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Published in final edited form as: Stem Cell Res. 2021 Oct 12;57:102572. doi: 10.1016/j.scr.2021.102572

Quantitative analysis of mitochondrial morphologies in human induced pluripotent stem cells for Leigh syndrome

Fibi Meshrkey a,b,c, Ana Cabrera Ayuso b, Raj R Rao b,d, Shilpa Iyer a,b,*
PMCID: PMC10332439  NIHMSID: NIHMS1896210  PMID: 34662843

Abstract

Mitochondria are dynamic organelles with wide range of morphologies contributing to regulating different signaling pathways and several cellular functions. Leigh syndrome (LS) is a classic pediatric mitochondrial disorder characterized by complex and variable clinical pathologies, and primarily affects the nervous system during early development. It is important to understand the differences between mitochondrial morphologies in healthy and diseased states so that focused therapies can target the disease during its early stages. In this study, we performed a comprehensive analysis of mitochondrial dynamics in five patient-derived human induced pluripotent stem cells (hiPSCs) containing different mutations associated with LS. Our results suggest that subtle alterations in mitochondrial morphologies are specific to the mtDNA variant. Three out of the five LS-hiPSCs exhibited characteristics consistent with fused mitochondria. To our knowledge, this is the first comprehensive study that quantifies mitochondrial dynamics in hiPSCs specific to mitochondrial disorders. In addition, we observed an overall decrease in mitochondrial membrane potential in all five LS-hiPSCs. A more thorough analysis of the correlations between mitochondrial dynamics, membrane potential dysfunction caused by mutations in the mtDNA in hiPSCs and differentiated derivatives will aid in identifying unique morphological signatures of various mitochondrial disorders during early stages of embryonic development.

Keywords: Mitochondrial dynamics, Mitochondrial morphology, Mitochondrial disorders, Mitochondrial membrane potential, Human induced pluripotent stem cells, Leigh syndrome

1. Introduction

Mitochondria are extremely dynamic organelles essential for a host of cellular functions that include regulation of apoptosis and metabolism, and generation of signaling metabolites (Sprenger and Langer, 2019). In live cells, studies have shown that ATP production is regulated by remodeling of the mitochondria (McCarron et al., 2013). In viable healthy cells, the mitochondrial structure is dependent on specific cell types and function (McCarron et al., 2013). The dynamic nature of the mitochondria contributes to rounds of fission (division) and fusion events to replenish the content of the organelle and maintain structural integrity to perform their function (Picard et al., 2013; Ni et al., 2015). Alterations in mitochondrial morphology can cause bioenergetic defects, and underlie a heterogeneous group of human diseases including myopathies (Wallace, 1999); neurodegeneration (Liu and Hajnoczky, 2009; Gao et al., 2017), cancer (Chan, 2006), diabetes mellitus (Ogawa et al., 2003; Duchen, 2004), and a host of other disorders (Picard et al., 2013). Owing to the involvement of mitochondrial dynamics in regulating cellular functions, studies have focused on understanding the relationship between the morphology and function of the mitochondria in health and disease.

Much is still unknown about the role of mitochondrial dynamics in human diseases and its specific role during early development. However, it has been suggested that in response to internal and external environmental cues or in the diseased state, mitochondrial fission and fusion may be modulated as a compensatory mechanism to maintain the pool of healthy mitochondria within cells (Ni et al., 2015; Nakada et al., 2001; Yoneda et al., 1994; Rolland et al., 2013). Mitochondrial fusion allows for replenishment of depleted cellular resources such as lipids, and proteins between organelles, while fission generates new organelles and is involved in mitochondrial quality control (Youle and van der Bliek, 2012). Recent studies have indicated the importance of mitochondrial network regulation as being key for cell fate decisions, such as self-renewal and pluripotency (Rasmussen et al., 2018; Khacho et al., 2017; Salisbury-Ruf et al., 2018). Specific studies have shown that human pluripotent stem cells (hPSCs) that include human embryonic stem cells (hESCs) and human induced pluripotent stem cells (hiPSCs) exhibit punctate mitochondria with immature inner membrane cristae and evidence of reduced mitochondrial functionality (Zhang et al., 2011; Prigione et al., 2010; Varum et al., 2011; Son et al., 2015; Wang et al., 2017). In addition, these studies have demonstrated the presence of granular mitochondrial morphology in hPSCs in contrast with elongated mitochondrial networks in somatic cells; and also related to regulation of specific mitochondrial fusion or fission proteins (Son et al., 2015; Son et al., 2013).

While there is now emerging evidence on the active role of mitochondrial network regulating the pluripotent state and reprogramming events (Zhang et al., 2018; Dahan et al., 2019; Rastogi et al., 2019), not much is known about the involvement of mitochondrial dynamics in mitochondrial disorders that result from specific perturbations in the mitochondrial genome. It is widely acknowledged that errors in mitochondrial DNA (mtDNA) encoding proteins that make up any of the respiratory chain subunits could result in inborn errors of metabolism. A few examples of mtDNA mutations that cause mitochondrial disorders as well as impact Complex I function; include 10158 T > C in the MTND3 gene (McFarland et al., 2004), 12706 T > C in the MTND5 gene (Kirby et al., 2003), while those that impact Complex V function include 8993 T > G and 9185 T > C in the MTATP6 gene (Uziel et al., 1997; D’Aurelio et al., 2010; Baracca et al., 2007).

Pediatric Leigh syndrome (LS) (OMIM #256000) is a classic mitochondrial disorder that affects mental and motor function, wherein disease severity and developmental delay is influenced by mutant mtDNA burden (heteroplasmy or mixtures of normal and mutant mtDNA) resulting in fatality from respiratory failure early in life (Loeffen et al., 2000; Leigh, 1951). Patients with LS also have multisystemic problems that affect the cardiac, renal, hepatic and gastrointestinal organs. The heterogeneous nature of LS can be attributed in part to the complex nature of the mitochondrial electron transport chain (ETC), composed of subunits that are encoded by both nuclear DNA and mtDNA, with mutations in either genomes coding for different ETC subunits resulting in LS (Bakare et al., 2021; Lake et al., 2016; Nesbitt et al., 2012; Rahman and Thorburn, 1993; Zinovkin et al., 2016). Many of the mtDNA mutations listed above are found in cells and tissues in LS patients. The link between mtDNA mutations, mitochondrial biochemical and morphological changes during early development is not well known and needs investigation. The generation of patient and disease-specific hiPSCs has emerged as an exciting opportunity, to understand the role of mtDNA defects, bioenergetics dysfunction and mitochondrial morphological changes in early stages of development and disease progression. LS-hiPSCs have been generated by our group (Grace et al., 2019) and others (Sequiera et al., 2020; Sequiera et al., 2020; Galera-Monge et al., 2020; Hattori et al., 2016; Ma et al., 2015; Son et al., 2021; Galera et al., 2016; Galera-Monge et al., 2016; Inak et al., 2021) and can serve as excellent models to understand early human development and mitochondrial diseases (Grace et al., 2019; Folmes et al., 2013; Cherry et al., 2013). Our study has demonstrated that reprogrammed hiPSCs display self-renewal and differentiation potential, while harboring the LS-specific mtDNA variants (Grace et al., 2019).

In this study, we used the mitochondrial network morphology analysis (MiNA) Image J tool (Valente et al., 2017), to analyze different mitochondrial shapes in fluorescently labeled hiPSCs generated from LS-patient fibroblasts with specific mtDNA mutations. Live-cells stained with MitoTracker Red CM-H2Xros (MTR), a dye that localizes in actively respiring mitochondria, were analyzed to quantify mitochondrial morphology in multiple hiPSCs. We analyzed one control healthy BJ-hiPSC and five diseased LS-hiPSC lines carrying various mtDNA mutations. The diseased cell lines carry some of the most prevalent mutations involved in (LS) (Iyer et al., 2009). We further analyzed mitochondrial membrane potential in all our cell samples because studies have demonstrated that inner mitochondrial membrane potential mediated the formation of networks independent of a functional respiratory chain (Legros et al., 2002). We observed that LS-hiPSCs exhibited a decrease in mitochondrial membrane potential (MMP) and various remodeling of mitochondria when compared with control BJ-hiPSCs. To our knowledge, this is the first study that quantifies mitochondrial structure and function in early developmental in vitro models for LS.

2. Materials and methods

2.1. Fibroblast and stem cell culture

Cultures of healthy control BJ (ATCC® CRL-2522™) fibroblasts were obtained from American Type Culture Collection (ATCC, Manassas, VA) and the five patient-derived diseased fibroblasts (SBG1, SBG2, SBG3, SBG4, SBG5) were obtained from the Medical University of Salzburg, Austria. The patient fibroblast cell lines were obtained from five different patients with Leigh syndrome (LS) or related symptoms (Bakare et al., 2021). Informed consent was obtained to use these samples for research in an anonymized way. In accordance with federal regulations regarding the protection of human research subjects (32 CFR 219.101(b)(4)), the University of Arkansas Office of Research Compliance determined that the project was exempt from Institutional Review Board (IRB) oversight and human research subjects protection regulations.

Three of the patient fibroblast cell lines carry point mutations in the MTATP6 gene (SBG1, SGB2: 8993 T > G, SBG3: 9185 T > C) that impact complex V (ATP synthase). Two of the patient cell lines have mtDNA mutations in the MTND3 (SBG4: 10158 T > C) and MTND5 (SBG5: 12706 T > C) genes that impact complex I subunit of the electron transport chain. These mutations affecting complex I and V of the ETC have been implicated in LS (Baracca et al., 2007; Iyer et al., 2009; Piekutowska-Abramczuk et al., 2018; Taylor et al., 2002; Tilokani et al., 2018). These cells were maintained in a fibroblast expansion medium that consisted of minimal essential medium (MEM) (Cat#11380037, Thermo Fisher Scientific, Waltham, MA), 10% fetal bovine serum (FBS) (Cat# SH30071.04IH25–40; GE Healthcare - HyClone™, Chicago, IL), and 2 mM L-glutamine (Cat# 25030081, Thermo Fisher Scientific, Waltham, MA). Fibroblasts were enzymatically passaged in 0.05% Trypsin-EDTA (Cat# 15400054, Thermo Fisher Scientific, Waltham, MA).

Reprogramming was conducted using a combination of non-modified reprogramming and immune evasion RNAs using established protocols in our laboratory (Grace et al., 2019). Once reprogrammed, all six hiPSCs (1 control BJ-hiPSC and 5 diseased SBG1, SBG2, SBG3, SBG4, SBG5-hiPSC) were maintained in NutriStem hPSC xeno-free (XF) medium (Cat# 05–100–1A, Biological Industries, Cromwell, CT) with Ste-molecule Y27632 Dihydrochloride Hydrate (Cat# 04–0112-H-10, Reprocell, Beltsville, MD) on a highly purified and refined laminin-511 E8 fragment matrix, iMatrix-511 (Cat# NP892–011, Reprocell, Beltsville, MD) on a 24-hour feeding schedule. hiPSCs were enzymatically passaged once reaching 70 – 80% confluency at a split ratio of 1:3 using StemPro® Accutase® (Cat# A1110501, Thermo Fisher Scientific, Waltham, MA). Both fibroblast and hiPSC cultures were maintained without the use of antibiotics, handled in Biosafety Type II sterile hoods regularly cleaned with UV irradiation and 70% ethanol, and grown in 37 °C incubators at 5% CO2 and 95% humidity. Prior to use in experimentation, hiPSCs were seeded at a density of 150,000 cells per 35 mm dish and incubated for 48 h to ensure proper attachment.

2.2. Fluorescence labeling of mitochondria and live-cell microscopy

To label the mitochondria, hiPSCs were incubated with stem cell medium (detailed in Section 2.1) containing 100 nM Mitotracker Red CM-H2Xros (Cat# M7513, Thermo Fisher Scientific, Waltham, MA) for 30mins. At the end of the incubation period, the cells were washed 3x times with pre-warmed Dulbecco’s phosphate-buffered saline (dPBS). The nucleus was stained by further incubating cells with basal medium containing Nucblue Hoechst (Cat# R37605, Thermo Fisher Scientific, Waltham, MA) for 15mins. Following this incubation, cells were washed several times with pre-warmed dPBS to remove excess dye. At the end of the wash, phenol-red-free stem cell medium was added to each dish prior to image acquisition.

Fluorescence images of live cells were acquired using an EVOS FL inverted light/epifluorescence microscope with 40X/0.65 objective and a Sony ICX445 monochrome CCD digital camera. Red fluorescence from Mitotracker Red CM-H2Xros was measured using a 530 nm excitation and a 593 nm emission filter set. Blue fluorescence from Nucblue Hoechst was measured using a 360 nm excitation and a 447 nm emission filter set. All live cells were imaged on 35 mm dishes containing phenol-red-free basal medium. Image acquisition was performed one dish at a time with a maximum time of 30 min between dishes. All dishes were stored in a humidified 37 °C, 5% CO2 incubator until image acquisition. All images of live cells were taken on the same day as the labeling of mitochondria. All live-cell images were exported as TIFF files for further analysis. 5–7 images were acquired per dish and three dishes were stained per trial. Three independent trials were performed for each of the hiPSC lines used in the study.

Mitochondrial morphology and network analysis

The images generated for the hiPSCs were pre-processed on Image-J following steps previously outlined (Valente et al., 2017; Bakare et al., 2021). After pre-processing, the images were skeletonized. Post-skeletonization, images were segmented using Adobe Photoshop CC 2018. These segmented images were opened in Image-J and the MiNA macros were used to quantify mitochondrial morphological parameters of each segmented image. Since hiPSC lines have different colony morphologies, we normalized the parameters generated through MiNA by the cell surface area and the number of cells, which was also measured using Image-J.

2.3. Mitochondrial membrane potential (MMP) analysis

In this study, all hiPSCs were maintained in culture following established protocols detailed in Section 2.1. Prior to use in imaging analysis, hiPSCs were prepared as detailed in Section 2.1. On the day of the experiment, cells were incubated in stem cell medium, with desired amount of tetramethylrhodamine, ethyl ester (Cat# ab113852, TMRE-Abcam, Cambridge, MA, USA) added (for a final concentration of 50 nM). Cells were incubated with TMRE in a 37 °C 5% CO2 incubator for 30 min. At the end of the incubation period, cells were resuspended in 1x dPBS solution for 5 min to wash off the excess dye; and subsequently resuspended in phenol-red free stem cell medium. Fluorescence images of live cells were acquired using an EVOS FL inverted light/epifluorescence microscope with 40X/0.65 objective and a Sony ICX445 monochrome CCD digital camera. Red fluorescence from TMRE was measured using a 488 nm excitation and a 575 nm emission filter set. Blue fluorescence from Nucblue Hoechst was measured using a 360 nm excitation and a 447 nm emission filter set. All live-cell images were exported as TIFF files for further analysis with ImageJ. 5–7 images were acquired per dish and three dishes were stained per trial. Three independent trials were performed for each of the hiPSC lines used in the study. Mean fluorescent intensity (MFI) values, a measure of the geometric mean of TMRE positive cells was obtained for statistical analysis.

2.4. Statistical analysis

To ensure scientific rigor and reproducibility, an ANOVA design accounting for at least 3 biological and 5–7 images from control (BJ-hiPSC) and diseased (SBG1, SBG2, SBG3, SBG4, SBG5- LS- hiPSCs) was used to identify any differences with respect to control BJ hiPSCs for the MiNA analysis. Similarly, for MMP analysis, an ANOVA design accounting for at least three images analyzed from three independent dishes from two independent experiments from control (BJ-hiPSC) and diseased (SBG1, SBG2, SBG3, SBG4, SBG5- LS-hiPSCs) was used to identify any differences with respect to control BJ hiPSCs. Post-hoc Tukey HSD test was used to identify differences among specific groups. Data are presented as the mean ± standard deviation (SD) and were analyzed using the GraphPad Prism 8 program (GraphPad Software, San Diego, CA, USA). A p < 0.05 was considered significant.

3. Results

3.1. Descriptors of mitochondrial morphology in hiPSCs

An understanding of mitochondrial morphology is important in determining the health status of the cell with descriptors such as tubular, fragmented, and hyperfused (Zemirli et al., 2018) often used to characterize mitochondrial morphology. In a recently published study with patient fibroblasts containing mutations and deletions related to different mitochondrial diseases, we have demonstrated the utility of the Mitochondrial Network Analysis (MiNA) toolset, a relatively simple pair of macros making use of existing ImageJ plug-ins, that allows for semi-automated analysis of mitochondrial morphologies in cultured mammalian cells (Valente et al., 2017). The study demonstrated the usefulness of identifying different mitochondrial morphologies in healthy and diseased fibroblast cells to gain a better understanding of mitochondrial dynamics as it relates to cellular health.

In our current study, we maintained both the control BJ and diseased LS-hiPSCs in medium containing the Y-27632, a cell-permeable, highly potent and selective inhibitor of Rho-associated, coiled-coil containing protein kinase (ROCK). The presence of Y-27632 in the medium enhances survival of hiPSCs when they are dissociated to single cells by preventing dissociation-induced apoptosis. Thus, by promoting survival and cloning efficiency, maintaining both the control BJ and diseased LS-hiPSCs in a monolayer permitted detailed analysis of the mitochondrial network using the MiNA tool. In ongoing studies in our laboratory (Bakare and Iyer, Personal communication) and in a recent publication (Grace et al., 2019), we have demonstrated that the reprogrammed hiPSCs expressed pluripotent stem cell markers including transcription factors POU5F1, NANOG, and SOX2 and cell surface markers SSEA4, TRA-1–60, and TRA-1–81 at the protein level. Sanger sequencing analysis confirmed presence of mutations in all reprogrammed hiPSCs. In addition, cytogenetic analyses confirmed presence of normal karyotype in all reprogrammed hiPSCs. Patient-derived hiPSCs also demonstrated decreased maximal mitochondrial respiration, with reduced spare respiratory capacity in all LS- hiPSCs.

We observed that the mitochondria in hiPSCs are usually characterized by perinuclear distribution in a clustered form (Supplementary Fig. 1a). MiNA combines rods, punctate, and large/round shaped morphology in a group as ‘Individuals’, while branched morphologies are categorized as ‘networks’ (Supplementary Fig. 1). The program skeletonized the MitoTracker Red images (Supplementary Fig. 1a) and produces a collection of outputs of mitochondrial descriptors; such as the number of individuals and networks, mean branch size, mean branch length and network size. Individuals include rods and punctates and involve singular mitochondria that do not share pixelated area with other mitochondria, while networks are a group of mitochondria that share a connection point (Supplementary Fig. 1b). Using the MiNA macros, we were able to observe the dynamic nature of mitochondria in BJ healthy control hiPSCs. Based on our analysis, we determined that mitochondrial morphologies in BJ-hiPSCs consisted of 84.46% individuals and 15.54% networks.

It is important to note that mitochondria can take different morphologies such as branched networks, tubular networks or small fragmented forms depending on the cellular status. Previous studies have indicated fragmented mitochondria to be the predominant morphology associated with mitochondrial dysfunction (Kiryu-Seo et al., 2016; Rambold et al., 2011), while fused mitochondria are associated with cell survival mechanisms (Wai and Langer, 2016; Debray et al., 2007). It is therefore imperative that we identify these different morphologies to further delineate their contributions to cellular health and disease, in the context of mitochondrial disorders, especially during early developmental stages, by using hiPSCs. In this study, we have focused our efforts on better understanding the impact of the different mtDNA mutations that are correlated with LS and to specifically investigate the mitochondrial dynamics in the hiPSC state during early development.

3.2. hiPSCs with 8993 T > G, 9185 T > C mutations impacting Complex V exhibit higher mitochondrial footprint while exhibiting variable morphologies that is variant-specific

Having determined the utility of the MiNA toolset to detect different mitochondrial morphologies in control BJ hiPSCs, we decided to analyze differences between control and diseased cell lines carrying mutations in subunits of the Complex V-ATP synthase. We analyzed three patient-derived hiPSCs (SBG1 and SBG2: 8993 T > G; SBG3: 9185 T > C) carrying mutations that are most prevalent in patients with LS (Castagna et al., 2007; Vodopivec et al., 2016). As was done with the control BJ-hiPSCs, we stained the diseased hiPSCs with the mitochondrial specific stain (MitoTracker Red), obtained fluorescent images, skeletonized and analyzed the images (Figs. 1a, 2a and 3a). Our analysis demonstrated that all three diseased hiPSCs exhibited a statistically significant increased skeletal area (denoting a higher mitochondrial occupying area), when compared with control BJ-hiPSCs (Figs. 1b, 2b and 3b). However, based on our analysis, we determined that mitochondrial morphologies in SBG1-(8993 T > G)-hiPSCs contained 82.28% individuals and 17.72% networks; SBG2-(8993 T > G)-hiPSCs contained 84.85% individuals and 15.15% networks and SBG3- (9185 T > C)-hiPSCs contained 82.18% individuals and 17.82% networks respectively.

Fig. 1.

Fig. 1.

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Mitochondrial morphology of SBG1-(8993 T > G)-hiPSC: a) Representative images of SBG1-(8993 T > G)-hiPSC stained with MTR. From left to right are the phase contrast, Nucblue, RFP, overlay, and skeletonized images including the area of interest of SBG1-(8993 T > G)-hiPSC. Different mitochondrial morphological parameters were determined using MiNA, normalized, and analyzed in comparison to the BJ-hiPSC (control cell line) to quantify: b) the mitochondrial footprint or the skeletal area occupied by the mitochondria c) number of individuals d) number of networks e) mean branch length f) network size or the mean branch number per network. All data are representative of 5–7 analyzed images obtained from 7 to 9 independent dishes from three independent experiments. The bars represent minimum and maximal values including all points, and each black dot represents different data points. ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05. Scale bar = 100 μm.

Fig. 2.

Fig. 2.

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Mitochondrial morphology of SBG2-(8993 T > G)-hiPSC: a) Representative images of SBG2-(8993 T > G)-hiPSC stained with MTR. From left to right are the phase contrast, Nucblue, RFP, overlay, and skeletonized images including the area of interest of SBG2-(8993 T > G)-hiPSC. Different mitochondrial morphological parameters were determined using MiNA, normalized, and analyzed in comparison to the BJ-hiPSC (control cell line) to quantify: b) the mitochondrial footprint or the skeletal area occupied by the mitochondria c) number of individuals d) number of networks e) mean branch length f) network size or the mean branch number per network. All data are representative of 5–7 analyzed images obtained from 7 to 9 independent dishes from three independent experiments. The bars represent minimum and maximal values including all points, and each black dot represents different data points. ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05. Scale bar = 100 μm.

Fig. 3.

Fig. 3.

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Mitochondrial morphology of SBG3-(9185 T > C)-hiPSC: a) Representative images of SBG3-(9185 T > C)-hiPSC stained with MTR. From left to right are the phase contrast, Nucblue, RFP, overlay, and skeletonized images including the area of interest of SBG3-(9185 T > C)-hiPSCs. Different mitochondrial morphological parameters were determined using MiNA, normalized, and analyzed in comparison to the BJ-hiPSC (control cell line) to quantify: b) the mitochondrial footprint or the skeletal area occupied by the mitochondria c) number of individuals d) number of networks e) mean branch length f) network size or the mean branch number per network. All data are representative of 5–7 analyzed images obtained from 7 to 9 independent dishes from three independent experiments. The bars represent minimum and maximal values including all points, and each black dot represents different data points. ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05. Scale bar = 100 μm.

In SBG1-(8993 T > G)-hiPSCs, we observed a statistically significant increase in the number of networks (20%, p < 0.01) (Fig. 1d), mean branch length (13%, p < 0.0001) (Fig. 1e) and network size (15%, p < 0.01) (Fig. 1f), when compared with control BJ-hiPSCs. Although not statistically significant, we observed a 3% increase in the number of individuals in SBG1-(8993 T > G)-hiPSCs when compared with control BJ-hiPSCs (Fig. 1c). In SBG2-(8993 T > G)-hiPSCs, we observed a statistically significant increase in the number of individuals (31%, p < 0.0001) (Fig. 2c), and number of networks (27%, p < 0.0001) (Fig. 2d), when compared with control BJ-hiPSCs. Although not statistically significant, we observed a 9% increase in network size (Fig. 2f) and no change in mean branch length (Fig. 2e) in SBG2-(8993 T > G)-hiPSCs, when compared with control BJ-hiPSCs. In SBG3-(9185 T > C)-hiPSCs, we observed a statistically significant increase in mean branch length (49%, p < 0.0001) (Fig. 3e) and network size (67%, p < 0.0001) (Fig. 3f), when compared with control BJ-hiPSCs. We also observed a statistically significant decrease in number of individuals (13%, p < 0.05) (Fig. 3c) in SBG3-(9185 T > C)-hiPSCs when compared with control BJ-hiPSCs. Although not statistically significant, we observed a 3% increase in the number of networks in SBG3-(9185 T > C)-hiPSCs when compared with control BJ-hiPSCs (Fig. 3d). Together these results indicate that the presence of the same mutations could result in different compensatory responses by the mitochondria and variable bioenergetic demand within the hiPSCs could result in different mitochondrial morphologies.

3.3. hiPSCs with 10158 T > C, 12706 T > C mutations impacting Complex I exhibit higher mean branch length and network size

We next wanted to determine how specific mutations in other complexes that influence mitochondrial function affects mitochondrial morphology in patient-derived LS-hiPSCs. We assessed this using two patient hiPSC cell lines with mutations affecting subunits of complex I of the electron transport chain. The SBG4 hiPSC cell line has been derived from a patient fibroblast cell line with the 10158 T > C point mutation in the MTND3 gene, while SBG5 hiPSC has been derived from a patient fibroblast cell line with the 12706 T > C point mutation in the MTND5 gene. Studies have demonstrated the presence of these mutations in patients with LS or LS-like syndromes (Kori et al., 2019; Zhadanov et al., 2007; Mitchell, 1966). Our analysis demonstrated that both SBG4-(10158 T > C) and SBG5-(12706 T > C) diseased hiPSCs exhibited an increased skeletal area (denoting a higher mitochondrial occupying area), with SBG4-(10158 T > C) -hiPSC exhibiting a statistically significant increase (p < 0.05), when compared with control BJ-hiPSCs (Fig. 4b, 5b). However, based on our analysis, we determined that mitochondrial morphologies in SBG4-(10158 T > C)-hiPSCs contained 84.05% individuals and 15.95% networks; while SBG5-(12706 T > C)-hiPSCs contained 84.34% individuals and 15.66% networks respectively.

Fig. 4.

Fig. 4.

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Mitochondrial morphology of SBG4-(10158 T > C)-hiPSCs: a) Representative images of SBG4-(10158 T > C)-hiPSCs stained with MTR. From left to right are the phase contrast, Nucblue, RFP, overlay, and skeletonized images including the area of interest of SBG4-(10158 T > C)-hiPSCs. Different mitochondrial morphological parameters were determined using MiNA, normalized, and analyzed in comparison to the BJ-hiPSC (control cell line) to quantify: b) the mitochondrial footprint or the skeletal area occupied by the mitochondria c) number of individuals d) number of networks e) mean branch length f) network size or the mean branch number per network. All data are representative of 5–7 analyzed images obtained from 7 to 9 independent dishes from three independent experiments. The bars represent minimum and maximal values including all points, and each black dot represents different data points. ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05. Scale bar = 100 μm.

Fig. 5.

Fig. 5.

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Mitochondrial morphology of SBG5-(12706 T > C)-hiPSCs: a) Representative images of SBG5-(12706 T > C)-hiPSCs stained with MTR. From left to right are the phase contrast, Nucblue, RFP, overlay, and skeletonized images including the area of interest of SBG5-(12706 T > C)-hiPSCs. Different mitochondrial morphological parameters were determined using MiNA, normalized, and analyzed in comparison to the BJ-hiPSC (control cell line) to quantify: b) the mitochondrial footprint or the skeletal area occupied by the mitochondria c) number of individuals d) number of networks e) mean branch length f) network size or the mean branch number per network. All data are representative of 5–7 analyzed images obtained from 7 to 9 independent dishes from three independent experiments. The bars represent minimum and maximal values including all points, and each black dot represents different data points. ****p < 0.0001, ***p < 0.001, **p < 0.01, *p < 0.05. Scale bar = 100 μm.

In SBG4-(10158 T >C)-hiPSCs, we observed a statistically significant increase in the mean branch length (22%, p < 0.0001) (Fig. 4e) and network size (44%, p <0.001) (Fig. 4f), when compared with control BJ-hiPSCs. Although not statistically significant, we observed a 9.2% decrease in number of individuals (Fig. 4c) and 6.3% decrease in the number of networks (Fig. 4d), when compared with control BJ-hiPSCs. In SBG5-(12706 T > C)-hiPSCs, we observed a statistically significant increase in the mean branch length (9.6%, p < 0.01) (Fig. 5e) and network size (16%, p < 0.01) (Fig. 5f), when compared with control BJ-hiPSCs. We also observed a statistically significant decrease in number of individuals (15.7%, p < 0.01) (Fig. 5c) and in the number of networks (14.9%, p < 0.01) (Fig. 5d), when compared with control BJ-hiPSCs. Together these results indicate that the observed differences could be attributed to the location of the subunits affected by each mutation. The mutation in SBG5-(12706 T > C)-hiPSCs may affect the function of the mitochondria more than that of SBG4-(10158 T > C)-hiPSCs, requiring remodeling of the mitochondria to maintain optimal function of the respiratory chain. Overall, the increase in branch length and network size points to the presence of a fused mitochondria in both SBG4-(10158 T > C)- and SBG5-(12706 T > C)-hiPSCs.

3.4. Mitochondrial membrane potential was decreased in all diseased LS-hiPSCs

To investigate how the changes in mitochondrial dynamics affects mitochondrial function, we recorded mitochondrial membrane potential (MMP) in the diseased (SBG1, SBG2, SBG3, SBG4, SBG5) and healthy control BJ-hiPSC cell lines. We hypothesized that MMP would be perturbed in the diseased cell lines by different mechanisms. MMP generated by proton pumps at complex I, III and IV is an essential component in the process of energy storage (Zorova et al., 2018; Scaduto and Grotyohann, 1999). Fluorescence-based methodologies have been used to measure MMP with tetramethylrhodamine, ethyl ester (TMRE) in live cells with no quenching effect (Cottet-Rousselle et al., 2011). TMRE is a positively charged dye that is attracted to the negative potential across the inner mitochondrial membrane and thus accumulates in functionally active mitochondria in live cells (Hoppel et al., 2009). As active mitochondria maintain a net negative charge in the matrix, TMRE is sequestered in the matrix of these mitochondria. Depolarized or inactive mitochondria are not able to sequester TMRE as the MMP is compromised in these mitochondria. In our study, MMP was measured as mean fluorescent intensity (MFI) in all cell lines (Fig. 6a). Results indicate a significant decrease in MMP by 53.69% (p < 0.00001) in SBG1-(8993 T > G)-hiPSC, 26.9% (p < 0.05) in SBG2-(8993 T > G)-hiPSC, 19.48% in SBG3-(9185 T > C)-hiPSC, 25.54% (p < 0.05) in SBG4-(10158 T > C)-hiPSC, and 15.12% in SBG5-(12706 T > C)-hiPSC compared to healthy control BJ-hiPSCs (Fig. 6b).

Fig. 6.

Fig. 6.

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Mitochondrial membrane potential (MMP) analysis of BJ-hiPSC (control) and five LS-hiPSC lines. MMP was evaluated by analyzing the intensity of membrane-potential sensitive dye (TMRE). (a) Representative images of BJ and five LS-hiPSCs stained with TMRE. (b) Mean fluorescence intensity (MFI) was calculated based on three images analyzed from three independent dishes from two independent experiments and are shown for (Control BJ-hiPSC in grey; SBG1-(8993 T > G)-hiPSC, SBG2-(8993 T > G)-hiPSC, SBG3-(9185 T > C)-hiPSC in pink; SBG4-(10158 T > C)-hiPSC, and SBG5-(12706 T > C)-hiPSC in blue). *p < 0.05; ****p < 0.0001.

4. Discussion

Many recent studies have highlighted the significance of monitoring mitochondrial dynamics in various diseases (Gao et al., 2017; Chan, 2006; Harwig et al., 2018; Willems et al., 2015). In addition, studies have demonstrated the mitochondria’s ability to display different morphologies that could be correlated with the cell type and the metabolic state (Choi et al., 2015). Under normal healthy conditions, a balance between fission and fusion events helps maintain normal mitochondrial function (Chan, 2006; Youle and van der Bliek, 2012; Choi et al., 2015) (Fig. 7). During reprograming of fibroblasts and generation of hiPSCs, dynamic changes in mitochondrial morphology were observed that aligned with changes in energy metabolism (Prigione et al., 2010; Suhr et al., 2010; Folmes et al., 2011; Seo et al., 2018; Koopman et al., 2005). Previous studies have also evaluated mitochondrial dynamics in different diseases (including mitochondrial diseases) (Valente et al., 2017; Harwig et al., 2018; Choi et al., 2015; Tokuyama et al., 2020; Mortiboys et al., 2008), and have provided important insights into its importance in the context of understanding mitochondrial morphology changes in the diseased state. However, this is the first study that provides a comprehensive view of mitochondrial morphology and dynamics in hiPSC models of mitochondrial disorders, based on reprogramming from patient fibroblasts with pathogenic mtDNA mutations specific to LS. To ensure that the MiNA tool that quantifies mitochondrial morphology was effective in our study, we first evaluated its applicability in healthy control BJ-hiPSCs. Our results demonstrated that MiNA was sensitive in detecting ‘individuals’ and ‘networks’; and more importantly was able to delineate subtle structural differences (branched, elongated) in networks.

Fig. 7.

Fig. 7.

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Mitochondrial morphology summary for fibroblast cell lines. (A) Representative morphologies that exist in control BJ-hiPSCs and different LS-hiPSCs based on our observations (B) Schematic of aarious mitochondrial morphologies (Individuals, networks, branched networks, elongated networks, elongated branched networks) that exist in each cell. Results suggest the presence of subtle differences in mitochondrial morphologies that is mtDNA variant specific.

After confirming that MiNA is sensitive and can be used to characterize subtle differences in mitochondrial morphologies, we used it to quantify the morphology of the five LS-specific diseased hiPSCs. For the first time, we have conducted a comprehensive quantification of mitochondrial dynamics in hiPSCs with pathogenic mtDNA mutations. Using the MiNA toolkit with ImageJ, we were able to quantify the different morphologies in five (SBG1-(8993 T > G), SBG2-(8993 T > G), SBG3-(9185 T > C), SBG4-(10158 T > C), and SBG5-(12706 T > C)-hiPSCs. Across all five hiPSCs with different mtDNA variants, we observed and summarized the subtle differences in the mitochondrial morphologies when compared with the healthy control BJ-hiPSCs (Fig. 7). In computing the total observed mitochondria (defined as the sum of individuals and networks), we observed an increase in both SBG1-(8993 T > G); and SBG2-(8993 T > G)-hiPSCs, while we observed a decrease in total mitochondria in SBG3-(9185 T > C), SBG4-(10158 T > C), and SBG5-(12706 T > C)-hiPSCs, when compared with control BJ-hiPSCs.

In SBG1-(8993 T > G)-hiPSCs (Fig. 1), we observed a slight increase in the number of individuals, significant increase in the number of networks, significant increase in the mean branch length, and significant increase in the network size, when compared with control BJ-hiPSCs. Overall, these observations relate to the presence of more mitochondria (defined as the sum of individuals and networks), with mostly fragmented or fissioned mitochondria and some fused networks in SBG1-(8993 T > G)-hiPSCs. In SBG2-(8993 T > G)-hiPSCs (Fig. 2), we observed a significant increase in the number of individuals, significant increase in the number of networks, slight increase in the mean branch length and slight increase in the network size, when compared with control BJ-hiPSCs. Overall, these observations related to presence of more mitochondria, with highly fissioned or fragmented mitochondria due to presence of more individuals and more networks in SBG2-(8993 T > G)-hiPSCs. In SBG3-(9185 T > C)-hiPSCs (Fig. 3), we observed a significant decrease in the number of individuals, slight increase in the number of networks, significant increase in the mean branch length and significant increase in the network size, when compared with control BJ-hiPSCs. Overall, these observations related to presence of fewer mitochondria, with hyper-fused mitochondria due to decrease in the percentage of individuals, a slight increase in networks. However, presence of long branches and very big network size indicates hyperfusion, and the potential for many of the mitochondria to exhibit an elongated branched network.

In SBG4-(10158 T > C)-hiPSCs (Fig. 4), we observed a slight decrease in the number of individuals, slight decrease in the number of networks, significant increase in the mean branch length and significant increase in the network size, when compared with control BJ-hiPSCs. Overall, these observations relate to presence of fewer mitochondria, with presence of fused to hyper-fused mitochondria due to increase in the length of branches and network size. In SBG5-(12706 T > C)-hiPSCs (Fig. 5), we observed a significant decrease in the number of individuals, significant decrease in the number of networks, accompanied by significant increase in the mean branch length and significant increase in the network size, when compared with control BJ-hiPSCs. Overall, these observations relate to presence of fewer mitochondria, with presence of fused mitochondria due to decrease in the number of individuals and networks, while accompanied by a moderate increase in mean branch length and network size.

In a recent study, we performed a comprehensive analysis of mitochondrial dynamics in multiple patient-derived fibroblasts associated with several mitochondrial disorders (including LS) using the MiNA tool (Bakare et al., 2021). We demonstrated that the most predominant morphological signature for mitochondria in the diseased state is fragmentation, with eight out of the ten fibroblast cell lines exhibiting characteristics consistent with fragmented mitochondria. Comparing mitochondrial morphology results obtained with LS-fibroblasts with reprogrammed LS-hiPSCs, we noted an overall decrease in the percentage of individuals in the LS-hiPSCs when compared with the parental LS-fibroblasts. Mitochondrial membrane potential (MMP) was decreased in all diseased LS-hiPSCs when compared with control BJ-hiPSCs (Fig. 6). This observation is in line with studies that have demonstrated changes in MMP occurring in parallel with mitochondrial morphological alterations (Iannetti et al., 2015; Esteras et al., 2020; Schieke et al., 2008). Our observations are also in line with studies that have shown that normal hPSCs maintain high MMP and exhibit normal pluripotency, and differentiation potential; while PSCs with low MMP exhibit reduced differentiation potential (Armstrong et al., 2010; Prigione et al., 2011; Mah et al., 2011; Tsogtbaatar et al., 2020; Chen and Chan, 2017). Interestingly, in our previous study, we have shown that differentiated cultures from LS-hiPSCs preferentially exhibit upregulation in mesodermal-related genes, which could point to potential for aberrant differentiation during early development (Grace et al., 2019).

Our study for the first time has characterized mitochondrial morphology across five patient-derived hiPSCs exhibiting LS or LS-like syndromes. Our results describe the existence of subtle differences in mitochondrial morphologies in different LS-hiPSCs with different mtDNA mutation variants. A common view of mitochondrial disorders harboring mtDNA mutations is that there is a direct correlation between disease severity and clinical outcomes to the degree of heteroplasmy. Our preliminary results based on comprehensive next generation sequencing analysis indicate varying heteroplasmy levels (between 0.4 and 96%) in the different LS-hiPSCs. Given the low levels of mutation burden in two of the hiPSCs (SBG4-(10158 T > C)-hiPSCs-0.5%; SBG5-(12706 T > C)- hiPSCs-0.8%), we hypothesized that both the hiPSCs would contain sparse fragmented and immature mitochondria, as previously observed in hiPSCs (Mitra et al., 2009). However, the MiNA toolkit was sensitive enough to detect sparse mitochondria as well as mitochondria with elongated branches, indicating a tendency towards hyperfused state. In fact, SBG3-(9185 T > C)-hiPSCs exhibited a high level of mutation burden (96%) and was shown to have hyperfused mitochondria with elongated branched networks. The presence of the hyperfused state is an indication of abnormal mitochondrial phenotypes [90], which could indicate the diseased hiPSCs exhibit lower ATP production that may affect their proliferation and self-renewal potential (Mitra et al., 2009). While analysis using MiNA was able to provide us with necessary information to conduct comprehensive morphological analysis, there are a few limitations. For example, MiNA groups round, punctate and rods together as individuals. It would definitely help to distinguish between fragmentations that result in the formation of more round, punctate mitochondria as opposed to rods, as this would help with better delineation of the mitochondrial dynamics in diseased cell lines that contain different mtDNA variants. Nonetheless, this quantitative analysis allowed us to identify differences in mitochondrial dynamics in hiPSCs derived from patient fibroblasts that exhibit specific mtDNA mutations. In the long-term, we hope that a thorough evaluation of mitochondrial dynamics along with correlations with bioenergetic and genetic analyses in hiPSCs will aid in identifying the ‘signature’ of complex mitochondrial disorders, during early development.

5. Conclusions

In summary, we report for the first time the presence of disparate mitochondrial morphologies in LS-hiPSCs that contain different mtDNA variants. Our results indicate that specific point mutations affecting the OxPhos complexes could contribute to subtle alterations in mitochondrial morphologies that is mtDNA variant-specific. Although certain mutations and cellular demand cause hyper-fused mitochondria, our observations indicate that a detailed analysis of mitochondrial morphologies (branches and elongations) is necessary to better understand the effects of the specific mtDNA variants. Overall, this study improves our understanding of mitochondrial morphologies in patient- and disease-specific hiPSCs and could lead to a better understanding of the correlation between mitochondrial dynamics, genetics, and bioenergetics; and contribute to better diagnosis and focused therapies for treatment of devastating mitochondrial disorders like LS.

Supplementary Material

Supplementary Fig. 1.

Acknowledgements and Funding

This study and article were supported, in part, by NIHR21HD094394-02 (S.I.), NIHP20GM139768-01 (S.I), DoD W81XWH-16-1-0181 (R.R.R., and S.I.), and Arkansas Biosciences Institute (S.I). We also thank Dr. Johannes A. Mayr and Dr. Wolfgang Sperl from the Medical University of Salzburg Austria, and Dr. Daniela Karall from the Medical University of Innsbruck, Austria, for kindly providing us fibroblasts used in the study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the article. This article is being published in the spirit of “Gentle Science” and the authors wish to thank all hands and minds involved in this study.

Abbreviations:

hiPSC

Human induced pluripotent stem cell

hESC

Human embryonic stem cell

LS

Leigh syndrome

OXPHOS

Oxidative phosphorylation

mtDNA

Mitochondrial DNA

ETC

Electron Transport Chain

Footnotes

6.

Ethics approval and consent to participate

The current study was conducted with the approval of the University Of Arkansas Office Of Research Compliance which determined that the project was exempt from Institutional Review Board (IRB) oversight and human research subjects protection regulations.

Declarations

CRediT authorship contribution statement

Fibi Meshrkey: Methodology, Data curation, Formal analysis, Visualization, Writing – review & editing. Ana Cabrera Ayuso: Methodology, Data curation, Formal analysis. Raj R. Rao: Formal analysis, Visualization, Writing – review & editing. Shilpa Iyer: Conceptualization, Data curation, Formal analysis, Visualization, Supervision, Project administration, Writing – review & editing.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scr.2021.102572.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.

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Associated Data

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Supplementary Materials

Supplementary Fig. 1.
NIHMS1896210-supplement-Supplementary_Fig__1_.jpg (255.6KB, jpg)

Data Availability Statement

Data will be made available on request.

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