Work Description

Date: 17 February, 2025

Dataset Title: Raw data (CSV and bm2) for gene expression data in SH-SY5Y cells exposed to lead (Pb) during neural differentiation

Dataset Contact: Justin Colacino colacino@umich.edu

Dataset Creators:
Name: Rachel K. Morgan
Email: rkmorgan@umich.edu
Institution: University of Michigan Department of Environmental Health Sciences
ORCID: https://orcid.org/0000-0002-0157-7755

Name: Anagha Tpaswi
Email: atapaswi@umich.edu
Institution: University of Michigan Department of Environmental Health Sciences
ORCID: https://orcid.org/0000-0001-5051-8623

Name: Justin Colacino
Email: colacino@umich.edu
Institution: University of Michigan Department of Environmental Health Sciences
ORCID: https://orcid.org/0000-0002-5882-4569

Funding: National Institutes of Health (AG072396, ES031686, ES028802, ES017885, ES007062, HD079342, AG088407)

Key Points:
- We compare the effects of environmentally-relevant and continuous Pb exposure on gene expression in differentiating SH-SY5Y cells.
- Differential gene expression revealed several key pathways are affected by exposure, with 10uM Pb having the greatest impact on cells.
- Differential gene expression present at the low (0.16uM Pb) and moderate (1.26uM Pb) concentrations during differentiation (Days 9-15) disappeared once differentiation concluded on Day 18.
- Pathway analysis revealed environmentally relevant Pb exposure during neural differentiation impacts responses to oxidative stress and DNA damage, as well as protein misfiling and cell cycling.

Research Overview:
SH-SY5Y cells were differentiated into neuron-like cells in the presence of continuous and environmentally relevant levels of lead (Pb). Cells were collected every three days (beginning on day 6 of the 18 day protocol) for the purposes of RNA extraction and subsequent sequencing.

Methodology:
Following dosing and genomic extractions from the cells, RNA was processed into sequencing libraries using the plexWell (SeqWell) protocol. RNA libraries were sequenced at the UM Advanced Genomics Core on the Illumina NovaSeq 6000, using 151-bp paired end reads to an average depth of great than 30 million reads per sample. FastQC reads were demultiplexed back into individual samples based on the i7 and i5 indices. Sequencing data was transferred to the UM Great Lakes high-performance computing cluster for analysis. Sequencing read quality was assessed via FastQC and MultiQC. Reads were aligned to a splice junction-aware build of the human genome (GRCh38) using STAR. Gene counts per million were computed using featureCounts, where multimapping and multi-overlapping reads were not counted. Read count matrices were loaded into edgeR in R (v.4.3.3). Genes with low expression were excluded from downstream analysis using the default settings of filterByExpr. Library sizes were normalized using calcNormFactors and dispersion estimated using estimateDisp. Differential gene expression between each Pb condition (n = 3 replicates) and relevant controls (n = 3 replicates) at each time point was calculated using quasi-likelihood negative binomial generalized log-linear modeling.

Files contained here:
CSV/xlsx - Pb_x_5Y_Differentiation_Full_DGEs.xlsx
- This file contains the total results for differential gene expression (DGE) analysis using edgeR.
- This file is broken up into sheets, one for each day and Pb concentration (format: Day X_XuM Pb)
- Table incudes: gene (provided as gene symbols),
the log(fold change) (logFC) in the expression of that gene with a given Pb concentration relative to control (0uM Pb),
log (counts per million reads) (logCPM): measure of gene expression that is used to estimate normalized values,
F-statistic (F): measure of significant statistical differences in gene expression between multiple groups; a higher value indicates a strong difference in expression levels between groups,
P value (PValue): a number describing the liklihood of obtaining the observed data under the null hypothesis (that expression in the exposed condition will be equal to that of the control),
False discovery rate (FDR): the expected proportion of incorrect rejections of a null hypothesis; a way of controlling for multiple comparisons when testing multiple hypotheses.
bm2 - 5Y_Pb_BMD.bm2
- This file was generated by the open access software BMDExpress3, which is supported by the National Toxicology Program (NTP), US Environmental Protection Agency (EPA), Health Canada, and Sciome.
- This software conducts differentiatial gene expression testing, benchmark concentration analysis, and pathway analyses in one location. The end results are parameterized curve fits of concentration-response data for
individual gene probes together with associated statistics, and simple visualizations.
- Users can access different data formats by selecting from the dropdown menu in the top left corner of BMDExpress:
Expression Data: raw gene expression data
One-Way ANOVA: differential gene expression testing across multiple groups
Benchmark Dose Analyses: provides the estimated Pb concentration, based on the input data, at which a specific gene (Probe ID) would be expected to show a significant change in expression, relative to control
Functional Classification: Pathay Analysis of genes affected by Pb exposure including Gene Ontology (GO), Reactome, and Supplemental (imported by user from MSigDB)

Use and Access:
This data set is made available under a Creative Commons Public Domain license (CC0 1.0).

To Cite Data:
Morgan, R. K., Tapaswi, A., Colacino, J. Raw data (CSV and bm2) for gene expression data in SH-SY5Y cells exposed to lead (Pb) during neural differentiation [Data set], University of Michigan - Deep Blue Data.