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Evaluating side effects of nanoparticle-mediated siRNA delivery to mesenchymal stem cells using next generation sequencing and enrichment analysis.

ABSTRACT: RNA interference has immense potential to modulate cell functions. However, effective delivery of small interfering RNA (siRNA) while avoiding deleterious side effects has proven challenging. This study investigates both intended and unintended effects of diblock copolymer nanoparticle (NP) delivery of siRNA delivery to human mesenchymal stem cells (hMSC). Specifically, siRNA delivery was investigated at a range of NP-siRNA:hMSC ratios with a focus on the effects of NP-siRNA treatment on hMSC functions. Additionally, next generation RNA sequencing (RNAseq) was used with enrichment analysis to observe side effects in hMSC gene expression. Results show NP-siRNA delivery is negatively correlated with hMSC density. However, higher NP-siRNA:hMSC ratios increased cytotoxicity and decreased metabolic activity. hMSC proliferation was largely unaffected by NP-siRNA treatment, except for a threefold reduction in hMSCs seeded at 4,000 cells/cm². Flow cytometry reveals that apoptosis is a function of NP-siRNA treatment time and seeding density; ∼14% of the treated hMSCs seeded at 8,000 cells/cm² were annexin V⁺-siRNA⁺ 24 hr after treatment, while 11% of the treated population was annexin V⁺-siRNA^-. RNAseq shows that NP-siRNA treatment results in transcriptomic changes in hMSCs, while pathway analysis shows upregulation of apoptosis signaling and downregulation of metabolism, cell cycle, and DNA replication pathways, as corroborated by apoptosis, metabolism, and proliferation assays. Additionally, multiple innate immune signaling pathways such as toll-like receptor, RIG-I-like receptor, and nuclear factor-κB signaling pathways are upregulated. Furthermore, and consistent with traditional siRNA immune activation, cytokine-cytokine receptor signaling was also upregulated. Overall, this study provides insight into NP-siRNA:hMSC ratios that are favorable for siRNA delivery. Moreover, NP-siRNA delivery results in side effects across the hMSC transcriptome that suggest activation of the innate immunity that could alter MSC functions associated with their therapeutic potential.

SUBMITTER: Malcolm DW

PROVIDER: S-EPMC5125403 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:RNA interference (RNAi) has immense potential to modulate cell functions. However, effective delivery of siRNA while avoiding deleterious side effects has proven challenging. This study investigates both intended and unintended effects of diblock copolymer nanoparticle (NP) delivery of siRNA delivery to human mesenchymal stem cells (hMSC). Specifically, siRNA delivery was investigated at a range of NP-siRNA:hMSC ratios with a focus on the effects of NP-siRNA treatment on hMSC functions. Additionally, next-generation RNA sequencing (RNA-seq) was used with enrichment analysis to observe side effects in hMSC gene expression. Results show NP-siRNA delivery is negatively correlated with hMSC density. However, higher NP-siRNA:hMSC ratios increased cytotoxicity and decreased metabolic activity. hMSC proliferation was largely unaffected by NP-siRNA treatment, except for a 3-fold reduction in hMSCs seeded at 4,000 cells/cm2. Flow cytometry reveals that apoptosis is a function of NP-siRNA treatment time and seeding density; ~14% of the treated hMSCs seeded at 8,000 cells/cm2 were annexin V+-siRNA+ 24 h after treatment, while 11% of the treated population was annexin V+-siRNA-. RNA-seq shows that NP-siRNA treatment results in transcriptomic changes in hMSCs, while pathway analysis shows upregulation of apoptosis signaling and downregulation of metabolism, cell cycle, and DNA replication pathways, as corroborated by apoptosis, metabolism, and proliferation assays. Additionally, multiple innate immune signaling pathways such as toll-like receptor (TLR), retinoic acid-inducible gene 1 (RIG-I)-like receptor, and nuclear factor-κB (NF-κB) signaling pathways are upregulated. Furthermore, and consistent with traditional siRNA immune activation, cytokine-cytokine receptor signaling was also upregulated. Overall, this study provides insight into NP-siRNA:hMSC ratios that are favorable for siRNA delivery. Moreover, NP-siRNA delivery results in side effects across the MSC transcriptome that suggest activation of the innate immunity that could alter MSC functions associated with their therapeutic potential.

Project description:BackgroundReproducibility of results can have a significant impact on the acceptance of new technologies in gene expression analysis. With the recent introduction of the so-called next-generation sequencing (NGS) technology and established microarrays, one is able to choose between two completely different platforms for gene expression measurements. This study introduces a novel methodology for gene-ranking stability analysis that is applied to the evaluation of gene-ranking reproducibility on NGS and microarray data.ResultsThe same data used in a well-known MicroArray Quality Control (MAQC) study was also used in this study to compare ranked lists of genes from MAQC samples A and B, obtained from Affymetrix HG-U133 Plus 2.0 and Roche 454 Genome Sequencer FLX platforms. An initial evaluation, where the percentage of overlapping genes was observed, demonstrates higher reproducibility on microarray data in 10 out of 11 gene-ranking methods. A gene set enrichment analysis shows similar enrichment of top gene sets when NGS is compared with microarrays on a pathway level. Our novel approach demonstrates high accuracy of decision trees when used for knowledge extraction from multiple bootstrapped gene set enrichment analysis runs. A comparison of the two approaches in sample preparation for high-throughput sequencing shows that alternating decision trees represent the optimal knowledge representation method in comparison with classical decision trees.ConclusionsUsual reproducibility measurements are mostly based on statistical techniques that offer very limited biological insights into the studied gene expression data sets. This paper introduces the meta-learning-based gene set enrichment analysis that can be used to complement the analysis of gene-ranking stability estimation techniques such as percentage of overlapping genes or classic gene set enrichment analysis. It is useful and practical when reproducibility of gene ranking results or different gene selection techniques is observed. The proposed method reveals very accurate descriptive models that capture the co-enrichment of gene sets which are differently enriched in the compared data sets.

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Evaluating side effects of nanoparticle-mediated siRNA delivery to mesenchymal stem cells using next generation sequencing and enrichment analysis.

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