Biology subjects

Chuang, H.-W.

Publications and source records attributed to Chuang, H.-W..

4 recordsLinked to original sources

bioRxiv2025

Cell-type specific iron content regulation revealed by single-cell iron quantification

Iron is crucial for cellular metabolism and cell growth. Nevertheless, in humans, both iron deficiency and disorders of iron overload are widespread. How cellular iron content varies depending upon iron availability, and how this influences cell function is poorly characterised. We developed a method to quantify metals in hundreds of cells per minute via single-cell inductively-coupled plasma mass spectrometry (sc-ICP-MS), and used this to explore iron usage by immune cells. Activated murine T-cells exposed to a 625-fold titration of extracellular iron maintained close homeostatic control, with iron content varying by [~]20%. However, these variations strongly correlated with activation characteristics and proliferation. Running sc-ICP-MS downstream of flow cytometric sorting showed that murine T-cells and B-cells ex vivo exhibit similar mean and heterogeneity of cellular iron while splenic macrophages contain twice as much iron and more heterogeneous iron content. Finally, activated human B-cells contain [~]10-fold more iron per cell than murine B-cells. We suggest that mechanisms of iron homeostasis impart particular ranges or set-points of iron content to different cell types and activation states, and that small changes in iron content have large effects on cell behaviour. Our methodological advance and consequent findings suggest new approaches to studying the biology of metals.

cell biology↗

bioRxiv2024

Rapid and precise quantification of lymphocyte iron content by single cell inductively coupled plasma mass spectrometry

Metals facilitate catalysis during cellular metabolism, but heterogeneity of metal content at single-cell level within and between cell populations is poorly characterized. This is important because deficiencies of biometals, for example iron, are enormously prevalent worldwide. Here we quantify metal content of single-cells using inductively-coupled plasma mass spectrometry. To develop the method, we used rhodium and iridium-intercalated Jurkat cells, obtaining >0.96% r2 cross-analytical correlation with mass cytometry. We quantified iron and calcium mass/cell for murine T-lymphocytes with 3% and 8% 2-sigma intra-precision, respectively, when assessing thousands of cells/minute. T-lymphocytes exposed to a 625-fold difference in extracellular iron concentrations maintained close iron homeostatic control, varying [~]20% in iron content. Nevertheless, this relatively small variation strongly correlated with changes in cellular activation characteristics measured by flow cytometry. We also assessed human B-cell iron content, which was [~]10-fold higher than murine T-lymphocytes. Overall, we demonstrate rapid iron quantification at single-cell level in different cell types and relate cellular iron content to cell function. TeaserPrecise and rapid iron metallomics of lymphocytes by single cell ICP-MS is a powerful approach for accessing signatures of immunological status.

immunology↗

bioRxiv2023

Graph-KIR: Graph-based KIR Copy Number Estimation and Allele Calling Using Short-read Sequencing Data

MotivationThe Killer-cell Immunoglobulin-like Receptor (KIR) is a highly polymorphic region in the human genome, associated with autoimmune diseases and organ transplantation. The sequences of KIR genes are highly similar among star alleles as well as in between individual genes, with the copy number of each KIR gene typically ranging from 0 to 4. In this study, we introduce a tool Graph-KIR that aims to estimate the copy number of genes and to call full-resolution (7-digit) KIR alleles from a whole genome sequencing (WGS) sample. ResultsGraph-KIR, unlike most KIR tools, is capable of independently typing KIR alleles per sample with no reliance on the distribution of any framework gene in a cohort. In a set of 100 simulated samples, Graph-KIR demonstrated 100% accuracy in copy number estimation and high accuracy of allele typing: 91.2% at 7-digit resolution, 97.0% at 5-digit resolution, 97.2% at 3-digit resolution, and 99.6% at gene-level resolution. Graph-KIR outperforms existing tools such as PINGs WGS version (91.9% accuracy) and T1K (84.6% accuracy) at 5-digit resolution. By analyzing the results on 44 HPRC samples, Graph-KIR achieves an accuracy of 85.0%, better than PINGs WGS version (75.5% accuracy) at 5-digit resolution. The release of Graph-KIR adds another valuable tool to assist users in accurately estimating copy numbers and calling alleles of KIR genes from WGS samples, ensuring reliable performance. AvailabilityThe Graph-KIR and paper-related pipeline codes are available at https://github.com/linnil1/KIR_graph.

bioinformatics↗

bioRxiv2023

Genetic Diversity and Structural Complexity of the Killer-Cell Immunoglobulin-Like Receptor Gene Complex: A Comprehensive Analysis using Human Pangenome Assemblies

The killer-cell immunoglobulin-like receptor (KIR) gene complex, a highly polymorphic region of the human genome that encodes proteins involved in immune responses, poses strong challenges in genotyping due to its remarkable genetic diversity and structural intricacy. Accurate analysis of KIR alleles, including their structural variations, is crucial for understanding their roles in various immune responses. Leveraging the high-quality genome assemblies from the Human Pangenome Reference Consortium (HPRC), we present a novel bioinformatic tool, the Structural KIR annoTator (SKIRT), to investigate gene diversity and facilitate precise KIR allele analysis. We applied SKIRT on 47 HPRC-phased assemblies and identified a recurrent novel KIR2DS4/3DL1 fusion gene in the paternal haplotype of HG02630 and maternal haplotype of NA19240. Additionally, SKIRT accurately identifies eight structural variants and 17 novel nonsynonymous alleles, all of which were independently validated using short-read data or quantitative polymerase chain reaction. Our study has discovered a total of 570 novel alleles, among which eight haplotypes harbor at least one KIR gene duplication, six haplotypes have lost at least one framework gene, and 75 out of 94 haplotypes (79.8%) carry at least five novel alleles, thus confirming KIR genetic diversity. These findings are pivotal in providing insights into KIR gene diversity and serve as a solid foundation for understanding the functional consequences of KIR structural variations. High-resolution genome assemblies offer unprecedented opportunities to explore polymorphic regions that are challenging to investigate using short-read sequencing methods. The SKIRT pipeline emerges as a highly efficient tool, enabling the comprehensive detection of the complete spectrum of KIR alleles within human genome assemblies.

genomics↗