Biology subjects

Chellamuthu, V. R.

Publications and source records attributed to Chellamuthu, V. R..

2 recordsLinked to original sources

bioRxiv2024

CyNET- a network analysis framework for high dimensionality, system level analyses of the functional Immunome

The immune system is a complex "Network of Networks", in which various immune cell subsets interact and influence each others functions, ultimately determining immune competence and the control or onset of disease. The coordinated interactions between these cell subsets determine whether the outcome is a normal physiological state or a pathological condition. Established statistical procedures largely ignore the interactions between subsets and rely on statistically significant changes in the frequency of cell subsets. We developed CyNET (Cytometry Network)-- an analysis platform based on a network science approach to understand the immune system holistically. CyNET enables us to quantify the systems level and subsets level properties. We show that changes in the centrality of the nodes (immune subsets) reflect better biological functions than changes in frequency. We used CyNET to analyze the immune development along the chronological age gradient. Peripheral blood cells from healthy newborns (cord blood), adults (20 to 55 years), and elderly (70 years and above) human subjects were further used for validation using single-cell transcriptomics. We found that network edge density, degree centralization, and assortativity score reflect the maturation and development of the immune system along the age axis, thus enabling the characterisation of the functional architecture of the Immunome and the identification of key functional hubs within the immune networks.

immunology↗

bioRxiv2023

Comparative analysis of single-cell RNA sequencing methods with and without sample multiplexing

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating biological heterogeneity at the single-cell level in human systems and model organisms. Recent advances in scRNA-seq have enabled the pooling of cells from multiple samples into single libraries, thereby increasing sample throughput while reducing technical batch effects, library preparation time, and the overall cost. However, a comparative analysis of scRNA-seq methods with and without sample multiplexing is lacking. In this study, we benchmarked methods from two representative platforms: Parse Biosciences (Parse; with sample multiplexing) and 10X Genomics (10x; without sample multiplexing). By using peripheral blood mononuclear cells (PBMCs) obtained from two healthy individuals, we demonstrate that demultiplexed scRNA-seq data obtained from Parse showed similar cell type frequencies compared to 10X data where samples are not multiplexed. Despite a relatively lower library and cell capture efficiencies, Parse can detect rare cell types (e.g. plasmablasts and dendritic cells) which is likely due to its relatively higher sensitivity in gene detection. Moreover, comparative analysis of transcript quantification between the two platforms revealed platform-specific distributions of gene length and GC content. These results offer guidance for researchers in designing high-throughput scRNA-seq studies.

genomics↗