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. 2024 Aug 17;14:19075. doi: 10.1038/s41598-024-69280-z

Optimization of a human induced pluripotent stem cell-derived sensory neuron model for the in vitro evaluation of taxane-induced neurotoxicity

Erica L Cantor 1, Fei Shen 1, Guanglong Jiang 2, Santosh Philips 1, Bryan P Schneider 1,✉
PMCID: PMC11330481  PMID: 39154055

Abstract

Human induced pluripotent stem cell-derived sensory neuron (iPSC-dSN) models are a valuable resource for the study of neurotoxicity but are affected by poor replicability and reproducibility, often due to a lack of optimization. Here, we identify experimental factors related to culture conditions that substantially impact cellular drug response in vitro and determine optimal conditions for improved replicability and reproducibility. Treatment duration and cell seeding density were both found to be significant factors, while cell line differences also contributed to variation. A replicable dose–response in viability was demonstrated after 48-h exposure to docetaxel or paclitaxel. Additionally, a replicable dose-dependent reduction in neurite outgrowth was demonstrated, demonstrating the applicability of the model for the examination of additional phenotypes. Overall, we have established an optimized iPSC-dSN model for the study of taxane-induced neurotoxicity.

Keywords: Neurotoxicity, Dose response, iPSC, Paclitaxel, Docetaxel

Subject terms: Experimental models of disease, Diseases of the nervous system, Peripheral nervous system

Introduction

Taxanes are a class of drugs effectively used in the treatment of various cancer types. However, taxane-induced peripheral neuropathy (TIPN) can negatively impact quality of life, often resulting in the early cessation of cancer treatment and compromising treatment outcome. Paclitaxel and docetaxel are two standard taxane therapies for early-stage breast cancer. They share major parts of their structures and mechanisms of action and demonstrate similar treatment effectiveness. Docetaxel, however, causes less symptoms of peripheral neuropathy than paclitaxel1–3 but with an increased risk of other toxicities4. These data have led to a prospective trial for Black patients at high risk of TIPN to determine the optimal taxane in terms of toxicity and impact on quality of life5. As the mechanism of TIPN is not fully understood, effective models to study neurotoxicity are in great need. Obtaining primary nerve tissue is often painful and potentially damaging to a patient, and the use of immortalized cell lines established by genetic modification of primary cells is associated with the accumulation of genetic and epigenetic abnormalities6–8. Induced pluripotent stem cells (iPSC) are a valuable resource for this purpose due to their ability to be reprogrammed directly from somatic tissue, therefore reflecting the inherited genetic variability of the patient from which they are derived9–14. These patient-derived iPSCs can then be differentiated into neurons for the in vitro assessment of taxane-induced neurotoxicity7,15–17.

iPSC-derived models, however, lack replicability and reproducibility, and require a considerable degree of optimization to improve data quality and translatability18–21. In this study, we optimized a previously established iPSC-derived sensory neuron model (iPSC-dSN)17,22 for the assessment of taxane-induced neurotoxicity based on in vitro treatment with both docetaxel and paclitaxel. We previously characterized iPSC-dSNs generated using this model via immunofluorescent staining with neuron markers and bulk RNA-sequencing22. Expression of neuron marker tubulin and peripheral neuron marker peripherin was detected via both immunofluorescence and bulk RNA-sequencing. Additionally, the expression of key sensory neuron marker genes was detected, with highest expression in cells differentiated from lower passage number iPSCs22. In the current study, we have investigated additional factors relating to iPSC-dSN culture and in vitro exposure to docetaxel with subsequent validation using paclitaxel. Additionally, changes in neurite outgrowth in response to taxane treatment were measured to demonstrate the suitability of the model for downstream mechanistic studies of TIPN.

Results

Effects of experimental conditions on the viability of iPSC-dSNs treated with docetaxel

To optimize the modeling of docetaxel treatment in iPSC-dSNs in vitro, four experimental parameters (cell line, treatment duration, cell seeding density, and seeding-treatment interval) were evaluated based on the averaged relative viability of four iPSC-dSN lines exposed to docetaxel compared to vehicle (Table 1).

Table 1.

Experimental conditions tested for the optimization of modeling treatment with docetaxel in iPSC-dSNs using a cell viability assay.

Independent factor Conditions tested
Cell line 4 cell lines
Treatment duration 24 or 48 h
Cell seeding density 10 k, 25 k, or 50 k cells/well
Seeding-treatment interval 1, 2, or 3 days
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Treatment duration (24 or 48 h) was a highly significant factor affecting iPSC-dSN viability (p-value = 1.22 × 10−67). Figure 1 represents dose–response curves based on a four-parameter logistic regression model. When examined separately, drug dose was a significant parameter in both the 24- and 48-h treatment duration groups (p-values = 7.61 × 10−4 and 1.31 × 10−97, respectively). However, only 48-h treatment caused a significant reduction in viability compared to vehicle-treated cells at 1–1000 nM docetaxel (p-values = 5.42 × 10−9–4.21 × 10−13) and demonstrated a consistent dose response with good fit to a sigmoid curve and superior replicability (Fig. 1). As 24-h treatment did not result in a consistent dose response, data from the iPSC-dSNs treated for 48 h were further analyzed separately to identify other significant experimental parameters.

Figure 1.

Figure 1

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Average dose–response curves of iPSC-dSNs treated with 0.01–1000 nM docetaxel for either 24 or 48 h derived from the average relative cell viability of four iPSC-dSN lines treated with the correspondent dose in three independent experiments using four-parameter logistic regression. Bars represent standard error.

iPSC-dSNs exposed to docetaxel for 48 h exhibited significantly reduced viability from 1 to 1000 nM compared to vehicle-treated cells (p-values = 5.42 × 10−9–4.21 × 10−13). The four cell lines varied significantly in overall viability (p-value = 0.00170) and dose response (p-value = 2.40 × 10–9), demonstrating the capability of the model to capture the impact of intra-individual differences on cellular response in vitro (Fig. 2). Cell seeding density also significantly affected overall viability (p-value = 6.85 × 10−4) and dose response (p-value = 7.39 × 10−5), however seeding-treatment interval did not significantly affect overall viability (p-value = 0.252) or dose response (p-value = 0.600) of iPSC-dSNs (Fig. 2). Figure 3 shows dose response curves and IC50 of iPSC-dSNs seeded at three densities (10k, 25k, or 50k cells/well of a 96-well plate). IC50 was directly correlated with cell seeding density, suggesting a decrease in sensitivity with increasing cell density.

Figure 2.

Figure 2

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The effect of covariates (cell line, seeding density, seeding-treatment interval) and their interactions with drug dose on iPSC-dSN response to docetaxel treatment for 48 h in four cell lines. The effects were determined by ANOVA. A p-value < 0.05 was considered significant. The red-dashed line represents the cutoff for significance.

Figure 3.

Figure 3

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Cell viability and IC50 of docetaxel for iPSC-dSNs. (a) Dose response curves and (b) IC50 of docetaxel at different cell seeding densities. Cells were seeded at either 10k, 25k, or 50k cells per well of a 96-well plate and were treated with docetaxel for 48 h. Data were obtained from four cell lines. Triplicate experiments were performed for each cell line at each seeding density. Bars represent standard error.

Replication of dose response to paclitaxel exposure

The optimized experimental conditions identified above were applied to the viability analysis of 15 iPSC-dSN lines treated with paclitaxel. iPSC-dSNs were seeded at a consistent density of 25k cells/well of a 96-well plate and treated for 48 h. Results show a notable dose response with good fit to a standard sigmoid curve, low variability, and high replicability (Fig. 4). A significant reduction in cell viability was observed at 10–1000 nM paclitaxel (p-values = 3.38 × 10−9–3.69 × 10−12). Of note, when comparing the averaged IC50 of fifteen cell lines, docetaxel had an IC50 of 4.43 nM compared to paclitaxel, with an IC50 of 10.35 nM, indicating a greater sensitivity of iPSC-dSNs to docetaxel in vitro (p-value = 6.16 × 10−5).

Figure 4.

Figure 4

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Dose–response curve for iPSC-dSNs seeded at 25k cells/well and treated with paclitaxel for 48 h derived from the average relative viability of 15 cell lines using four-parameter logistic regression, with at least four technical replicates per line. Bars represent standard error.

Demonstration of dose response in neurite outgrowth after taxane exposure

The optimized model was further tested by examining morphological responses of iPSC-dSNs to taxanes. Average neurite outgrowth was measured in three cell lines seeded at a consistent cell density (25k cells/well) and treated for 48 h with either docetaxel or paclitaxel. A consistent dose-dependent reduction in neurite outgrowth was exhibited after treatment with both docetaxel (p-value = 7.55 × 10−39 ) and paclitaxel (p-values = 9.82 × 10−28) with a significant reduction in outgrowth observed at 10–1000 nM for both docetaxel (p-values = 5.63 × 10−13–9.29 × 10−13) and paclitaxel (p-values = 5.74 × 10−6–6.29 × 10−13; Fig. 5).

Figure 5.

Figure 5

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Average neurite outgrowth of iPSC-dSNs. Cells were treated with either vehicle, (a) docetaxel, or (b) paclitaxel for 48 h. Three cell lines were included in each analysis, with three technical replicates included per dose. Dots represent individual replicates.

Discussion

The development of iPSC-derived neuron models has greatly facilitated the study of neurological disease. However, these models suffer from high degrees of variability, and low replicability and reproducibility, often due to poor optimization of experimental variables whose specific effects have not been thoroughly studied18–21,23. Optimization of those parameters significantly impacting experimental outcomes is crucial to improve the fidelity of these models and minimize potential sources of error. In this report, we have optimized a previously established iPSC-dSN model for the study of taxane-induced neurotoxicity by examining dose-dependent changes in cell viability under several different conditions. We first identified treatment duration, cell line, and cell seeding density as significant variables affecting the replicability of viability assays in iPSC-dSNs treated with docetaxel and determined the optimal conditions for studying taxane-induced neurotoxicity in vitro. We further replicated these conditions with paclitaxel exposure using the optimized model. Finally, we demonstrated a reliable dose-dependent reduction in neurite outgrowth in iPSC-dSNs treated with docetaxel and paclitaxel under these optimized experimental conditions.

For the optimization of the model, we investigated the effect of each experimental variable alone, along with variable docetaxel doses. The interaction between each variable and drug dose were more biologically relevant, as they represent dose-dependent changes in viability, and therefore were the focus of the analysis. Determination of an optimal duration of treatment to generate a dose response is highly relevant to in vitro toxicity assays attempting to recapitulate the in vivo environment. 24- and 48-h treatment durations have been shown to induce neurotoxicity without complete cell kill and are commonly used in taxane-based studies6,15,24–36. Our results found that treatment duration significantly impacted the response of iPSC-dSNs to docetaxel exposure, with 48-h treatment resulting in a replicable dose–response. Cell line and seeding density were also found to be significant variables. The iPSC-dSN cell lines were genetically heterogenous, which may have accounted for some variation in their response to drug exposure. Genetic differences are well known to be drivers of variation in in vitro models and the ability to capture and account for this variation is critically important20,37–40. Additionally, a significant difference in drug response was noted with larger differences in cell seeding density. Our results indicate that cell sensitivity to docetaxel decreased with increasing cell density. These data are consistent with other studies21,39,41,42 and suggest that, at a minimum, cell seeding density should be held constant across experiments.

To test the reproducibility of the optimized model, viability experiments were repeated with paclitaxel, which has been shown to cause a dose-dependent reduction in iPSC-dSN viability7,17,35,36. In the current study, iPSC-dSNs treated with paclitaxel using the optimized model demonstrated a sigmoidal dose response curve with low variability across 15 cell lines. IC50s of paclitaxel and docetaxel were concordant with previous studies that demonstrated a greater sensitivity to docetaxel in vitro43–45. We then applied the optimized model to examine the alteration of a morphological phenotype of taxane-induced neurotoxicity, neurite outgrowth, a phenotype that has been given a lot of attention in in vitro neurotoxicity studies15,46–51. Several studies have demonstrated that paclitaxel causes a dose-dependent reduction in outgrowth in iPSC-dSNs7,15,17,50. Using the optimized model, we were able to demonstrate a dose-dependent reduction in average neurite outgrowth by docetaxel and paclitaxel individually, with low variability across cell lines. These results demonstrate the reliability of the optimized iPSC-dSN model for producing replicable drug responses in vitro.

The results of this study are concordant with other in vitro drug toxicity studies highlighting similar significant experimental parameters affecting dose response19,21,39,52. However, our study specifically examines these parameters in the context of an iPSC-derived sensory neuron model, identifying optimal conditions for the evaluation of taxane-induced neurotoxicity. A major strength of this work is that the iPSC lines were derived from high-risk patients enrolled in a prospective clinical trial with a specific focus on the study of TIPN. Thus, meticulous dosing information and follow-up were available to allow for comparisons between the patient’s actual clinical experience with in vitro conclusions. The translation of clinical phenotypes to the in vitro environment, however, has not been thoroughly demonstrated. Comprehensive comparisons are planned future work for our group. In addition, studies including cell lines derived from variable demographic backgrounds (variable risk groups, races, ages, etc.) would be of high utility. Furthermore, while the selection of variables for optimization in this study was carefully considered, technical requirements limit our ability to include a wider variety of parameters to study. Future studies including even more experimental parameters would further improve the reliability and applicability of the model.

The optimized model developed in this study can be utilized to reliably assess taxane-induced neurotoxicity using either docetaxel or paclitaxel. This will provide a valuable tool that can be applied in various contexts. Further examination of phenotypes such as neurite outgrowth and gene expression in patient-derived cells will allow for the comparison of clinical outcomes to in vitro cellular response to drug exposure. This could provide insight into the mechanisms underlying TIPN, as well as allow for the identification of potential new targets for treatment.

In conclusion, the optimization of experimental conditions is critical to ensure replicability and reproducibility of findings. Significant experimental factors may not only be a source of technical variation but could also provide insight into biological differences in cellular drug response and metabolism, thus increasing our understanding of these mechanisms and improving translatability. The results of this study provide the framework of an optimized iPSC-derived sensory neuron model and demonstrate its suitability for the study of neurotoxicity in vitro, providing a guideline for similar models. Controlling for as many sources of variation as possible will improve the likelihood of detecting real, biologically meaningful drug responses, while also providing further insight into drug response mechanisms.

Methods

Generation of iPSCs

Peripheral blood samples were obtained with informed, written consent as part of the EAZ171 trial (NCT04001829) under the approval of Indiana University Internal Review Boards 1907076233 and all experiments were performed in accordance with the relevant guidelines and regulations. All patients were female and of African ancestry within the age range of 33–72 (Supplemental Table 1).iPSCs were reprogrammed from peripheral blood mononuclear cells (PBMCs) using the CytoTune™-iPS 2.0 Sendai Reprogramming Kit (Invitrogen™), as previously described22. Briefly, PBMCs were isolated from peripheral blood using the BD Vacutainer® CPT™ Tube with Sodium Citrate (BD Biosciences) and expanded in complete StemPro™-34 medium (STEMCELL Technologies Inc.) for four days. The PBMCs were then transduced with Sendai virus vectors containing the four Yamanaka factors. Virus was removed via centrifugation the following day and transduced cells were maintained on Matrigel-coated plates for seven days. Cells were then transitioned into mTeSR™ Plus medium (STEMCELL Technologies, Inc.) and maintained until iPSC colony formation.

Maintenance and measurement of pluripotency of iPSC lines

The maintenance of iPSCs and measurement of pluripotency were performed as previously described22. After reaching an appropriate size for transfer, iPSC colonies were selected and maintained in mTeSR™ Plus medium. The medium was replaced daily and any spontaneously differentiating cells were removed manually. Cells were then passaged via single-colony subcloning for the first five passages in order to derive vector-free iPSCs. After the absence of Sendai virus was confirmed via immunofluorescent staining with an anti-Sendai virus antibody (MBL International Corporation), G-banded cytogenetic analysis was performed to confirm normal karyotypes (46, XX) of the iPSCs before they were further passaged one to two times per week using Dispase (1 U/mL in DMEM/F-12; STEMCELL Technologies, Inc.) The pluripotency of cells was measured with flow cytometry using the Human Pluripotent Stem Cell Transcription Factor Analysis Kit (BD Biosciences). The expression of the three core pluripotency transcription factors (Oct3/4, Sox2, and Nanog) was assessed graphically, with pluripotency defined by an average of at least 80% of cells expressing all three markers. Unstained and isotype controls were included with every sample.

Differentiation of sensory neurons

Pluripotent, vector-free iPSCs were induced into sensory neuronal differentiation at ~ 80% confluence as previously described17,22. Cells were plated in 6-well Matrigel-coated plates and the absence of mycoplasma contamination was confirmed before induction. Cells were then maintained in induction medium for eight days, with complete medium replacement every other day. On day nine post-induction, cells were dissociated from the plate, strained through a 37 µm mesh, and single-cell seeded onto new plates. Cells were maintained thereafter in a maintenance medium with complete medium replacement every other day.

Viability analysis

In the fifth week post-induction, iPSC-dSNs were passaged onto white, opaque-walled Matrigel-coated 96-well plates for luminescence-based viability analyses. Four iPSC-dSN lines were treated with docetaxel. Cells were seeded at three different densities: 10k, 25k, and 50k cells per well for either one, two, or three days, and then treated for 24 or 48 h independently with 0.01–1000 nM docetaxel or a matched vehicle control (DMSO) that corresponded to the amount of DMSO used in each dose (Table 1). Three replicates were included for each experimental condition, edge wells were omitted to avoid excess evaporation, and blank wells were included to determine background luminescence. Upon optimization, the viability analyses were performed in 15 cell lines exposed to 0.01–1000 nM paclitaxel after cells were seeded for two days at 25k cells per well.

Cell viability was assessed using the CellTiter-Glo® 2.0 Assay (Promega) and luminescence was measured on a Cytation 5 Cell Imaging Multimode Reader (BioTek Instruments, Inc.) with an integration time of 1 s. Background luminescence was subtracted from raw values and data were normalized to matched vehicle controls.

Imaging and measurement of neurite outgrowth

During the 5th week post-induction, iPSC-dSNs were dissociated using Accutase™ (STEMCELL Technologies, Inc.) and enriched using magnetic-activated cell sorting (MACS) via positive selection with anti-PSA-NCAM MicroBeads (Miltenyi Biotec). Cells were then seeded at a density of 1.25 × 105 cells per well in Matrigel-coated 12-well plates for 48 h before exposure to a taxane. iPSC-dSNs were treated with either vehicle or 0.01–1000 nM docetaxel or paclitaxel for 48 h before being fixed in 4% paraformaldehyde for immunofluorescent staining. Nuclei were stained with NucBlue Fixed Cell ReadyProbes Reagent (1:1000; Invitrogen) and neurites were stained for peripheral neuron marker βIII-tubulin (1:1000; R & D Systems). Three replicates were included for each drug concentration.

Images were taken using the Lionheart FX Automated Microscope (BioTek Instruments, Inc.) at 4 × magnification. Neurite outgrowth was measured using the MetaMorph Microscopy Automation and Image Analysis (Molecular Devices) software; nine images were analyzed per experimental replicate. Average neurite outgrowth was then calculated as the sum of total neurite outgrowth divided by the total number of cells from the nine images.

Statistical analyses

ANOVA was used to evaluate the effect of experimental parameters on cell viability, including cell line, cell seeding density, taxane dose, treatment duration, and seeding-treatment interval (time between seeding and treating cells) in four cell lines. Relative cell viability was calculated as % viability of the treated cells at each dose compared to the averaged matched vehicle-treated cell viability. A post-hoc Tukey test was used to examine specific comparisons for main effects when applicable (cell line, cell seeding density, dose, and seeding-treatment interval). Two-way interactions were also included in the model to examine differences in dose response with cell line, seeding density, and seeding-treatment interval. ANOVA was also used to assess dose response to paclitaxel and in neurite outgrowth, with 15 cell lines included in each analysis. A post-hoc Tukey test was again used to examine specific comparisons. Significance was determined as p-value < 0.05.

Dose–response curves and IC50 (dose of taxane resulting in a 50% reduction of iPSC-dSN viability) were generated using the Quest Graph™ IC50 Calculator53. The calculator fits the data using a four-parameter logistic regression model, which typically results in a sigmoid-shaped curve.

Supplementary Information

Supplementary Table 1. (122.3KB, pdf)

Acknowledgements

We thank our donors for donating blood for this study. The study was supported by Susan G. Komen for the Cure and the Vera Bradley Foundation for Breast Cancer.

Author contributions

All authors contributed to the conceptualization and design of the study, data interpretation, and manuscript revisions. E.C. performed experiments, data acquisition, data analysis, and primary authorship of the manuscript. F.S. and B.P.S. conducted critical review of the manuscript. B.P.S. acquired funding for the study.

Funding

The study was funded by Susan G. Komen for the Cure, SAC232156.

Data availability

The corresponding author can provide the datasets utilized in this study on reasonable request.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-69280-z.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 1. (122.3KB, pdf)

Data Availability Statement

The corresponding author can provide the datasets utilized in this study on reasonable request.

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