Biology subjects

Kovacevic, V.

Publications and source records attributed to Kovacevic, V..

5 recordsLinked to original sources

bioRxiv2026

Nanoscale Material Size Shapes Distinct Immune Transcriptional States Under Physiological Flow

Nanoplastics interact continuously with circulating immune cells, yet how particle size and exposure complexity shape immune transcriptional organization under physiological flow conditions remains poorly understood. Here, controlled microfluidic exposure was combined with single-cell RNA sequencing to investigate the effects of size-defined polystyrene nanoplastics (PSNPs; 40 nm, 200 nm, and 40 + 200 nm) on primary human peripheral blood mononuclear cells (PBMCs) under dynamic flow conditions. Across immune populations, PSNP exposure induced a conserved ribosome-associated and RNA-regulatory transcriptional program, indicating a shared intracellular adaptive response. Monocytes displayed the strongest transcriptional remodeling, characterized by coordinated modulation of ribosome-associated, metabolic, and inflammatory signaling pathways in a size-dependent manner. Exposure to 40 nm PSNPs negatively enriched (suppressed) mitochondrial metabolic pathways, whereas 200 nm PSNPs enriched inflammatory signaling programs. Combined exposure induced concurrent metabolic and inflammatory pathway engagement without evidence of major immune topology disruption or discrete inflammatory state transitions. In contrast, adaptive immune cells exhibited comparatively modest and lineage-preserving transcriptional modulation. Together, these findings demonstrate that nanoplastic size and exposure complexity shape coordinated immunometabolic adaptation in human immune cells under physiologically relevant flow conditions and establish a framework for studying dynamic material-immune interactions at single-cell resolution.

bioinformatics↗

bioRxiv2023

Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics

Tracing cellular dynamic changes across conditions, time, and space is crucial for understanding the molecular mechanisms underlying complex biological systems. However, integrating multi-sample data in a unified and flexible way to explore cellular heterogeneity remains a major challenge. Here, we present Stereopy, a flexible and versatile framework for modeling and dissecting comparative and spatiotemporal patterns in multi-sample spatial transcriptomics with interactive data visualization. To optimize this flexible framework, we have developed three key components: a multi-sample tailored data container, a scope controller, and an analysis transformer. Furthermore, Stereopy showcases three transformative applications supported by pivotal algorithms. Firstly, the multi-sample cell community detection (CCD) algorithm introduces an innovative capability to detect specific cell communities and identify genes responsible for pathological changes in comparable datasets. Secondly, the spatially resolved temporal gene pattern inference (TGPI) algorithm represents a notable advancement in detecting important spatiotemporal gene patterns while concurrently considering spatial and temporal features, which enhances the identification of important genes, domains and regulatory factors closely associated with temporal datasets. Finally, the 3D niche-based regulation inference tool, named NicheReg3D, reconstructs the 3D cell niches to enable the inference of cell-gene interaction network within the spatial texture, thus bridging intercellular communications and intracellular regulations to unravel the intricate regulatory mechanisms that govern cellular behavior. Overall, Stereopy serves as both a bioinformatics toolbox and an extensible framework that provides researchers with enhanced data interpretation abilities and new perspectives for mining multi-sample spatial transcriptomics data.

bioinformatics↗

bioRxiv2023

INAEME: Integral Neoantigen Analysis with Entirety of Mutational Events

Neoantigens are peptides presented on the surface of cancer cells that can be recognized by the immune system. Multiple novel therapeutic approaches involve the administration of neoantigens to trigger immunity-induced tumor regression. Identifying neoantigens includes a personalized approach consisting of detailed analyses of the sequenced tumor tissue and its comparison with wild type to identify somatic mutations. Altered peptides are translated from nucleotides around somatic mutations, and their binding affinity and immunogenicity need further evaluation. Still, the entire bioinformatics analysis is very complex, and accurate prediction of the neoantigen candidates represents a true challenge. Here, we present the novel, integral bioinformatic analysis workflow for neoantigen discovery, denoted INAEME (Integral Neoantigen Analysis with Entirety of Mutational Events). The workflow performs integral processing of an individuals DNA tumor-normal and RNA tumor raw reads to output prioritized neoantigen candidates. Through conducted analysis and benchmarks, our main goal was to demonstrate the necessity of taking into account a wide scope of mutational events so far not considered in the existing solutions, including phasing of variants, influence of both somatic and germline variants, positions of all transcripts, neighboring variants, and frameshifts. The influence of each mutational event on the accuracy of predicted neoepitope candidates is tested across 300 TCGA samples from multiple cancer types, including melanoma, hepatocellular carcinoma, and lung squamous cancer. The observed loss of neoantigen nests, going from 8.45% up to 23.65%, underscores the importance of accounting for the entirety of mutational events to accurately identify robust neoantigen candidates for cancer immunotherapy and vaccine development. The adaptation of the described methods in the bioinformatics analysis minimizes the existence of false positives, which are only later discovered in a laboratory environment using expensive methods such as mass spectrometry or microscopy.

bioinformatics↗

bioRxiv2020

The first insight into the genetic structure of the population of modern Serbia

The complete understanding of the genomic contribution to complex traits, diseases, and response to treatments, as well as genomic medicine application to the well-being of all humans will be achieved through the global variome that encompasses fine-scale genetic diversity. Despite significant efforts in recent years, uneven representation still characterizes genomic resources and among the underrepresented European populations are the Western Balkans including the Serbian population. Our research addresses this gap and presents the first ever dataset of variants in clinically relevant genes in the population sample of contemporary Serbia. A few variants significantly more frequent in the analyzed sample population compared to the European population as a whole are distinguished as its unique genetic determinants. We explored thoroughly their potential functional impact and its correlation with the health burden of the population of Serbia. Our variants catalogue improves the understanding of genetics of modern Serbia, contributes to application of precision medicine and health equity. In addition, this resource may also be applicable in neighboring regions and in worldwide functional analyses of genetic variants in individuals of European descent.

genomics↗

bioRxiv2020

Benchmarking challenging small variants with linked and long reads

Genome in a Bottle (GIAB) benchmarks have been widely used to help validate clinical sequencing pipelines and develop new variant calling and sequencing methods. Here, we use accurate linked reads and long reads to expand the prior benchmarks in 7 samples to include difficult-to-map regions and segmental duplications that are not readily accessible to short reads. Our new benchmark adds more than 300,000 SNVs, 50,000 indels, and 16 % new exonic variants, many in challenging, clinically relevant genes not previously covered (e.g., PMS2). For HG002, we include 92% of the autosomal GRCh38 assembly, while excluding problematic regions for benchmarking small variants (e.g., copy number variants and reference errors) that should not have been in the previous version, which included 85% of GRCh38. By including difficult-to-map regions, this benchmark identifies eight times more false negatives in a short read variant call set relative to our previous benchmark.We have demonstrated the utility of this benchmark to reliably identify false positives and false negatives across technologies in more challenging regions, which enables continued technology and bioinformatics development.

genomics↗