantitative phase imaging with subcellular resolution

A growing body of evidence has substantiated the significance of quantitative phase imaging (QPI) in enabling cost-effective and label-free cellular assay, which provides useful insights into understanding biophysical properties of cells and their roles in cellular functions. However, available QPI modalities are limited by the loss of imaging resolution at high throughput and thus run short of sufficient statistical power at the single cell precision to define cell identities in a large and heterogeneous population of cells - hindering their utility in mainstream biomedicine and biology. Here we present a new QPI modality, coined multi-ATOM that captures and processes quantitative label-free single-cell images at ultra-high throughput without compromising sub-cellular resolution. We show that multi-ATOM, based upon ultrafast phase-gradient encoding, outperforms state-of-the-art QPI in permitting robust phase retrieval at a QPI throughput of >10,000 cell/sec, bypassing the need for interferometry which inevitably compromises QPI quality under ultrafast operation. We employ multi-ATOM for large-scale, label-free, multi-variate, cell-type classification (e.g. breast cancer sub-types, and leukemic cells versus peripheral blood mononuclear cells) at high accuracy (>94%). Our results suggest that multi-ATOM could empower new strategies in large-scale biophysical single-cell analysis with applications in biology and enriching disease diagnostics.

bioengineering↗