Dataset Information

A Versatile Distance-Based Approach for Gene Expression Selection Across Diverse Biological Systems

ABSTRACT: Differential gene expression analysis is essential for characterizing immune cell phenotypes, yet conventional approaches—typically based on log₂ fold‑change (log₂FC) and False Discovery Rate (FDR) thresholds—often struggle to capture the complexity and continuum of transcriptional states. To address this limitation, we developed a new computational method for gene selection from mRNA‑seq data: the Cartesian Distance‑Based Gene Expression (CDBGE) selector. This algorithm identifies differentially expressed genes by leveraging multidimensional expression distances rather than relying on traditional univariate statistical cutoffs, enabling a more refined and biologically coherent gene‑marker selection. We applied the CDBGE selector to construct a gene‑based framework for distinguishing macrophage polarization states. The model was trained using publicly available macrophage transcriptomic datasets and subsequently validated with in vitro human macrophages stimulated with IFN‑γ/LPS, conditioned medium from HepG2 liver cancer cells, or IL10. To evaluate its generalizability beyond macrophage biology, we further tested the method on human embryonic stem cell differentiation datasets. Compared with standard differential expression pipelines, the CDBGE selector more effectively identified subtype‑specific markers and revealed dynamic transcriptional transitions over time. These findings demonstrate that distance‑based gene classification provides an improved strategy for analyzing complex mRNA‑seq datasets. Overall, the CDBGE selector offers a robust, scalable, and broadly applicable tool for differential gene expression analysis and phenotype characterization. Overall design Transcriptomic RNA-seq from Human macrophages stimulated with IFN‑gamma/LPS, conditioned medium from HepG2 liver cancer cells, or IL10 at different times.

ORGANISM(S): Homo sapiens

PROVIDER: GSE331143 | GEO | 2026/08/05

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Purpose: To gain additional insights to candidate factors promoting longevity in Caloric Restricted Conditioned Media (CRCM), we performed transcriptomics analysis (RNA-seq) on BY4741 yeast cells grown in non-restricted SC media supplemented with CRCM, Non-restricted Conditioned Media (NRCM), or water as a control with a goal of identifying physiological responses that could be linked to specific metabolites. Methods: RNA profiles of NR (SC, 2% glucose) treated cells supplemented with water, 2% CRCM, or 2% NRCM at log phase, 24, and 96 hours post-inoculation. These samples were conducted in triplicate. Following inoculation, we harvested cells at log phase, 24 hrs (late diauxic shift), and 96 hrs (stationary phase).Total RNA was isolated using the hot acid phenol method and then processed into Illumina DNA sequencing libraries. Briefly, total RNA was treated with DNase I for 10 min at 37°C and then measured for concentration and quality with an Agilent Bioanalyzer. PolyA mRNA selection was performed on 5µg of the DNase-treated total RNA with the NEBNext Poly(A) mRNA magnetic isolation module (E7490). DNA sequencing libraries were then generated with the NEBNExt Ultra Directional RNA library Prep kit for Illumina (E7420). Libraries were sequenced on an Illumina NextSeq 500 by the UVA Genome Analysis and Technology Core. Results: Sequencing reads were mapped to the sacCer3 genome using bowtie2 with default settings. We preprocessed sequencing data from the UVA genomics core and analyzed differential gene expression in R using DESeq2. We found that In log phase cells, there were no significantly upregulated or downregulated genes in the CRCM or NRCM samples as compared to the water supplemented control (FDR <0.05). At the 24 hr timepoint, CRCM samples diverged from the NRCM and water supplemented control samples, and showed many more differentially regulated genes than the NRCM-treated samples. At the 96 hr timepoint, NRCM samples also became more clearly separated from the control in a PCA plot , though there were still a large number of genes exclusively altered in the CRCM samples. Furthermore, the YCL064C (CHA1) gene clearly stood out as being the most significantly upregulated in conditioned media supplementation. Conclusions: RNAseq data led us to explore YCL064C (CHA1) gene clearly stood out as being the most significantly upregulated in the conditioned media experiments. CHA1 encodes a predominantly mitochondrial L-serine (L-threonine) deaminase that catabolizes these amino acids. RNA-seq based transcriptome analysis allowed us to explore L-serine contribution to longevity phenotype we observed with CRCM.

Dataset Information

A Versatile Distance-Based Approach for Gene Expression Selection Across Diverse Biological Systems

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets