Nevena Milivojević Dimitrijević1,*, Miloš Ivanović2, Andreja Živić2, Biljana Ljujić3, Marina Gazdić Janković3, Uršula Prosenc Zmrzljak4, Ana Mirić1, Valentina Đorđević5, Feđa Puač6, Marko Živanović1,7, Nenad Filipović7,8
1 Institute for Information Technologies, University of Kragujevac, Kragujevac, Serbia
2 Faculty of Science, University of Kragujevac, Kragujevac, Serbia
3 Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
4 BIA Separations CRO Laboratory, Ljubljana, Slovenia
5 Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, Belgrade, Serbia
6 Labena d.o.o. Serbia, Belgrade, Serbia
7 BioIRC – Bioengineering Research and Development Center, University of Kragujevac, Kragujevac, Serbia
8 Faculty of Engineering, University of Kragujevac, Kragujevac, Serbia
nevena.milivojevic [at] uni.kg.ac.rs
Abstract
Transcriptome profiling at the single cell level is crucial for understanding complex biological systems and molecular mechanisms. We wanted to unravel the influence of polystyrene nanoparticles on peripheral blood mononuclear cells (PBMCs), using microfluidic technology. A total of 4 single-cell sequencing libraries were analyzed (one control and three different treatments). Thousands of individual cells per sample are Barcoded separately to index the transcriptome of each cell individually. Raw sequencing data were analyzed with the Cell Ranger software and visualized using Loupe Browser software. Set of analysis pipelines processes Chromium Single Gene Expression data to align reads, generate Feature Barcode matrices, and perform clustering and gene expression analysis. Each element of the matrix is the number of UMIs (Unique Molecular Identifier) associated with a feature (row) and a barcode (column). Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithms on single-cell sequencing samples were carried out. The expressed cells were clustered during which typical cell marker genes were used for annotation. Genes showing adjusted p-value < 0.05 and |log2 (fold change) | > 0.5 were considered to be marker genes. Loupe Browser was used for visualization of clusters and analysis of the single-cell data. In this way, gene markers for individual cell types obtained by single-cell sequencing represent a good model for the analysis of biological events.
Keywords: bioinformatics, single cell, sequencing, scRNA-seq, microfluidic
Acknowledgement: This research is funded by Labena Slovenia 10xGenomics Grant Challenge. This research was supported by Labena Serbia, the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, contract number 451-03-66/2024-03/200378 (Institute for Information Technologies Kragujevac, University of Kragujevac), 451-03-47/2023-01/200111 (Faculty of Medical Sciences, University of Kragujevac), as well as Junior projects of Faculty of Medical Sciences, University of Kragujevac JP 25/19, JP 05/20, JP 06/20 and JP 24/20.