Skeletal dysplasia-causing TRPV4 mutations suppress the hypertrophic differentiation of human iPSC-derived chondrocytes

Abstract

Mutations in the TRPV4 ion channel can lead to a range of skeletal dysplasias. However, the mechanisms by which TRPV4 mutations lead to distinct disease severity remain unknown. Here, we use CRISPR-Cas9-edited human induced pluripotent stem cells (hiPSCs) harboring either the mild V620I or lethal T89I mutations to elucidate the differential effects on channel function and chondrogenic differentiation. We found that hiPSC-derived chondrocytes with the V620I mutation exhibited increased basal currents through TRPV4. However, both mutations showed more rapid calcium signaling with a reduced overall magnitude in response to TRPV4 agonist GSK1016790A compared to wildtype. There were no differences in overall cartilaginous matrix production, but the V620I mutation resulted in reduced mechanical properties of cartilage matrix later in chondrogenesis. mRNA sequencing revealed that both mutations upregulated several anterior HOX genes and downregulated antioxidant genes CAT and GSTA1 throughout chondrogenesis. BMP4 treatment upregulated several essential hypertrophic genes in WT chondrocytes; however, this hypertrophic maturation response was inhibited in mutant chondrocytes. These results indicate that the TRPV4 mutations alter BMP signaling in chondrocytes and prevent proper chondrocyte hypertrophy, as a potential mechanism for dysfunctional skeletal development. Our findings provide potential therapeutic targets for developing treatments for TRPV4-mediated skeletal dysplasias.

Full text

Dicks etal. eLife 2023;12:e71154. DOI: https://doi.org/10.7554/eLife.71154 1 of 34
Skeletal dysplasia- causing TRPV4
mutations suppress the hypertrophic
differentiation of human iPSC-
derivedchondrocytes
Amanda R Dicks1,2,3, Grigory I Maksaev4, Zainab Harissa1,2,3, Alireza Savadipour2,3,5,
Ruhang Tang2,3, Nancy Steward2,3, Wolfgang Liedtke6,7, Colin G Nichols4,
Chia- Lung Wu8†, Farshid Guilak2,3*†
1Department of Biomedical Engineering, Washington University in St. Louis, St Louis,
United States; 2Department of Orthopedic Surgery, Washington University School
of Medicine, St. Louis, St Louis, United States; 3Shriners Hospitals for Children -
St. Louis, St. Louis, United States; 4Department of Cell Biology and Physiology,
Washington University School of Medicine, St. Louis, St Louis, United States;
5Department of Mechanical Engineering and Material Science, Washington University
in St. Louis, St. Louis, United States; 6Department of Neurology, Duke University
School of Medicine, Durham, United States; 7Department of Molecular Pathobiology
- NYU College of Dentistry, New York, United States; 8Department of Orthopaedics
and Rehabilitation, Center for Musculoskeletal Research, University of Rochester,
Rochester, United States
Abstract Mutations in the TRPV4 ion channel can lead to a range of skeletal dysplasias. However,
the mechanisms by which TRPV4 mutations lead to distinct disease severity remain unknown. Here,
we use CRISPR- Cas9- edited human- induced pluripotent stem cells (hiPSCs) harboring either the
mild V620I or lethal T89I mutations to elucidate the differential effects on channel function and
chondrogenic differentiation. We found that hiPSC- derived chondrocytes with the V620I mutation
exhibited increased basal currents through TRPV4. However, both mutations showed more rapid
calcium signaling with a reduced overall magnitude in response to TRPV4 agonist GSK1016790A
compared to wildtype (WT). There were no differences in overall cartilaginous matrix production, but
the V620I mutation resulted in reduced mechanical properties of cartilage matrix later in chondro-
genesis. mRNA sequencing revealed that both mutations up- regulated several anterior HOX genes
and down- regulated antioxidant genes CAT and GSTA1 throughout chondrogenesis. BMP4 treat-
ment up- regulated several essential hypertrophic genes in WT chondrocytes; however, this hyper-
trophic maturation response was inhibited in mutant chondrocytes. These results indicate that the
TRPV4 mutations alter BMP signaling in chondrocytes and prevent proper chondrocyte hypertrophy,
as a potential mechanism for dysfunctional skeletal development. Our findings provide potential
therapeutic targets for developing treatments for TRPV4- mediated skeletal dysplasias.
Editor's evaluation
Analysis of different types of TRPV4 mutant hiPS cells (mild V620I vs severe T89I mutations) showed
alterations in calcium channel function and chondrocyte differentiation. hiPSC- derived chondrocytes
with the V620I mutation exhibited increased basal currents through TRPV4, while both mutations
showed more rapid calcium signaling with a reduced overall magnitude in response to TRPV4
RESEARCH ARTICLE
*For correspondence:
guilak@wustl.edu
†co- senior author
Competing interest: See page
22
Funding: See page 22
Received: 10 June 2021
Preprinted: 15 June 2021
Accepted: 03 February 2023
Published: 22 February 2023
Reviewing Editor: Di Chen,
Chinese Academy of Sciences,
China
Copyright Dicks etal. This
article is distributed under the
terms of the Creative Commons
Attribution License, which
permits unrestricted use and
redistribution provided that the
original author and source are
credited.

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agonist GSK1016790A compared to wild- type cells. These findings provide potential therapeutic
targets for developing treatments for TRPV4- mediated skeletal dysplasias.
Introduction
Skeletal dysplasias comprise a heterogeneous group of over 450 bone and cartilage diseases with
an overall birth incidence of 1 in 5000 (Krakow and Rimoin, 2010; Nemec etal., 2012; Ngo etal.,
2018; Orioli etal., 1986; Superti- Furga and Unger, 2007). Mutations in transient receptor potential
vanilloid 4 (TRPV4), a non- selective cation channel, can lead to varying degrees of skeletal dysplasia,
including moderate autosomal- dominant brachyolmia and severe metatropic dysplasia (Andreucci
etal., 2011; Kang, 2012). For example, a V620I substitution (exon 12, G858A) in TRPV4 is respon-
sible for moderate brachyolmia, which exhibits short stature, scoliosis, and delayed development of
deformed bones (Kang etal., 2012; Rock et al., 2008; Kang, 2012). These features, albeit more
severe, are also present in metatropic dysplasia. Metatropic dysplasia can be caused by a TRPV4 T89I
substitution (exon 2, C366T) and leads to joint contractures, disproportionate measurements, and,
in severe cases, neonatal death due to small chest size and cardiopulmonary compromise (Camacho
etal., 2010; Kang etal., 2012; Kang, 2012). Both V620I and T89I TRPV4 mutations are considered
gain- of- function variants (Leddy etal., 2014b; Loukin etal., 2011). Given the essential role of TRPV4
during chondrogenesis (Muramatsu etal., 2007; Willard et al., 2021) and cartilage homeostasis
(O’Conor etal., 2014), it is hypothesized that TRPV4 mutations may affect endochondral ossification
during skeletal development.
Endochondral ossification is a process by which bone tissue is created from a cartilage template
(Breeland etal., 2021; Camacho et al., 2010; Krakow and Rimoin, 2010; Rimoin etal., 2007).
During this process, chondrocytes transition from maintaining the homeostasis of cartilage, regulated
by transcription factor SRY- box containing gene 9 (SOX9) (Breeland etal., 2021; Nishimura etal.,
2012b; Prein and Beier, 2019; Sophia Fox etal., 2009), to hypertrophy. Hypertrophy is driven by
runt- related transcription factor 2 (RUNX2) and bone morphogenic protein (BMP) signaling (Breeland
etal., 2021; Nishimura etal., 2012b; Prein and Beier, 2019) and leads to chondrocyte apoptosis or
differentiation into osteoblasts to form bone (Breeland etal., 2021; Nishimura etal., 2012b; Prein
and Beier, 2019). However, how TRPV4 and its signaling cascades regulate endochondral ossification
remains to be determined.
The activation of TRPV4 increases SOX9 expression (Muramatsu etal., 2007) and prevents chon-
drocyte hypertrophy and endochondral ossification (Amano etal., 2009; Hattori etal., 2010; Lui
etal., 2019; Nishimura etal., 2012a; Nishimura etal., 2012b). One study found that overexpressing
wildtype (WT) Trpv4 in mouse embryos increased intracellular calcium (Ca2+) concentration and delayed
bone mineralization (Weinstein etal., 2014), a potential link between intracellular Ca2+, such as with
gain- of- function TRPV4 mutations, and delayed endochondral ossification. Our previous study also
observed increased expression of follistatin (FST), a potent BMP inhibitor, and delayed hypertrophy
in porcine chondrocytes overexpressing human V620I- and T89I- TRPV4 (Leddy etal., 2014a; Leddy
etal., 2014b). While previous studies have greatly increased our knowledge of the influence of TRPV4
mutations on chondrogenesis and hypertrophy, most of them often involved animal models (Leddy
etal., 2014b; Weinstein etal., 2014) or cells (Camacho etal., 2010; Krakow and Rimoin, 2010;
Leddy etal., 2014b; Loukin etal., 2011; Rock etal., 2008) overexpressing mutant TRPV4. There-
fore, these approaches may not completely recapitulate the effect of TRPV4 mutations on human
chondrogenesis.
Human- induced pluripotent stem cells (hiPSCs), derived from adult somatic cells (Takahashi etal.,
2007), offer a system for modeling human disease to study the effect of mutations throughout differ-
entiation (Adkar etal., 2017; Lee etal., 2021). In fact, two studies have used patient- derived hiPSCs
with TRPV4 mutations to study lethal and non- lethal metatropic dysplasia- causing variants I604M
(Saitta etal., 2014) and L619F (Nonaka etal., 2019), respectively. However, patient samples are
often challenging to procure due to the rarity of skeletal dysplasias. In this regard, CRISPR- Cas9 tech-
nology allows the creation of hiPSC lines harboring various mutations along with isogenic controls
(i.e., WT).
The goal of this study was to elucidate the molecular mechanisms underlying how two TRPV4
gain- of- function mutations lead to strikingly distinct severities of skeletal dysplasias (i.e., moderate

Research article Stem Cells and Regenerative Medicine
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brachyolmia vs. lethal metatropic dysplasia). To achieve this goal, we generated CRISPR- Cas9 gene-
edited hiPSC lines bearing either the V620I or T89I TRPV4 mutation, and their isogenic WT control, to
delineate the effects of TRPV4 mutations on chondrogenesis and hypertrophy using RNA sequencing
and transcriptomic analysis. We further examined the effects of the mutations on channel function
and matrix production and properties. We hypothesized the V620I and T89I TRPV4 mutations would
enhance chondrogenesis with distinct degrees of altered hypertrophy. This study will improve our
understanding of the role of TRPV4 in chondrocyte homeostasis and maturation and lay the founda-
tion for treatment and prevention of TRPV4- mediated dysplasias.
Results
Mutant TRPV4 has altered response to chemical agonist GSK101
We first assessed TRPV4 channel function and alterations in Ca2+ signaling due to the V620I and T89I
mutations in day- 28 hiPSC- derived chondrocytes using electrophysiology and fluorescence imaging.
Using whole- cell patch clamping, we measured the basal membrane current of the hiPSC- derived
chondrocytes from the mutated and WT lines. V620I- TRPV4 had the highest basal currents at both
70 and −70mV (70/−70mV pA/pF – WT: 18.52/5.93 vs. V602I: 77.79/55.33 vs. T89I: 40.97/50.13;
Figure1A). However, when TRPV4 was inhibited with GSK205 (Kanju etal., 2016), a TRPV4- specfic
chemical antagonist, the three lines had similar, decreased currents (70/−70mV – WT: 18.72/14.36pA/
pF vs. V620I: 13.55/9.15pA/pF vs. T89I: 29.27/13.8pA/pF; Figure1A). To capture the specific current
through TRPV4, we took the difference of the basal current (no GSK205) and the average TRPV4-
inhibited current (with GSK205). TRPV4 inhibition caused a significant change in current in V620I at
both 70 and −70mV (70mV – V620I: Δ64.28 vs. WT: Δ –0.19, p = 0.0379 and T89I: Δ11.67, p < 0.0001;
−70mV – V620I: Δ46.13 vs. WT: Δ −8.47, p < 0.0001 and T89I: Δ36.33, p = 0.0057; Figure1B). Inter-
estingly, T89I- TRPV4 was not significantly different from WT despite also causing a gain- of- function
in recombinant channels (Loukin etal., 2011). Further, the increase in signaling in V620I only may
indicate different mechanisms of action leading to the varying disease caused by the two mutations.
Next, we activated WT and mutant TRPV4 with chemical agonist GSK1016790A (GSK101) (Jin
etal., 2011) and found that the mutations decreased the cellular response to the agonist, resulting in
reduced Ca2+ signaling. These results were supported using two methods: inside- out excised patches
and confocal imaging of Ca2+ signaling (Figure1C, D). The representative traces of inside- out patches
showed increased current through the patch with the addition of GSK101 and the attenuation by
GSK205 (Figure 1C). GSK205 continued to block the channel and prevented another increase in
current despite the addition of GSK101. Though the unitary currents were indistinguishable (8pA at
−30mV) among WT and mutants, in excised inside- out patches, WT typically produced higher GSK101-
induced currents than the mutants (WT: 290pA vs. V620I: 87.1pA and T89I: 62.3pA at −30mV),
potentially indicative of more channels per patch (Figure1C). In the confocal imaging experiments,
a ratiometric fluorescence indicated Ca2+ signaling of the hiPSC- derived chondrocytes in response to
either 10nM GSK101 or a cocktail of 10nM GSK101 and 20µM GSK205. WT cells had significantly
higher fluorescence, and therefore Ca2+ signaling, in response to GSK101 according to the plots and
their area under the curve (WT: 1470 vs. V620I: 1114 and T89I: 1044; p < 0.0001; Figure1D, E). The
presence of GSK205 attenuated this response for all three lines, confirming the Ca2+ influx was due
to the TRPV4 ion channel (WT: 366 vs. V620I: 460 vs. T89I: 358). We also evaluated the response time
of the cells to GSK101 and GSK101 + GSK205. We considered a cell to be responding if more than
a quarter of its frames, after stimuli, had a fluorescence higher than the mean baseline plus 3 times
the standard deviation. The mutants responded faster to GSK101 than the WT (WT: 46.2s vs. V620I:
12s, p = 0.0048 and T89I: 10.8s, p = 0.0097; Figure1F). Interestingly, the addition of GSK205 did
not significantly slow the response of WT, but it did slow the response of the mutants, with the severe
mutation slower than the moderate (WT: 35.4s vs. V620I: 234s and T89: 366s; p < 0.0001; Figure1F).
These data highlight that the mutations alter the activation kinetics of TRPV4, which could play a role
in the disease phenotype.
Chondrogenic differentiation of WT and mutant hiPSC lines
To investigate if the hiPSCs with dysplasia- causing mutations exhibit altered chondrogenesis, we
differentiated CRISPR- Cas9- edited hiPSCs with mutant TRPV4 alongside an isogenic WT using our

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Figure 1. Differences in TRPV4 electrophysiological properties of wildtype (WT) and mutant human- induced
pluripotent stem cell (hiPSC)- derived chondrocytes. (A) Whole- cell currents were higher, on average, in mutant
hiPSC- derived chondrocytes than WT at 70 and −70mV. TRPV4 inhibition with 20µM GSK205 reduced mutant
currents to similar levels as WT. Mean ± standard error of the mean (SEM). n = 20–40 cells from 4 differentiations.
Figure 1 continued on next page

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previously published protocol (Adkar etal., 2019; Wu etal., 2021). After 12 days of monolayer meso-
dermal differentiation, the cells underwent 42 days of chondrogenic differentiation, and pellets were
collected at days 7, 14, 28, and 42. Since we had previously shown that 28 days is sufficient for hiPSC
chondrogenesis, day 28 was our primary time point while days 7 and 14 identified changes during
differentiation. We included day 42 data in the supplement to investigate any potential changes in
transcriptomic profiles and cartilaginous matrix production in chondrocyte maturation. At day 28, the
three lines had similar chondrogenic matrix as shown with Safranin- O staining for sulfated glycosami-
noglycans (sGAGs) and collagen type 2 alpha chain 1 (COL2A1) labeling with immunohistochemistry
(IHC; Figure2A, B). All three lines had little to no labeling of fibrocartilage marker COL1A1 and
hypertrophic cartilage marker COL10A1 with IHC (Figure2C, D). To quantitatively confirm the matrix
production throughout chondrogenesis, we performed biochemical assays to measure sGAG produc-
tion and normalized it to double- stranded DNA content. As expected, differences in matrix produc-
tion were significant between time points (p < 0.0001; Figure2E). The sGAG/DNA ratio increased in
WT by 8- fold and in V620I and T89I by 5- to 5.5- fold from day 14 to 28 (p < 0.0001; Figure2E). V620I
pellets also increased in matrix content by 150% from day 28 to 42 (p = 0.0163; Figure2—figure
supplement 2–1A) with all three lines reaching an sGAG/DNA ratio of approximately 30. However,
there were no differences in sGAG/DNA ratios among the three cell lines at any time point (cell line:
p = 0.1206; interaction: p = 0.7426; Figure2E).
Atomic force microscopy (AFM) was then used to measure the mechanical properties of the hiPSC-
derived cartilaginous matrix deposited by the WT and two TRPV4- mutated cell lines. The elastic
modulus ranged from 14 to 20kPa, consistent with mouse iPSC- derived cartilage (Diekman etal.,
2012). At day 28, the three lines had similar properties (WT: 14.4kPa vs. V620I: 15.9 kPa vs. T89I:
14.8kPa; Figure2F); however, at day 42, V620I had a significantly decreased elastic modulus (V620I:
10.32kPa vs. WT: 20.0kPa, p = 0.0004 and T89I: 17.5kPa, p = 0.0328; Figure2—figure supplement
2–1B). These experiments indicated that all three lines properly differentiated into chondrocytes and
had similar cartilaginous matrix production at day 28. With 14 more days of chondrogenic culture,
minor differences in matrix accumulation were observed with the moderate V620I line.
TRPV4 mutations altered chondrogenic gene expression in hiPSC-
derived chondrocytes
Reverse transcription quantitative polymerase chain reaction (RT- qPCR) analysis throughout differ-
entiation shows that mutants had higher ACAN expression compared to WT at day 28 (day- 28 fold
changes; WT: 2314 vs. V620I: 6418, p = 0.1092 and T89I: 5870, p = 0.0316; Figure3A); however,
expression decreased at day 42 in T89I (Figure3—figure supplement 1A). COL2A1 expression was
Kruskal- Wallis test with multiple comparisons comparing cell lines at 70 and -70 mV. No signicance. (B) The
difference between the current (I) through TRPV4 without GSK205 from the average current through inhibited
channels was signicantly higher in V620I. There was no difference between no drugs and GSK205 in WT. Mean
± SEM. n = 27–40 from 4 differentiations. Kruskal–Wallis test with multiple comparisons comparing cell lines at
70 and −70mV. *p < 0.05, **p < 0.01, ****p < 0.001. (C) Inside- out excised patches of WT had a higher current
in response to 10nM GSK101 (indicated by *) than mutants. The addition of 10nM GSK101 + 20µM GSK205
(indicated by arrow head) decreased the current and continued to block the channel when GSK101 alone was
re- introduced (*). Representative plots with average unitary current and current in response to GSK101. Mean ±
SEM. N = 5, 9, and 8 for WT, V620I, and T89I, respectively, from 2 differentiations. (D) Mutant TRPV4 decreased the
channels’ sensitivity to activation with GSK101 (indicated by arrow) as shown with confocal imaging of ratiometric
uorescence indicating Ca2+ signaling. GSK205 attenuated GSK101- mediated signaling. Mean ± 95% CI. n = 3
experiments with a total of 158–819 cells per line. (E) Quantication of the area under the curve of (D). Mean ±
SEM. n = 158–819 cells from 3 experiments. Ordinary two- way analysis of variance (ANOVA) with Tukey’s post hoc
test. Interaction, cell line, and treatment p < 0.0001. Different letters are signicantly different, p < 0.05, from each
other. (F) Time of initial response of each responding cell (≥25% of frames for that cell are responding) measured
from the addition of stimulus. Mutant TRPV4 responded faster to GSK101, but the response was signicantly
slowed by GSK205. Responding frames were considered to have a uorescence greater than the mean plus three
times the standard deviation. Mean ± SEM. n = 21–360 responding cells from 3 experiments. Ordinary two- way
ANOVA with Tukey’s post hoc test. Interaction, cell line, and treatment p < 0.0001.Different letters are signicantly
different, p < 0.05, from each other.
Figure 1 continued

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similar among the three lines at day 28 (day- 28 fold changes; WT: 6492 vs. V620I: 6524, p > 0.9999
and T89I: 8131, p = 0.3304; Figure3B) but significantly lower in T89I at day 42 (day- 42 fold changes;
T89I: 2798 vs. WT: 9209, p = 0.0144 and V620I: 7177, p = 0.0007; Figure3—figure supplement 1B).
Throughout chondrogenesis, V620I significantly increased expression of chondrogenic transcription
factor SOX9 (day- 28 fold changes; V620I: 178.9 vs. WT: 49.16, p = 0.0011 and T89I: 55.37, p = 0.0117;
Figure3C) and TRPV4 (day- 28 fold changes; V620I: 112.1 vs. WT: 42.14, p < 0.0001 and T89I: 45.82, p
= 0.0002; Figure3D). On the other hand, T89I significantly increased expression of pro- inflammatory,
calcium- binding protein S100B (Yammani, 2012) throughout chondrogenesis (day- 28 fold changes;
T89I: 2552 vs. WT: 415.8, p < 0.0001 and V620I: 633.6, p = 0.0019; Figure3E). T89I also had signifi-
cantly higher expression of fibrocartilage marker COL1A1 at days 7, 14, and 28 than the other two
lines (day- 28 fold changes; T89I: 80.33 vs. WT: 13.15, p < 0.0001 and V620I: 23.61, p = 0.0043;
Figure3F), and both mutations had increased expression at day 42 compared to WT (Figure3—
figure supplement 1F). In contrast, hypertrophic marker COL10A1 was significantly higher in the WT
line than the mutants at days 28 and 42 (day- 28 fold changes; WT: 310.4 vs. V620I: 30.26, p = 0.0338
and T89I: 52.92, p = 0.0033; Figure3G; Figure3—figure supplement 1G). Surprisingly, there was no
Figure 2. Mutant TRPV4 had little effect on chondrogenic matrix production. (A) Wildtype (WT), V620I, and T89I day- 28 pellets exhibit similar matrix
production shown by staining for sulfated glycosaminoglycans (sGAGs) with Safranin- O and hematoxylin and labeling with immunohistochemistry
(IHC) for (B) COL2A1 (C), COL1A1 (D), and COL10A1. Scale bar = 500µm. Representative images from 3 to 4 differentiations. (E) The sGAG/DNA ratio
increased in all three lines from day 14 to 28 of chondrogenesis. There were no differences between lines at each time point. Mean ± standard error of
the mean (SEM). n = 11–16 from 3 to 4 independent differentiation experiments. ****p < 0.0001 Statistical signicance determined by an ordinary two-
way analysis of variance (ANOVA) with Tukey’s post hoc test. (F) There were no differences in the elastic modulus of the matrix at day 28. Mean ± SEM. n
= 11–14 from 3 experiments. Statistical signicance determined by an ordinary two- way ANOVA with Tukey’s post hoc test.
The online version of this article includes the following gure supplement(s) for gure 2:
Figure supplement 1. Minor differences in V620I matrix were observed at day 42 of chondrogenesis.

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significant increase in follistatin (FST) expression in mutants at later time points (day- 28 fold changes;
WT: 0.7042 vs. V620I: 0.4025, p = 0.6228 and T89I: 0.4242, p > 0.9999; Figure3H) despite previous
findings (Leddy etal., 2014b).
To obtain comprehensive transcriptomic profiles of WT and TRPV4- mutated cell lines, we
performed bulk RNA sequencing of day- 28 chondrogenic pellets. We compared V620I and T89I gene
expression to WT and plotted the log2 fold change in heatmaps (Figure4A, B). While many chon-
drogenic and hypertrophic genes had similar levels of expression between the lines, the mutants had
increased expression of cartilage oligomeric matrix protein (COMP), collagen type VI alpha chains
1 and 3 (COL6A1, COL6A3), growth differentiation factor 5 (GDF5), high- temperature requirement
A serine peptidase 1 (HTRA1), and secreted protein acidic and cysteine rich (SPARC) (Figure4A).
Additionally, the mutations up- regulated expression levels of bone morphogenic protein 6 (BMP6),
transforming growth factor 3 (TGFB3), nuclear factor of activated T- Cells C2 (NFATC2), Twist family
Figure 3. V620I and T89I exhibited differing effects on gene expression during chondrogenic differentiation. (A) V620I and T89I had increased ACAN
gene expression at day 28 compared to wildtype (WT). (B) The three lines had similar COL2A1 expression throughout differentiation. V620I increased
expression of (C) SOX9 and (D) TRPV4 throughout chondrogenesis. T89I increased expression of (E) S100B and (F) COL1A1 throughout chondrogenesis.
(G) Both mutations decreased COL10A1 gene expression at day 28 compared to WT. (H) There were no differences in FST expression at day 28. Mean
± standard error of the mean (SEM). n = 10–12 from 3 independent differentiation experiments. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001
Signicance determined by one- way analysis of variance (ANOVA) with Tukey’s post hoc test for each time point.
The online version of this article includes the following gure supplement(s) for gure 3:
Figure supplement 1. V620I and T89I had differing effects on gene expression during chondrogenic differentiation.

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Figure 4. Dynamic changes in transcriptomic proles of V620I and T89I mutants during chondrogenesis. Heatmaps comparing the log2 fold change
of common chondrogenic and hypertrophic genes (A) and growth factor and signaling genes (B) in day- 28 V620I and T89I chondrocytes compared to
wildtype (WT). (C) Clustering of the samples using Euclidean distances reveals that V620I and T89I human- induced pluripotent stem cell (hiPSC)- derived
chondrocytes are more similar to each other than WT. (D) The number of up- and down- regulated differentially expressed genes (DEGs) in V620I and
T89I day- 28 chondrocytes compared to WT. (E–G) Analysis of the down- regulated genes compared to WT. (E) A Venn diagram reveals the number
of similar and different down- regulated DEGs between V620I and T89I, where most genes are shared. (F) A heatmap showing the log2 fold change,
Figure 4 continued on next page

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BHLH transcription factor 1 (TWIST1), ADAM metallopeptidase with thrombospondin type 1 motif 4
(ADAMTS4), and WNT3A (Figure4B). In contrast, the mutants had decreased expression of hyper-
trophic markers COL10A1, secreted phosphoprotein 1 (SPP1), and alkaline phosphatase, biomin-
eralization associated (ALPL) (Figure 4B). The mutations also down- regulated osteoblastogenesis
transcription factors SOX2 and SOX11 and previously identified genes governing off- target differentia-
tion during hiPSC chondrogenesis including nestin (NES), orthodenticle homeobox 2 (OTX2), WNT7A,
and WNT7B (Figure4B). Overall, these results indicate the mutant chondrocytes express higher levels
of chondrogenic markers and lower levels of genes associated with hypertrophy compared to WT.
V620I and T89I mutants demonstrate similar gene expression profiles
early in differentiation
First, to evaluate the similarities and differences in transcriptomic profiles between the hiPSC- derived
chondrocytes with and without the TRPV4 mutations, we computed the Euclidean distance between
day- 28 samples of each cell line. The WT samples clustered away from the mutants, and the V620I
samples were the most variable. (Figure4C). In terms of total differentially expressed genes (DEGs)
compared to WT, V620I had 8% fewer DEGs than T89I (2459 vs. 2671; Figure4D). Mutants had only
about half of the number of up- regulated genes compared to down- regulated genes (V620I: 884
vs. 1575, T89I: 978 vs. 1693; Figure 4D). The majority of the down- regulated DEGs were shared
between the two mutants when compared to WT, comprising 76% and 71% of V620I’s and T89I’s total
down- regulated DEGs, respectively (Figure4E). We plotted the top 25 most down- regulated DEGs
for each line in a heatmap. These included antioxidant catalase (CAT), anti- inflammatory nucleotide-
binding and leucine- rich repeat receptor family pyrin domain containing 2 (NLRP2), and Kruppel- like
factor 8 (KLF8) (Figure4F). Interestingly, many of the down- regulated DEGs, both unique and shared
between V620I and T89I, were associated with Gene Ontology (GO) terms related to nervous system
development, including many potassium channel genes (i.e., KCN family; Figure4G). This finding is
potentially indicative of changes in ion channel signaling beyond TPRV4 with the mutations.
In contrast, 686 up- regulated DEGs were shared by both mutants, while 22% of V620I’s and 30%
of T89I’s up- regulated DEGs were unique to each mutation (198 vs. 292; Figure4H). A heatmap of
the top 25 up- regulated DEGs showed that several homeobox (HOX) genes were highly expressed in
chondrocytes with the TRPV4 mutations (Figure4I). These included HOXA2 to HOXA7, HOXA- AS2,
HOXB2 to HOXB4, and HOXB- AS1, which are associated with morphogenesis and anterior patterning
(Seifert etal., 2015). Furthermore, the shared, up- regulated DEGs between two mutants are associ-
ated with extracellular matrix production and organization and growth factor binding in GO term anal-
ysis, while V620I genes were associated with type I interferon (Figure4J). These data highlighted an
early morphogenic genetic profile in hiPSC- derived chondrocytes with the V620I and T89I mutations.
Additionally, while mutated chondrocytes were more similar to each other compared to WT, we
identified a set of genes that may regulate the different disease phenotypes of moderate brachyolmia
and severe metatropic dysplasia caused by the V620I and T89I mutation, respectively. For example,
the top 15 up- and down- regulated genes unique to either V620I or T89I were plotted in a heatmap
(Figure 4—figure supplement 1A). Interferon- induced protein with tetratricopeptide repeats 3
(IFIT3), interferon- induced GTP- binding protein Mx1 (MX1), and p53 up- regulated regulator of p53
compared to WT, of the top 25 down- regulated genes for each line. (G) The top 3 Gene Ontology (GO) terms (biological process) associated with the
DEGs unique to V620I, shared between V620I and T89I, and unique to T89I. Symbol color represents the cell line, and size represents the −log10(padj).
(H–J) Analysis of the up- regulated genes compared to WT. (H) A Venn diagram reveals the number of similar and different up- regulated DEGs between
V620I and T89I, where most genes are shared. (I) A heatmap showing the log2 fold change, compared to WT, of the top 25 up- regulated genes for each
line. (J) The top 3 GO terms (biological process) associated with the DEGs unique to V620I, shared between V620I and T89I, and unique to T89I. Symbol
color represents the cell line, and size represents the −log10(padj). (K) Clustering of the day- 28 and -56 samples using Euclidean distances reveals that the
WT chondrocytes, at both days 28 and 56, cluster together while mutants cluster by time point. (L) The number of up- and down- regulated DEGs for
V620I and T89I compared to WT at days 28 and 56. (M) A Venn diagram reveals the number of similar and different up- regulated DEGs between V620I
and T89I, with T89I becoming more unique at day 56. n = 3–4 samples.
The online version of this article includes the following gure supplement(s) for gure 4:
Figure supplement 1. Distinction between V620I and T89I.
Figure supplement 2. Top differentially expressed genes (DEGs) of V620I and T89I chondrocytes compared to wildtype (WT) remain from day 28 to 56.
Figure 4 continued

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levels (PURPL) were all up- regulated in V620I, but not T89I, consistent with the associated pathways
regarding interferon signaling (Figure4—figure supplement 1B and Figure4J). Interestingly inter-
feronopathies with enhanced type 1 signaling may lead to intracranial calcification and skeletal devel-
opment problems (Yu and Song, 2020). We also observed that protein kinase C alpha (PKC; PRKCA),
which plays a role in the phosphorylation of TRPV4, was up- regulated in V620I compared to WT
(Figure4—figure supplement 1B). V620I also uniquely had many down- regulated genes related to
DNA- and RNA- binding such as zinc finger proteins (ZNF736, ZNF717, and ZNF594) and ribosomal
protein S4 y- linked 1 (RPS4Y1; Figure4—figure supplement 1A). T89I had much higher expression
of micro- RNA MIR1245A, compared to both WT and V620I, which has been shown to increase prolif-
eration in colon cancer (Pan etal., 2019; Figure4—figure supplement 1A). Developmental protein
dickkopf WNT signaling pathway inhibitor 2 (DKK2) and carbonic anhydrase 2 (CA2), which is essential
for bone resorption, were also uniquely up- regulated in T89I at day 28 (Figure4—figure supplement
1A). In contrast, T89I had reduced expression of bone matrix structural protein bone sialoprotein
II (IBSP) and limb development transcription factor SP9 (Figure4—figure supplement 1A). These
mutant- specific DEGs highlight that the severe T89I mutation began to have a unique skeletal devel-
opment transcriptome as early as day 28.
The severe T89I mutation inhibits chondrocyte hypertrophy more than
moderate V620I mutation
Following an additional 4 weeks of chondrogenic culture, we performed RNA sequencing to investi-
gate how the differences between the WT and the two mutants change with further differentiation.
Using Euclidean distances, we compared the WT, V620I, and T89I hiPSC- derived chondrocytes at both
days 28 and 56 (Figure4K). WT clustered together at both days 28 and 56; however, the mutants
clustered by time point. Again, there were more down- regulated genes than up- regulated at day
56 (Figure4L). The lethal, metatropic- dysplasia- causing T89I mutation had the most DEGs, and the
number increased from day 28 to 56. In contrast, the moderate, brachyolmia- causing V620I mutation
DEGs decreased at day 56. 74% of V620I up- regulated DEGs, but only 24% of T89I DEGs, were shared
between the two lines (424 total genes; Figure4M). These intersecting, up- regulated genes were
associated with the biological processes of skeletal development, morphogenesis, and patterning
due to the up- regulation of many HOX genes (Figure4—figure supplement 2A). Most of the top
up- and down- regulated genes were consistent between days 28 and 56 (Figure4—figure supple-
ment 2A–B), including both anterior and posterior HOX genes (i.e., HOXA1 to HOXA7, HOXB2 to
HOXB4, HOXB6 to HOXB8, HOXC4, HOXD8, HOXA- AS2- 3, and HOXB- AS1- 2) (Seifert etal., 2015).
Although V620I and T89I TRPV4 mutants continued to share the up- regulated HOX genes, which may
be responsible for dysfunctional chondrogenic hypertrophy compared to WT cells, our results also
indicate that these two mutated lines started to demonstrate further divergent transcriptomic profiles
in later chondrogenesis. We observed more up- and down- regulated DEGs in T89I vs. WT compared to
V620I vs. WT. Insulin growth factor- like family member 3 (IGFFL3) and matrix extracellular phosphogly-
coprotein (MEPE) were significantly up- regulated in T89I; however, they were slightly down- regulated
in V620I, compared to WT (Figure4—figure supplement 1C). T89I also up- regulated calcium- binding
proteins annexin A8 (ANXA8) and S100A3 (Figure4—figure supplement 1C). Consistent with its
regulation of many Wnt- related genes, T89I up- regulated beta catenin (CTNNB1) compared to WT at
day 56 (Figure4—figure supplement 1D). Both T89I and V620I uniquely down- regulated DNA- and
RNA- binding genes, such as various zinc finger proteins, with many of same up- and down- regulated
genes in V620I at day 56 as 28 (Figure4—figure supplement 1C). Moderate V620I’s difference from
WT remained, while T89I continued to diverge with further differentiation.
TRPV4 mutations exhibit dysregulated BMP4-induced chondrocyte
hypertrophy
To evaluate how TRPV4 mutations may affect hypertrophy, BMP4 was added to the chondrogenic
medium with and without TGFβ3 to stimulate hypertrophic differentiation starting at day 28 of chon-
drogenic pellet culture (Craft etal., 2015). At day 56, Safranin- O staining indicated the BMP4- treated
WT had developed a more hypertrophic phenotype compared to TGFβ3- and TGFβ3 + BMP4- treated
pellets with enlarged chondrocytes (cell diameter; WT- BMP4: 27.6 µm vs. WT- TGFβ3: 11.8 µm,
V620I- BMP4: 12.5µm, and T89I- BMP4: 11.3µm; p < 0.0001; Figure5A, B). This phenotype was not

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Figure 5. Wildtype (WT) chondrocytes are more sensitive to BMP4 treatment. (A) WT chondrocytes treated with BMP4 developed a hypertrophic
phenotype with enlarged lacunae, which was not present in the mutant cell lines or other conditions, as shown by Safranin- O and hematoxylin staining.
Scale bar = 500µm. Representative images from 2 experiments. (B) Cell diameter was signicantly increased in the WT with BMP4 treatment compared
to all other groups indicating a hypertrophic phenotype. Mean ± standard error of the mean (SEM). n = 249–304 cells from 2 pellets. Different letters
indicate statistical signicance (p < 0.05) between groups as determined by Kruskal–Wallis test with multiple comparisons since data was not normally
distributed. (C) Western blot shows that WT had a stronger increased production of ALPL, COL10A1, IHH, RUNX2, and RUNX2- 9 in response to BMP4
treatment than the mutants. (D) Principle component analysis (PCA) of bulk RNA- seq reveals an increased sensitivity to BMP4 (and TGFβ3 + BMP4)
treatment in WT human- induced pluripotent stem cell (hiPSC)- derived chondrocytes compared to V620I and T89I. n = 3–4 samples.
The online version of this article includes the following source data and gure supplement(s) for gure 5:
Figure supplement 1. Hypertrophic gene and protein expression.
Source data 1. ALPL western blot: the full raw unedited gel with and without the bands labeled.
Source data 2. COL10A1 western blot: the full raw unedited gel with and without the bands labeled.
Source data 3. IHH western blot: the full raw unedited gel with and without the bands labeled.
Source data 4. MMP13 western blot: the full raw unedited gel with and without the bands labeled.
Figure 5 continued on next page

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present in any of the groups from the V620I and T89I lines. Western blot and RNA sequencing further
confirmed BMP4- induced hypertrophy was more prominent in the WT line. There was an increase
in gene expression and protein production of hypertrophic cartilage markers COL10A1, ALPL, IHH,
RUNX2 isoform 9 (RUNX2- 9) in all three lines with BMP4 treatment; however, there was a stronger
effect in WT (Figure5C; Figure5—figure supplement 1). Additionally, only BMP4- treated WT had
an increase in RUNX2 (Figure5C; Figure5—figure supplement 1). These data also highlight the
stratification between the moderate V620I and severe T89I mutations as BMP4- treated T89I had lower
expression and production of COL10A1, ALPL, and IHH compared to BMP4- treated V620I (Figure5C;
Figure5—figure supplement 1). Interestingly, BMP4 treatment reduced MMP13 in the mutants but
did not affect WT (Figure 5C; Figure 5—figure supplement 1). A principle component analysis
(PCA) of the RNA sequencing data revealed that the WT line was overall more sensitive to BMP4, as
indicated by the arrows (Figure5D). Given that the BMP4- treated WT chondrocytes had the most
apparent hypertrophic phenotype, later analyses were performed comparing the BMP4- and TGFβ3-
treated chondrocytes for simplification.
Hierarchical k- means clustering of gene expression profiles of BMP4- and TGFβ3- treated chon-
drocytes resulted in 9 unique clusters, as determined using the gap statistics method (Figure6A).
Most of the clusters, including the largest (i.e., cluster 1), showed up- regulation of gene expression
with BMP4 treatment, while clusters 4, 5, and 9 showed down- regulation. The gene expression per
group for each cluster is listed in Supplementary file 1. Overall, WT responded to BMP4 treatment
with the largest number of DEGs, over 2500, with only 22% of them shared among all three lines
(Figure6B). Although cluster 1 shows an overall increase in gene expression with BMP4 treatment,
WT had a larger increase in expression than the mutants (Figure6C). In fact, some of the genes that
were up- regulated with BMP4 treatment in WT may have no change or down- regulation in mutants
(cluster 1, Figure6A).
As cluster 1 represents the primary response to BMP4 treatment and may highlight how the
TRPV4 mutations inhibit chondrocyte hypertrophy, we constructed a gene network of this cluster
(Figure6D). The log fold change of each gene per cell line is represented by a color scale, which is
consistent with WT having overall higher expression of the genes (as indicated by the white arrows
in the legend; Figure6D). With GO term analysis, the cluster 1 gene network is highly associated
with ossification, biomineral tissue development, skeletal system development, tissue development,
and osteoblast differentiation (Figure6D). Alkaline phosphatase, biomineralization associated (ALPL),
amelogenin X- linked (AMELX), fibroblast growth factor receptor 3 (FGFR3), interferon- induced trans-
membrane protein 5 (IFITM5), Indian hedgehog (IHH), parathyroid hormone 1 receptor (PTH1R), and
noggin (NOG) were connected to at least 4 of the top 5 GO terms. Of those, ALPL, AMELX, and
IFITM5 showed much higher expression in WT than the mutants alongside antioxidant glutathione
S- transferase alpha 1 (GSTA1) and bone ECM proteins integrin- binding sialoprotein (IBSP) and matrix
extracellular phosphoglycoprotein (MEPE). Lack of expression of these key genes, particularly ALPL,
may be responsible for the inhibited hypertrophy in TRPV4 V620I- and T89I- mutated chondrocytes.
We next investigated and plotted the top 25 up- regulated genes for each line with BMP4 treatment
(compared to their respective TGFβ3 control) (Figure6E). 88% of these genes were also present in
cluster 1. The key genes ALPL, AMELX, IFITM5, GSTAI, IBSP, and MEPE had distinctly higher expres-
sion in WT than mutants, in agreement with the network analysis. Both mutants showed higher expres-
sion than WT of ankyrin repeat and SOCS box containing 10 (ASB10), GTPase, IMAP family member
6 (GIMAP6), and adhesion G- protein- coupled receptor D1 (ADGRD1) when compared to their corre-
sponding TGFβ3 control group. GO term analysis was further performed on all BMP4 up- regulated
DEGs for each line (Figure6F). WT was highly associated with skeletal system development, ossifi-
cation, endochondral ossification, and extracellular structure organization. V620I was also associated
with these concepts to a lesser degree, while T89I showed little to no association. We believe these
results highlight that the TRPV4 mutations reduce BMP4- induced hypertrophy but to a greater extent
with the T89I mutation, which causes the more severe phenotype.
Source data 5. RUNX2 western blot: the full raw unedited gel with and without the bands labeled.
Source data 6. GAPDH western blot: the full raw unedited gel with and without the bands labeled.
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Figure 6. V620I and T89I had an inhibited hypertrophic response to BMP4 treatment. (A) There are 9 clusters of genes based on expression and
hierarchical k- means clustering of the samples. (B) Venn diagram shows similar and distinct differentially expressed genes (DEGs) in response to BMP4
treatment in all three lines. (C) Cluster 1 represented increasing in expression from TGFβ3 to BMP4 treatment (left to right on x- axis). Y- axis scale (−1.5
to 2) represents the scaled mean counts. (D) A protein–protein interaction network with functional enrichment analysis of cluster 1 reveals the top
Figure 6 continued on next page

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Discussion
To elucidate the detailed molecular mechanisms underlying the distinct severity of skeletal dyspla-
sias caused by two TRPV4 mutations (moderate brachyolmia- causing V620I vs. severe metatropic
dysplasia- causing T89I), we used CRISPR- Cas9 gene editing to generate hiPSC- derived chondrocytes
bearing either V620I or T89I mutation. We observed that day- 28 chondrocytes exhibited differences
in channel function and gene expression between the mutants and WT control. Differences in tran-
scriptomic profiles between V620I and T89I and from WT became more apparent with maturation
following 4 additional weeks of culture with TGFβ3 or hypertrophic differentiation with BMP4 treat-
ment. Of note, WT was significantly more sensitive to BMP4- induced hypertrophy. At the transcrip-
tomic and proteomic levels, TRPV4 mutations inhibited chondrocyte hypertrophy, particularly with the
T89I mutation, whereas V620I exhibited a milder phenotype, consistent with the clinical presentation
of these two conditions. Our results suggest that skeletal dysplasias may be, at least in part, resulting
from improper chondrocyte hypertrophy downstream of altered TRPV4 function. Furthermore, with
our genome- wide RNA sequencing analysis, we also identified several putative genes that may be
responsible for these dysregulated pathways in human chondrocytes bearing V620I or T89I TRPV4
mutations.
Our findings are generally consistent with previous non- human models of V620I and T89I muta-
tions. Two other models that have studied the V620I and T89I mutations include X. laevis oocytes
injected with rat TRPV4 cRNA (Loukin etal., 2011) or primary porcine chondrocytes transfected with
human mutant TRPV4 (Leddy et al., 2014b). Both reports and our current study investigated the
baseline currents of the mutant TRPV4 compared to WT. Here, we used patch clamping and observed
high basal currents in V620I with a significant decrease when TRPV4 was inhibited. However, this
characteristic was trending, but not significant, in T89I, despite both V620I and T89I being reported
as gain- of- function mutations (Camacho etal., 2010; Rock etal., 2008). Both the X. laevis oocyte
and porcine chondrocyte models confirmed high basal currents through V620I- TRPV4 (Leddy etal.,
2014b; Loukin etal., 2011). Interestingly, X. laevis oocytes, but not the humanized porcine chondro-
cytes, showed an increase in basal Ca2+ signaling through T89I (Leddy etal., 2014b; Loukin et al.,
2011). Furthermore, our results were consistent with a summary of TRPV4 channelopathies reporting
an increase in conductivity in V620I but no change in T89I (Kang, 2012). Interestingly, V620I also had
increased expression of PRKCA, the gene encoding for protein kinase C alpha. Phosphorylation by
PKC has been shown to alter TRPV4 activation (Cao etal., 2018) and therefore may play a role in
the altered signaling with these mutations. In future experiments, we will further investigate PKC and
PKA phosphorylation of TRPV4 and the effects on channel activity in these mutations. The conflicting
basal current results could be due to differences in phosphorylation or the species of the TRPV4, but
this was not the case regarding channel activation. As mentioned, the hiPSC- derived chondrocytes
with V620I and T89I TRPV4 had reduced currents and Ca2+ signaling in response to chemical agonist
GSK101. However, our previous study showed the porcine chondrocytes with mutant human TRPV4
had increased peak Ca2+ signaling in response to hypotonic changes (Leddy et al., 2014b). This
discrepancy could be due to the mode of activation of TRPV4 (i.e., osmotic vs. chemical agonist). In
contrast, the oocytes with mutant rat TRPV4 had lower currents in response to both hypotonic and
chemical (GSK101) TRPV4 activation compared to WT- TRPV4, consistent with our findings. It can be
speculated that there is decreased sensitivity to the antagonist because the mutated hiPSC- derived
chondrocytes are compensating for the increased basal activity by reducing the number of TRPV4
channels or other ion channels and signaling transducers as shown with the RNAseq data and asso-
ciated GO terms. The increased basal currents and decreased channel sensitivity to TRPV4 agonist
GSK101 with mutated TRPV4 are also likely resulting from an increased open probability of TRPV4
regulating genes and their associated concepts. Connections between protein- coding genes and Gene Ontology (GO) processes are based on the
average log fold change between cell lines. Coloring of the protein- coding gene circles is divided into three to represent the log fold change for each
cell line as shown in the legend. The white arrows in the legend indicates the location of the maximum log fold change for each respective cell line. The
gray boxes represent the top 5 GO terms (biological process) identied for the network with the log10(false discovery rate) underneath the term. (E) A
heatmap of the top 25 up- regulated genes, and their log2 fold change, in each line compared to their respective TGFβ3 controls. (F) The top GO terms
and biological pathways associated with the up- regulated DEGs with BMP4 treatment. Symbol color represents the cell line, and size represents the −
log10(padj).
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making the channels less likely to be activated by a chemical agonist (Loukin et al., 2011). The
obvious differences in both resting and activated states confirm functional differences with TRPV4
mutations that may ultimately lead to changes in downstream signaling of the channel, which alter
joint development and result in skeletal dysplasias.
It was hypothesized, in the porcine chondrocyte study, that the increased Ca2+ signaling due to the
V620I and T89I TRPV4 mutations increased FST expression that inhibited BMP signaling and hyper-
trophy (Leddy et al., 2014a; Leddy et al., 2014b). Surprisingly, we found no differences in FST
expression in mutant hiPSC- derived chondrocytes compared to WT. However, our previous study used
non- human cells, which could alter the effects of the human TRPV4 mutations and downstream gene
expression. Another previous hypothesis made was that the altered TRPV4 signaling increased SOX9
expression, a known regulator of resting and proliferating chondrocytes up- regulated by TRPV4 acti-
vation (Muramatsu etal., 2007), thus decreasing hypertrophy (Rock etal., 2008). SOX9- knockin mice
exhibit a dwarfism phenotype (Amano etal., 2009), and SOX9 overexpression inhibits hypertrophy
and endochondral ossification (Hattori etal., 2010; Lui etal., 2019), likely via parathyroid hormone-
related protein (PTHrP) (Amano etal., 2009; Nishimura et al., 2012b). However, PTHrP was not
strongly regulated in our data set. Furthermore, our RT- qPCR revealed that only V602I significantly
up- regulated SOX9, and the RNAseq data showed that SOX9 had a smaller fold change compared to
other chondrogenic genes, such as GDF5, COL6A1, COL6A3, and COMP. In fact, these genes, which
were up- regulated in V620I- and T89I- hiPSC- derived chondrocytes, have a pro- chondrogenic but anti-
hypertrophic phenotype (Caron etal., 2020; Chu etal., 2017; Hecht and Sage, 2006). Therefore,
these results suggest additional and alternative pathways to FST and SOX9 that are responsible for
the V620I and T89I skeletal dysplasias.
Our results are generally consistent with previous reports on the effects of other TRPV4 mutations
such as lethal and non- lethal metatropic dysplasia- causing I604M (Saitta et al., 2014) and L619F
(Nonaka etal., 2019). The data also reveal potential differences in the effects of these varying TRPV4
mutations on cell electrophysiology or differentiation. For example, we saw an increase in SOX9
expression in V620I, while no change in T89I. Gain- of- function mutation L619F also increased SOX9
expression (Nonaka etal., 2019), while I604M, which has been reported to not alter conductivity like
T89I (Kang, 2012), decreased SOX9 expression (Saitta etal., 2014). I604M also decreased COL2A1,
COL10A1, and RUNX2 expression consistent with our T89I results (Saitta etal., 2014). Intriguingly, the
L619F mutation was reported to increase Ca2+ signaling with activation via a TRPV4 agonist (Nonaka
et al., 2019). However, we observed that V620I and T89I had significantly reduced Ca2+ signaling
compared to WT in response to chemical agonist GSK101, as confirmed by both confocal imaging
and patch clamping. These results highlight that TRPV4 mutations have heterogeneous effects on
downstream signaling pathways and thus lead to diverse disease phenotypes, despite similar classi-
fication of these mutations as ‘gain- of- function’. It is also important to note that in previous studies,
chondrogenic differentiation of iPSCs (Saitta etal., 2014) or dental pulp cells (Nonaka etal., 2019)
were performed in short- term micromass culture, and not long- term pellet culture as in our study,
potentially leading to different levels of chondrogenesis and maturation of the cells.
Our transcriptomic analysis showed significant changes in various HOX family genes due to TRPV4
mutations, suggesting a potential role of these genes in maintaining the immature, chondrogenic
phenotype in the mutated lines. At both days 28 and 56, the top 25 up- regulated genes in the V620I
and T89I lines included genes from the anterior HOX family (Iimura and Pourquié, 2007; Seifert
etal., 2015). The high expression of anterior HOX genes indicates that the mutants are maintaining
the chondrocytes in an early developmental stage with axial patterning. At days 28 and 56, HOXA2,
HOXA3, and HOXA4 were in the top up- regulated genes, with HOXA4 having the largest fold change.
Interestingly, gain- of- function mutations or overexpression of HOXA2, HOXA3, and HOXA4 impair
chondrogenesis, limit skeletal development, decrease endochondral ossification regulators, and delay
mineralization in animal models (Creuzet etal., 2002; Deprez etal., 2013; Kanzler etal., 1998; Li
and Cao, 2006; Massip etal., 2007; Seifert etal., 2015). HOXA5 was also highly up- regulated at
both days 28 and 56, and mutations in this gene showed disordered patterning of limb bud develop-
ment (Pineault and Wellik, 2014). Finally, the rib and spine phenotypes associated with brachyolmia
and metatropic dysplasia could be contributed to the altered expression of HOXA4 to HOXA7 as
it has been shown that these genes are associated with rib and spine patterning, and alterations in
expression have led to defects (Chen etal., 1998; Wellik, 2009). The only up- regulated posterior

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HOX genes were HOXC8 and HOXD8 at day 56 (Iimura and Pourquié, 2007; Seifert etal., 2015).
The absence of posterior HOX9, HOX11, and HOX13, which are associated with limb development
and hypertrophic RUNX2/3 expression (Pineault and Wellik, 2014; Qu etal., 2020), may be at least
partially responsible for the improper development in skeletal dysplasias. Interestingly, many links
have been identified between HOX genes and TGFβ3- family signaling, specifically through SMAD
proteins, both within skeletal development and other processes (e.g., murine lung development) (Li
and Cao, 2006; Li and Cao, 2003; Volpe etal., 2013).
In fact, TRPV4 and TGF-β signaling have recently been shown to interact, with effects specific to
the order in which they occur (Nims etal., 2021; O’Conor etal., 2014; Woods etal., 2021). Consis-
tent with previous finding with hiPSCs housing the I604M TRPV4 mutations (Saitta etal., 2014), the
altered TRPV4 activity in our hiPSC- derived chondrocytes could be altering their response to the
TGFβ3 and BMP4 treatments. Furthermore, the V620I and T89I mutations increased expression of
HTRA1, which has been shown to bind to and alter the response to members of the TGFβ family
(Polur etal., 2010). Furthermore, TGFβ3 and TWIST, which is downstream of TGFβ3 signaling, were
both up- regulated in TRPV4- mutated hiPSC- derived chondrocytes. It has been reported that TGFB3
expression and signaling prevent osteoblastogenesis of mesenchymal stem cells (Nishimura etal.,
2012a; Nishimura etal., 2012b), while TWIST inhibits hypertrophy regulators RUNX2 and FGFR2
(Michigami, 2014; Miraoui and Marie, 2010). Therefore, another mechanism of hypertrophic dysreg-
ulation with these mutations could be altered response to TGFβ family signaling.
In fact, in response to treatment with BMP4, a member of the TGFβ family, there was increased
expression of GSTA1, which produces the antioxidant glutathione (Chen etal., 2008; Hayes etal.,
2005), in WT but not in mutants. BMP4 treatment of T89I- mutated chondrocytes significantly increased
expression of another antioxidant catalase (CAT ) compared to its TGFβ3 control group; however,
TRPV4- mutated chondrocytes without BMP4 treatment had significantly lower CAT expression. This
may potentially indicate an association between antioxidants, which remove reactive oxygen species
(ROS; e.g., H2O2), and chondrocyte maturation. While one study observed that chondrocyte matura-
tion is associated with decreasing catalase (Morita etal., 2007), this is inconsistent with other find-
ings. Another report stated hypoxia, which increases ROS, inhibits hypertrophic differentiation and
endochondral ossification (Leijten etal., 2012). Many others found that ROS prevent endochondral
ossification, potentially via inhibition of the hedgehog pathways (Atashi etal., 2015; Chen et al.,
2008; Fragonas et al., 1998). Interestingly, IHH also had the lowest expression level in our T89I
mutant chondrocytes. These findings suggest that decreased expression of CAT and GSTA1 in TRPV4
mutants may also be involved in dysregulating endochondral ossification in these cells.
GSTA1 is one of many genes with significantly lower expression in mutated chondrocytes compared
to WT in response to BMP4 treatment including ALPL, AMELX, IFITM5, IBSP, and MEPE. These genes
play important roles in bone development. For example, IBSP is downstream of RUNX2, a primary
transcription factor of hypertrophic differentiation and osteoblast differentiation (Komori, 2018).
Further, MEPE negatively (Lu etal., 2004; Staines etal., 2012) and IFITM5 (Hanagata etal., 2011;
Moffatt etal., 2008) and ALPL (Millán, 2013; Strzelecka- Kiliszek etal., 2018) positively regulate
bone mineralization during skeletogenesis, respectively. Mutations in these genes also lead to bone
mineralization diseases such as rickets (Lu etal., 2004; Staines etal., 2012), osteogenesis imperfecta
(Hanagata, 2016), and hypophosphatasia with deformed long bones (Taillandier etal., 2015). Not
only did we observe significantly lower gene expression of ALPL in TRPV4- mutated chondrocytes
treated with BMP4; we also demonstrated that ALPL protein production is negatively associated with
disease severity. Our results indicate that not only do the mutated cells have an altered hypertro-
phic response to BMP4, but there is a connection between these genes, particularly ALPL, or tissue-
nonspecific alkaline phosphatase, and delayed endochondral ossification in chondrocytes bearing
V620I or T89I mutations. However, how these genes and their transcription are associated with TRPV4
function and mutations still warrants further investigation.
The gene expression and protein production of ALPL, as well as IHH, in response to BMP4 treat-
ment was not only significantly lower in mutants compared to WT, but it was also significantly lower for
the severe T89I mutation compared to the moderate V620I mutation. This is one of the many targets
showing the different levels of genetic and protein production between the two mutations that may
be responsible for the distinct severity and phenotype of the corresponding diseases. Furthermore,
several other genes were also detected to be uniquely up- or down- regulated compared to WT in

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one mutated line but not the other. Interestingly, there were more uniquely expressed genes between
the two mutated lines at day 56, suggesting the separation of transcriptomic profiles in V620I and
T89I occurs at later time points of chondrocyte maturity. Among those unique to severe T89I include
down- regulation of IBSP, a positive mineralization regulator, at day 28, and up- regulation of nega-
tive regulator MEPE at day 56. These unique genes were also not associated with many of the same
biological processes as WT and V620I, especially those regarding endochondral ossification, when
treated with BMP4. This, in conjunction with the high number of unique DEGs, represents a poten-
tial inhibition of hypertrophy, particularly in response to BMP4 treatment, with the T89I mutation
leading to severe metatropic dysplasia. V620I also had unique differences from both WT and T89I. The
decrease in mechanical properties with increased basal current of the V620I mutant was unexpected
since TRPV4 activation was previously shown to increase matrix production and properties (O’Conor
et al., 2014). Furthermore, genes uniquely up- regulated in V620I were associated with interferon
type I (IFNβ). IFNβ has been reported to decrease inflammatory markers and matrix degradation (Hu
etal., 2005; Palmer etal., 2004; van Holten etal., 2004; Zhao etal., 2014), despite the decrease
in moduli observed in the day- 42 V620I chondrogenic pellets. Interestingly, a study comparing bone
marrow- derived MSCs from healthy and systemic lupus erythematous patients found that IFNβ-inhib-
ited osteogenesis via suppression of RUNX2 and other osteogenic genes (Gao etal., 2020). High-
lighting a potential, unique regulator of the delayed hypertrophy in V620I leading to brachyolmia.
Here, we present multiple putative genes and pathways that could be involved in delaying, and
potentially inhibiting, chondrocyte hypertrophy in V620I- and T89I- TRPV4 mutants. It should be
noted, however, that this study has some potential limitations. It is well- recognized that Wnt/β-catenin
signaling plays an important role in chondrocyte hypertrophy (Hou etal., 2019; Huang etal., 2018;
Michigami, 2014). After 56 days of differentiation, we observed increased expression of β-catenin-
coding gene CTNNB1 in T89I- mutated chondrocytes highlighting this pathway could be playing
a role in the inhibited hypertrophy. However, we may be preventing some hypertrophy since our
chondrogenic protocol uses a pan- Wnt inhibitor to prevent off- target differentiation and promote a
homogenous chondrocyte population (Wu etal., 2021). Nevertheless, our WT chondrocytes, but not
TRPV4 mutants, exhibited hypertrophic differentiation with BMP4 treatment, suggesting that DEGs/
pathways detected in our sequencing analysis are still robust. Since this study focuses on TRPV4 gain-
of- function mutations, future studies could fully or partially inhibit TRPV4 signaling to determine if that
would increase similarity between the mutant and WT lines at various stages of chondrogenic and
hypertrophic differentiation. Additionally, this study only activated TRPV4 using the pharmacological
activator GSK101. Other future experiments could activate the channel osmotically or with mechanical
loading to investigate additional differences in TRPV4 function leading to skeletal dysplasias during
development.
In summary, our study found that dysregulated skeletal development in the V620I- and T89I- TRPV4
dysplasias is likely due, at least in part, to delayed and inhibited chondrocyte hypertrophy. The gain-
of- function mutations may lead to increased HOX gene expression, altered TGFβ signaling, decreased
hypertrophic and biomineralization gene expression (e.g., ALPL, AMELX, IFITM5, IBSP, and MEPE),
and genes regulating hedgehog pathways and ROS accumulation (e.g., GSTA1 and CAT). Our find-
ings lay a foundation for the development of therapeutics for these diseases and provide significant
insights into the regulation of endochondral ossification via TRPV4.
Materials and methods
hiPSC culture
The BJFF.6 (BJFF) human iPSC line (Washington University Genome Engineering and iPSC Center
[GEiC], St. Louis, MO), was used in this study as the isogenic WT control. CRISPR- Cas9 gene editing
was used to create the V620I and T89I mutations in the BJFF cell line as described previously (Adkar
etal., 2019). All three lines underwent STR profiling for cell line authentication and were verified to
have no cross- contamination with other cell lines. All cells tested negative for mycoplasma. The hiPSCs
were maintained on vitronectin (VTN- N; cat. num. A14700; Thermo Fisher Scientific, Waltham, MA)-
coated plates in Essential 8 Flex medium (E8; cat. num. A2858501; Gibco, Thermo Fisher Scientific,
Waltham, MA). Medium was changed daily until cells were passaged at 80–90% confluency (medium

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supplemented with Y- 27632 [cat. num. 72304; STEMCELL Technologies, Vancouver, Canada] for 24hr)
or induced into mesodermal differentiation at 30–40%confluency.
Mesodermal differentiation
The hiPSCs were differentiated through the mesodermal pathway as previously described (Adkar
etal., 2019; Dicks etal., 2020; Wu etal., 2021). In brief, cells were fed daily with different cock-
tails of growth factors and small molecules for 12 days in mesodermal differentiation medium and
driven through the anterior primitive streak (1 day; 30ng/ml Activin [cat. num. 338- AC; R&D Systems,
Minneapolis, MN], 20ng/ml FGF2 [cat. num. 233- FB- 025/CF; R&D Systems, Minneapolis, MN], 4µM
CHIR99021 [cat. num. 04- 0004- 02; Reprocell, Beltsville, MD]), paraxial mesoderm (1 day; 20ng/ml
FGF2, 3µM CHIR99021, 2µM SB505124 [cat. num. 3263; Tocris Bioscience, Bristol, UK], 4µM dorso-
morphin [DM; cat. num. 04- 0024; Reprocell, Beltsville, MD]), early somite (1 day; 2 µM SB505124,
4µM dorsomorphin, 500nM PD173074 [cat. num. 3044; Tocris Bioscience, Bristol, UK], 1µM Wnt- C59
[cat. num. C7641- 2s; Cellagen Technologies, San Diego, CA]), and sclerotome (3 days; 1µM Wnt- C59,
2 µM purmorphamine [cat. num. 04- 0009; Reprocell, Beltsville, MD]) into chondroprogenitor cells
(6 days; 20ng/ml BMP4 [cat. num. 314- BP- 010CF; R&D Systems, Minneapolis, MN]). Mesodermal
differentiation medium had a base of Iscove’s Modified Dulbecco’s Medium, glutaMAX (IMDM;
cat. num. 31980097; Gibco, Thermo Fisher Scientific, Waltham, MA) and Ham’s F- 12 nutrient mix,
glutaMAX (F12; cat. num. 31765092; Gibco, Thermo Fisher Scientific, Waltham, MA) in equal parts
supplemented with 1% penicillin–streptomycin (P/S; cat. num. 15140122; Gibco, Thermo Fisher Scien-
tific, Waltham, MA), 1% Insulin–Transferrin–Selenium (ITS+; cat. num. 41400045; Gibco, Thermo Fisher
Scientific, Waltham, MA), 1% chemically defined concentrated lipids (cat. num. 11905031; Thermo
Fisher Scientific, Waltham, MA), and 450µM 1- thioglycerol (cat. num. M6145; Millipore Sigma, St.
Louis, MO). The chondroprogenitor cells were then disassociated for chondrogenic differentiation.
Chondrogenic differentiation with 3D pellet culture
Cells were differentiated into chondrocytes using a high- density, suspension pellet culture (Adkar
etal., 2019; Dicks etal., 2020; Wu etal., 2021). In summary, cells were resuspended in chondro-
genic medium: Dulbecco’s Modified Eagle Medium/F12, glutaMAX (DMEM/F12; cat. num. 10565042;
Gibco, Thermo Fisher Scientific, Waltham, MA), 1% P/S, 1% ITS+, 1% Modified Eagle Medium
(MEM) with nonessential amino acids (NEAA; cat. num. 11140050; Gibco, Thermo Fisher Scientific,
Waltham, MA), 0.1% dexamethasone (Dex; cat. num. D4902; Millipore Sigma, St. Louis, MO), and
0.1% 2- Mercaptoethnol (2- ME; cat. num. 21985023; Gibco, Thermo Fisher Scientific, Waltham, MA)
supplemented with 0.1% L- ascorbic acid (ascorbate; cat. num. A8960; Millipore Sigma, St. Louis,
MO), 0.1% L- proline (proline; cat. num. P5607; Millipore Sigma, St. Louis, MO), 10ng/ml human trans-
forming growth factor- β3 ( TGFβ3; cat. num. 243- B3- 010/CF; R&D Systems, Minneapolis, MN), 1µM
Wnt- C59, and 1µM ML329 (cat. num. 22481; Cayman Chemical, Ann Arbor, MI) at 5 × 105 cells/ml.
One mL of the cell solution was added to a 15- ml conical tube (cat. num. 430790; Corning, Corning,
NY) and centrifuged to form the spherical pellets. Pellets were fed every 3–4 days with complete
chondrogenic medium until the desired time point. Several time points of the chondrogenic pellets
were used to study chondrocyte maturation (7, 14, 28, and 42 days), mechanical properties (28 and 42
days), hypertrophy (28 days) or, after digestion to single- cell day- 28 chondrocytes, on Ca2+ signaling
in response to pharmacological activation of TRPV4.
BMP4 treatment to promote hypertrophic differentiation
Some day- 28 pellets were also further differentiated for an additional 4 weeks to examine the effects
of the mutations on chondrocyte hypertrophy. Pellets were cultured with complete chondrogenic
medium with either TGFβ3 (10 ng/ml) alone, BMP4 (50ng/ml) alone, or a combination of TGFβ3
(10ng/ml) and BMP4 (50ng/ml).
Dissociation of chondrogenic pellets to obtain single-cell hiPSC-derived
chondrocytes
To isolated hiPSC- derived chondrocytes, day- 28 chondrogenic pellets were rinsed and placed in an
equal volume (1 pellet per 1 ml) of digestion medium (0.4%wt/vol type II collagenase [cat. num.
LS00417; Worthington Biochemical, Lakewood, NJ] in DMEM/F12 with 10% fetal bovine serum [FBS;

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cat. num. S11550; Atlanta Biologicals, R&D Systems, Minneapolis, MN]). The tubes were placed on an
orbital shaker at 37°C and vortexed every 20min for approximately 2hr. Once the tissue was digested
and could no longer be seen by the naked eye, the digestion medium was neutralized in DMEM/F12
medium containing 10% FBS. These cells were used for patch clamping and confocal experiments.
TRPV4 agonists and antagonists
Solutions were prepared immediately before experiments and held at room temperature.
GSK1016790A (GSK101; cat. num. G0798; Sigma- Aldrich, St. Louis, MO) and/or GSK205 (cat. num.
AOB1612 1263130- 79- 5; AOBIOUS, Gloucester, MA), in addition to dimethyl sulfoxide (DMSO)
for a vehicle control, were added to assay buffer (Hanks’ Balanced Salt Solution [HBSS; cat. num.
14025076; Gibco, Thermo Fisher Scientific, Waltham, MA] with 2% N- 2- hydroxyethylpiperazine- N'-2-
ethanesulfonic acid (HEPES) [cat. num. 15630130; Gibco, Thermo Fisher Scientific, Waltham, MA]) at
2- folds of the desired concentration (20nM GSK101, 40µM GSK205). Solutions were made at 2- folds
of the desired concentration because they would be mixed at an equal volume of assay buffer after
capturing a baseline fluorescence in Ca2+ signaling experiments.
Patch clamping
Isolated chondrocytes were kept on ice and used for patching within 36hr. Patch- clamp experiments
were carried out at RT under two conditions. Single- channel measurements were made in excised
inside- out membrane patches in a symmetric potassium chloride (KCl) solution (148mM KCl, 1mM
K2EDTA, 1mM ethylene glycol- bis(β-aminoethyl ether)- N,N,N′,N′-tetraacetic acid (egtazic acid; EGTA),
10mM HEPES, pH 7.4). Channel activation was achieved by bath perfusion with the same buffer solu-
tion containing 10nM GSK101. Blocking was performed using the same buffer solution supplied with
both 10nM GSK101 and 20µM GSK205. Recordings were made at −30mV membrane. Whole- cell
currents were recorded using an external sodium chloride (NaCl) solution (150mM NaCl, 5mM KCl,
1mM EGTA, 10mM glucose, 10mM HEPES, and 10µM free Ca2+) and KCl pipette solution as used
for single- channel recordings. Inhibition of basal currents was performed by pre- incubation of the
cells in external solution supplied with 20µM GSK205 for 20min before patching; the drug was also
present in the bath at the same concentration during the experiment. Data were acquired at 3kHz,
low- pass filtered at 1kHz with Axopatch 1D patch- clamp amplifier and digitized with Digidata 1320
digitizer (Molecular Devices, San Jose, CA). Data analysis was performed using the pClamp software
suite (Molecular Devices, San Jose, CA). Pipettes with 2.0–4.0 MOhm resistance in symmetric 150mM
KCl buffer were pulled from Kimble Chase 2502 soda lime glass with a Sutter P- 86 puller (Sutter Instru-
ments, Novato, CA).
Confocal imaging of Ca2+ signaling
hiPSC- derived chondrocytes from digested pellets were plated in DMEM medium containing 10%
FBS at 2.1 × 104 cells/cm2 in 35- mm dishes for 6–8 hr to allow the cells to adhere without dediffer-
entiating. Cells were then rinsed and stained for 30min with Fluo- 4 AM (cat. num. F14201; Thermo
Fisher Scientific, Waltham, MA), Fura Red AM (cat. num. F3021; Thermo Fisher Scientific, Waltham,
MA), and sulfinpyrazone (cat. num. S9509- 5G; Sigma- Aldrich, St. Louis, MO) with 20mM GSK205 or
1000× DMSO (vehicle control). The dye solution was replaced with assay buffer before imaging cells
on a confocal microscope (LSM 880; Zeiss, Oberkochen, Germany) at baseline for the first 100 frames
(approximately 6min). Then, an equal volume of a 2× solution of GSK101 or GSK101 and GSK205
was added, and imaging continued for an additional 300 frames (approximately 20min). Fiji software
(ImageJ, version 2.1.0) was used to locate cells and quantify the ratiometric fluorescence intensity
(Intensityfluo- 4/Intensityfura red). In brief, .czi files were imported into Fiji and the channels were split. After
applying the median filter, the image calculator divided the green channel by the red. A Z- projection
was performed based on the maximum fluorescence of the red channel (to ensure that all cells were
identified even in groups were there was no increase in Ca2+ signaling). A threshold and watershed
binary were then applied, and measurements were set for a cell size of 100- infinity. Outlines were
projected, and the mean fluorescence of each cell was measured over time. The average fluorescence
was plotted for all the cells in the group over time. Area under the curve and time of response were
calculated to quantify differences between groups. Cells were classified as responders if they had a
fluorescence greater than the baseline mean plus 3 times the standard deviation in at least a quarter

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of the frames. Time of response was the time of the first frame in which the cell responded for at least
two consecutive frames. The fluorescence was measured for all the cells in the frame of view as tech-
nical replicates for two experimental replicates.
AFM measurement of neocartilage mechanical properties
Day- 28 and -42 hiPSC- derived pellets were rinsed in phosphate- buffered saline (PBS) and snap frozen
in optimal cutting temperature (cat. num. 4583; Sakura Finetek, Torrance, CA) medium and stored
at −80 °C. Pellets were cryosectioned using cryofilm (type 2C(10); Section- Lab, Hiroshima, Japan)
in multiple different regions of the pellet (i.e., zones). The 10µm cryosection with cryofilm was fixed
on a microscope slide using chitosan and stored at 4°C overnight. The next day, cryosections were
mechanically loaded using an AFM (MFP- 3D Bio, Asylum Research, Goleta, CA) as previously described
(Votava etal., 2019). Briefly, the samples were tested in PBS at 37°C to maintain hydration and mimic
physiologic conditions, respectively. The sections were mechanically probed using a silicon cantilever
with a spherical tip (5μm diameter, k ~ 7.83N/m, Novascan Technologies, Ames, IA). An area of 10
μm2 with 0.5μm intervals (400 indentations) was loaded to 300 nN with the loading rate of 10μm/s.
Multiple locations from different sites of each zone and pellet were loaded as replicates. The curves
obtained from AFM were imported into a custom written MATLAB code to determine the mechanical
properties of the pellets. Using contact point extrapolation, the contact point between the cantilever’s
tip and the tissue was detected, and the elastic modulus was calculated using a modified Hertz model
(Darling etal., 2010; Darling etal., 2006; Votava etal., 2019; Wilusz etal., 2013; Zelenski etal.,
2015). This code is available at: https://github.com/guilak-lab/programs/tree/guilak-lab-TRPV4-paper
(copy archived at swh:1:rev:465cfaeea5676c514c264785b5db626513baa0d1; Dicks etal., 2022).
Histology
Chondrogenic pellets at days 7, 14, 28, 42, and 56 (with and without BMP4) were fixed and dehydrated
in sequential steps of increasing ethanol and xylene solutions until embedded in paraffin wax. Wax
blocks were cut into 8µm sections on microscope slides for histological and immunohistochemical
analysis. Slides were rehydrated in ethanol and water and the nuclei were stained with Harris hema-
toxylin and sGAGs with Safranin- O. Antigen retrieval was performed on rehydrated slides followed by
blocking, the addition of primary and secondary antibodies, and AEC development to label collagen
proteins (COL1A1, COL2A1, COL6A1, and COL10A1) and Vector Hematoxylin QS counterstain (cat.
num. H- 3404, Vector Laboratories, Newark, CA).
Biochemical analysis
Chondrogenic pellets at days 7, 14, 28, and 42 were washed with PBS and digested in papain over-
night at 65°C. sGAG and dsDNA content were measured using the dimethylmethylene blue (DMMB;
cat. num. 341088, Sigma- Aldrich, St. Louis, MO) and PicoGreen assays (Quant- iT PicoGreen dsDNA
Assay Kit; cat. num. P7589; Thermo Fisher Scientific, Waltham, MA), respectively. sGAG content was
normalized to dsDNA. Three to four independent experiments were performed with 3–4 technical
replicates per group.
Western blot
Day- 56 pellets treated with TGFβ3, TGFβ3 + BMP4, or BMP4 were digested to single cells, as
described above, and lysed in RIPA buffer (cat. num. 9806S; Cell Signaling Technology, Danvers, MA)
with protease inhibitor (cat. num. 87786; Thermo Fisher Scientific, Waltham, MA). Protein concen-
tration was then measured using the BCA Assay (Pierce). Twenty micrograms of protein for each
well were separated on 10% sodium dodecyl sulfate–polyacrylamide gel electrophoresis gel with
pre- stained molecular weight markers (cat. num. 161- 0374; Bio- Rad, Hercules, CA) and transferred
to a polyvinylidene fluoride (PVDF) membrane. The PVDF membrane blots were incubated over-
night at 4°C with the primary antibodies, respectively: anti- COL10A1 (1:500; cat. num. PA5- 97603;
Thermo Fisher Scientific, Waltham, MA), anti- RUNX2 (1:2000; cat. num. 41- 1400, Thermo Fisher Scien-
tific), anti- MMP13 (1;2000; cat. num. MA5- 14238; Thermo Fisher Scientific, Waltham, MA), anti- IHH
(1:500; cat. num. MA5- 37541; Thermo Fisher Scientific, Waltham, MA), anti- ALPL (1:3000; cat. num.
MAB29092, R&D systems), and anti- GAPDH (1:30000; cat. num. 60004- 1- Ig; Proteintech, Rosemont,
IL) as the loading control. TidyBlot- Reagent- HRP (1:1000; cat. num. 147711; Bio- Rad, Hercules, CA)

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and horse anti- mouse IgG secondary antibody (1:3000; cat. num. 7076; Cell Signaling, Danvers, MA)
were used accordingly. Immunoblots were imaged using the iBright FL1000 Imaging System (Thermo
Fisher Scientific, Waltham, MA). Using photoshop, the images were inverted, and the protein abun-
dance of each band was quantified by multiplying the mean of signal intensity by the pixels of the
individual band. The relative protein abundance was normalized to the GAPDH levels. The maximum
value was arbitrarily set to 1.
RNA isolation
Chondrogenic pellets at days 7, 14, 28, 42, and 56 were washed with PBS, lysed, snap frozen, and
homogenized. RNA was isolated using the Total RNA Purification Plus Kit (cat. num. 48400; Norgen
Biotek, Thorold, Canada) and used immediately for either RT- qPCR or RNA- seq.
Gene expression with RT-qPCR
Isolated RNA was reverse transcribed into cDNA. The cDNA was used to run real- time, quantitative
PCR using Fast SYBR Green Master Mix (cat. num. 4385610; Thermo Fisher Scientific, Waltham, MA).
Gene expression was analyzed using the ΔΔCT method with hiPSC as the reference time point and TBP
as the housekeeping gene (Livak and Schmittgen, 2001). Three to four independent experiments
were performed with 3–4 technical replicates per group. Primers can be found in Figure3—figure
supplement 1.
Genome-wide mRNA sequencing
Isolated RNA was treated with DNase (cat. num. 25720; Norgen Biotek, Thorold, Canada) and cleaned
(cat. num. 43200; Norgen Biotek, Thorold, Canada) according to the manufacturer’s instructions prior
to submitting to the Genome Technology Access Center at Washington University in St. Louis (GTAC).
Libraries were prepared according to the manufacturer’s protocol. Samples were indexed, pooled,
and sequenced at a depth of 30 million reads per sample on an Illumina NovaSeq 6000. Basecalls
and demultiplexing were performed with Illumina’s bcl2fastq software and a custom python demul-
tiplexing program with a maximum of one mismatch in the indexing read. RNA- seq reads were then
aligned to the Ensembl release 76 primary assembly with STAR version 2.5.1a (Dobin etal., 2013).
Gene counts were derived from the number of uniquely aligned unambiguous reads by Subread:fea-
tureCount version 1.4.6- p5 (Liao etal., 2014). Isoform expression of known Ensembl transcripts were
estimated with Salmon version 0.8.2 (Patro etal., 2017). Sequencing performance was assessed for
the total number of aligned reads, total number of uniquely aligned reads, and features detected. The
ribosomal fraction, known junction saturation, and read distribution over known gene models were
quantified with RSeQC version 2.6.2 (Wang etal., 2012).
Transcriptomic analysis of sequencing datasets
R and the DESeq2 package were used to read un- normalized gene counts, and genes were removed
if they had counts lower than 200 (Love et al., 2014). Regularized- logarithm transformed data of
the samples were visualized with the Pheatmap package (Kolde, 2015) function on the calculated
Euclidean distances between samples or with the ggplot2 package (Wickham, 2009) to create a
PCA. The transformed data were also used to determine the top 5000 most variable genes across the
samples. The replicates, from DESeq data, for each group were averaged together, and the up- and
down- regulated DEGs were determined. The total number of DEGs was plotted using GraphPad
Prism. At day 28, the V620I and T89I lines were compared to WT. At day 56, TGFβ3- treated V620I
and T89I were compared to TGFβ3- treated WT, and BMP4- treated groups were compared to their
respective TGFβ3- treated group of the same line (e.g., BMP4- treated WT vs. TGFβ3- treated WT).
Genes were considered differentially expressed if adjusted p value (padj) <0.1 and log2(fold change)
≥1 or ≤−1. The intersecting and unique DEGs were determined and plotted with the intersect and
setdiff, and venn.diagram functions (VennDiagram package; Chen and Boutros, 2011). The fold
changes of common chondrogenic, hypertrophic, growth factor, Ca2+ signaling, and off- target genes,
in the top 5000 most variable genes, were plotted using the pheatmap function. The top 25 most
up- and down- regulated for each group, based on log2(fold change), and the log2(fold change) of that
gene for the other group(s) were also plotted with the pheatmap. Gene lists (e.g., intersected genes,
genes up- regulated with BMP4 treatment) were entered into g:profiler to determine associated GO

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Biological Processes, Molecular Functions, Cellular Components, KEGG pathways, Reactome path-
ways, and Human Phenotype (HP) Ontologies (Raudvere et al., 2019). The negative log10 of the
adjusted p value for each term was plotted with GraphPad Prism or using a function to scale circle
diameter to the p value in Illustrator.
The gap statistic method determined the ideal number of clusters resulting from BMP4 treat-
ment was either 1 or 9. We then performed k- means clustering with 9 clusters and plotted the gene
expression trends for each gene within the cluster with the average expression trend overlaying for
each cell line of the largest cluster using the tidyverse package (Altman and Krzywinski, 2017). The
genes in each cluster, with the normalized counts for each group, are listed in Supplementary file
1. The largest cluster was plotted using the Cytoscape String app’s protein interaction to create a
protein–protein network (Doncheva etal., 2019; Shannon etal., 2003). Using the average log fold
change with BMP4 treatment across lines, the network was propagated using the Diffusion app, and
functional enrichment with EnrichmentMap was performed on the network (Merico etal., 2010). We
then created a network connecting the genes to their associated genes with black lines and to their
associated GO processes using gray lines. We colored the gene circles with three colors representing
the log fold change of that gene in each line. The white arrows were added to the color scale legend
to indicate maximum log fold change for each line.
Statistical analysis
Data were graphed and analyzed using GraphPad Prism (Version 9.1.0). Outliers were removed from
the data using the ROUT method (Q = 1%), and the data were tested for normality with the Shapiro–
Wilk test (a = 0.05). For RT- qPCR, normally distributed data were analyzed within each time point
using a Brown–Forsythe and Welch one- way analysis of variance (ANOVA) with multiple comparisons
(mean of each column, cell line, with every other column). A Kruskal–Wallis test was used if data were
not normally distributed. For biochemical analysis, mechanical properties, and area under the curve,
and time of response, data were analyzed using an ordinary two- way ANOVA, comparing each cell
with all other cells, with Tukey’s post hoc test. Area under the curve was quantified for plots over
time considering a baseline of Y = 0, ignoring peaks less than 10% of the distance from minimum to
maximum Y, and all peaks going over the baseline.
Acknowledgements
This work was supported by Shriners Hospitals for Children – St. Louis, the National Institutes of Health
(R01 AG46927, R01 AG15768, R01 AR072999, R00 AR075899, P30 AR073752, P30 AR074992, T32
DK108742, T32 EB018266, and CTSA grant UL1 TR002345). We would like to thank the Washington
University Genome Engineering and iPSC Center and the Genome Technology Access Center (GTAC)
for their assistance with the CRISPR- Cas9 editing and RNA sequencing, respectively. We would also
like to thank Dr. Monica Sala- Rabanal for assistance and advice in the initial aspects of this study.
Additional information
Competing interests
Wolfgang Liedtke: Patents on TRPV4 inhibitors have been licensed to TRPblue (US Patents 9,701,675;
10,329,265; and 11,014,896). Dr. Liedtke is an executive employee of Regeneron Pharmaceuticals
(Tarrytown NY).. Farshid Guilak: Patents on TRPV4 inhibitors licensed to TRPblue Inc (US Patents
9,701,675; 10,329,265; and 11,014,896). The other authors declare that no competing interests exist.
Funding
Funder Grant reference number Author
National Institutes of
Health
AG15768 Farshid Guilak
National Institutes of
Health
AG46927 Farshid Guilak

Research article Stem Cells and Regenerative Medicine
Dicks etal. eLife 2023;12:e71154. DOI: https://doi.org/10.7554/eLife.71154 23 of 34
Funder Grant reference number Author
National Institutes of
Health
ar072999 Farshid Guilak
National Institutes of
Health
AR075899 Chia-Lung Wu
The funders had no role in study design, data collection, and interpretation, or the
decision to submit the work for publication.
Author contributions
Amanda R Dicks, Conceptualization, Data curation, Formal analysis, Validation, Writing – original draft;
Grigory I Maksaev, Zainab Harissa, Data curation, Formal analysis, Investigation, Writing – review and
editing; Alireza Savadipour, Data curation, Formal analysis, Investigation, Visualization, Methodology,
Writing – review and editing; Ruhang Tang, Data curation, Formal analysis, Investigation, Visualiza-
tion, Writing – review and editing; Nancy Steward, Conceptualization, Data curation, Formal anal-
ysis, Supervision, Funding acquisition, Investigation, Writing – review and editing; Wolfgang Liedtke,
Conceptualization, Supervision, Funding acquisition, Writing – review and editing; Colin G Nichols,
Conceptualization, Supervision, Investigation, Writing – review and editing; Chia- Lung Wu, Concep-
tualization, Resources, Supervision, Funding acquisition, Investigation, Writing – review and editing;
Farshid Guilak, Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding
acquisition, Investigation, Writing – review and editing
Author ORCIDs
Amanda R Dicks
http://orcid.org/0000-0002-6036-280X
Colin G Nichols
http://orcid.org/0000-0002-4929-2134
Chia- Lung Wu
http://orcid.org/0000-0001-9598-7036
Farshid Guilak
http://orcid.org/0000-0001-7380-0330
Decision letter and Author response
Decision letter https://doi.org/10.7554/eLife.71154.sa1
Author response https://doi.org/10.7554/eLife.71154.sa2
Additional files
Supplementary files
• Supplementary file 1. Additional figures to support data in Figures2, 3 and 5. Figure2—figure
supplement 1. Matrix production and mechanical properties through day 42 of chondrogenic
differentiation. Figure3—figure supplement 1. Gene expression using RT- qPCR through day 42 of
chondrogenic differentiation. Figure4—figure supplement 1. Top 15 mutant- specific differentially
expressed genes (DEGs) compared to wildtype (WT) and genes of interest at days 28 and 56
as identified by RNA sequencing. Figure4—figure supplement 2. Top 25 most up- and down-
regulated DEGs compared to WT at day 56 as identified by RNA sequencing. Figure5—figure
supplement 1. Hypertrophic gene and protein expression in TGFβ3- and BMP4- treated day- 56
chondrogenic pellets.
• Transparent reporting form
Data availability
All RNAseq data files generated and reported in this study are available on GEO (accession number
GSE225446,https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE225446).
The following dataset was generated:
Author(s) Year Dataset title Dataset URL Database and Identifier
Dicks AR, Maksaev
GI, Harissa Z,
Savadipour A, Tang
R, Steward N, Liedtke
W, Nichols CG, Wu C,
Guilak F
2023 Skeletal dysplasia-
causing TRPV4 mutations
suppress the hypertrophic
differentiation of human
iPSC- derived chondrocytes
https://www. ncbi.
nlm. nih. gov/ geo/
query/ acc. cgi? acc=
GSE225446
NCBI Gene Expression
Omnibus, GSE225446

Research article Stem Cells and Regenerative Medicine
Dicks etal. eLife 2023;12:e71154. DOI: https://doi.org/10.7554/eLife.71154 24 of 34
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Appendix 1
Appendix 1—key resources table
Reagent type
(species) or
resource Designation
Source or
reference Identifiers Additional information
Gene (Homo
sapien)
TPRV4; Transient Receptor
Potential Cation Channel
Subfamily V Member 4 HGNC Symbol
HGNC:18083;
ENSEMBL:ENSG00000111199
Gene (Homo
sapien)
SOX9; SRY- box transcription
factor 9 HGNC Symbol
HGNC:11204;
ENSEMBL:ENSG00000125398
Gene (Homo
sapien)
RUNX2; RUNX family
transcription factor 2 HGNC Symbol
HGNC:10472;
ENSEMBL:ENSG00000124813
Gene (Homo
sapien)FST; follistatin HGNC Symbol
HGNC:3971;
ENSEMBL:ENSG00000134363
Gene (Homo
sapien)ACAN; aggrecan HGNC Symbol
HGNC:319;
ENSEMBL:ENSG00000157766
Gene (Homo
sapien)
COL2A1; collagen type II alpha
1 chain
HGNC Symbol
HGNC:2200;
ENSEMBL:ENSG00000139219
Gene (Homo
sapien)
S100B; S100 calcium- binding
protein B HGNC Symbol
HGNC:10500;
ENSEMBL:ENSG00000160307
Gene (Homo
sapien)
COL1A1; collagen type I alpha
1 chain HGNC Symbol
HGNC:2197;
ENSEMBL:ENSG00000108821
Gene (Homo
sapien)
COL10A1; collagen type X alpha
1 chain HGNC Symbol
HGNC:2185;
ENSEMBL:ENSG00000123500
Gene (Homo
sapien)
ALPL; alkaline phosphatase,
biomineralization associated HGNC Symbol
HGNC:438;
ENSEMBL:ENSG00000162551
Gene (Homo
sapien)
IHH; Indian hedgehog signaling
molecule HGNC Symbol
HGNC:5956;
ENSEMBL:ENSG00000163501
Gene (Homo
sapien)
GSTA1; glutathione S- transferase
alpha 1 HGNC Symbol
HGNC:4626;
ENSEMBL:ENSG00000243955
Gene (Homo
sapien)AMELX; amelogenin X- linked HGNC Symbol
HGNC:461;
ENSEMBL:ENSG00000125363
Gene (Homo
sapien)
IFITM5; interferon induced
transmembrane protein 5 HGNC Symbol
HGNC:16644;
ENSEMBL:ENSG00000206013
Gene (Homo
sapien)
IBSP; integrin- binding
sialoprotein HGNC Symbol
HGNC:5341;
ENSEMBL:ENSG00000029559
Gene (Homo
sapien)
MEPE; matrix extracellular
phosphoglycoprotein HGNC Symbol
HGNC:13361;
ENSEMBL:ENSG00000152595
Cell line
(Homo sapien) BJFF.6; BJFF
Washington
University
Genome
Engineering and
iPSC Center RRID:CVCL_VU02
Induced pluripotent stem cell derived
from foreskin broblast
Cell line
(Homo sapien) V620I This paper
Washington University Genome
Engineering and iPSC Center; CRISPR-
edited BJFF.6 with V620I TRPV4 mutation
Cell line
(Homo sapien) T89I This paper
Washington University Genome
Engineering and iPSC Center; CRISPR-
edited BJFF.6 with T89I TRPV4 mutation
Antibody
Human Alkaline Phosphatase/
ALPL Antibody; Anti- ALPL
(mouse monoclonal) R&D Systems Cat #: MAB29092; RRID:AB_2924405 WB (1:3000)
Antibody
Anti- Collagen I antibody; Anti-
COL1A1 (mouse monoclonal) Abcam Cat #: ab90395; RRID:AB_2049527 IHC P (1:800); pepsin retrieval (5min, RT)
Antibody
Collagen type II: Anti- COL2A1
(mouse monoclonal)
Iowa Hybridoma
Bank Cat #: II- II6B3- s; RRID:AB_528165
IHC P (1:10); proteinase k retrieval (3min,
37°C)
Antibody
Collagen Type VI antibody; Anti-
COL6A1 (rabbit polyclonal)
Fitzgerald
Industries Cat #: 70F- CR009X; RRID:AB_1283876
IHC P (1:1000); proteinase k retrieval
(3min, 37°C)
Appendix 1 Continued on next page

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Reagent type
(species) or
resource Designation
Source or
reference Identifiers Additional information
Antibody
Monoclonal Anti- Collagen,
Type X antibody produced in
mouse; Anti- COL10A1 (mouse
monoclonal) Millipore Sigma Cat #: C7974; RRID:AB_259075 IHC P (1:200); pepsin retrieval (5min, RT)
Antibody
Collagen X Polyclonal Antibody;
anti- COL10A1 (rabbit polyclonal)
Thermo Fisher
Scientic Cat #: PA5- 97603; RRID:AB_2812218 WB (1:500)
Antibody
GAPDH Monoclonal
antibody; anti- GAPDH (mouse
monoclonal) Proteintech Cat #: 60004- 1- Ig; RRID:AB_2107436 WB (1:30,000)
Antibody
IHH Monoclonal Antibody
(363CT4.1.6); Anti- IHH (mouse
monoclonal)
Thermo Fisher
Scientic Cat #: MA5- 37541; RRID:AB_2897471 WB (1:500)
Antibody
MMP13 Monoclonal Antibody
(VIIIA2); Anti- MMP13 (mouse
monoclonal)
Thermo Fisher
Scientic Cat #: MA5- 14238; RRID:AB_10981616 WB (1:2000)
Antibody
RUNX2 Monoclonal Antibody
(ZR002); Anti- RUNX2 (mouse
monoclonal)
Thermo Fisher
Scientic Cat #: 41- 1400 RRID: AB_2533497 WB (1:2000)
Antibody
Anti- mouse IgG, HRP- linked
antibody; horse anti- mouse
IgG secondary antibody (horse
polyclonal) Cell Signaling Cat #: 7076; RRID:AB_330924 WB (1:30,000)
Antibody
Goat Anti- Mouse IgG H&L
(Biotin); Goat anti- mouse
antibody (goat polyclonal) Abcam Cat #: ab97021; RRID:AB_10679674 IHC (1:500)
Antibody
Goat Anti- Rabbit IgG H&L
(Biotin); Goat anti- rabbit
antibody (goat polyclonal) Abcam Cat #: ab6720; RRID:AB_954902 IHC (1:500)
Sequence-
based reagent ACAN_F
Huynh etal.,
2020 PCR primers CACTTCTGAGTTCGTGGAGG
Sequence-
based reagent ACAN_R
Huynh etal.,
2020 PCR primers ACTGGACTCAAAAAGCTGGG
Sequence-
based reagent COL1A1_F
Adkar etal.,
2019 PCR primers TGTTCAGCTTTGTGGACCTC
Sequence-
based reagent COL1A1_R
Adkar etal.,
2019 PCR primers TTCTGTACGCAGGTGATTGG
Sequence-
based reagent COL2A1_F
Adkar etal.,
2019 PCR primers GGCAATAGCAGGTTCACGTA
Sequence-
based reagent COL2A1_R
Adkar etal.,
2019 PCR primers CTCGATAACAGTCTTGCCCC
Sequence-
based reagent COL10A1_F
Adkar etal.,
2019 PCR primers CATA AAAG GCCC ACTA CCCAAC
Sequence-
based reagent COL10A1_R
Adkar etal.,
2019 PCR primers ACCT TGCT CTCC TCTT ACTGC
Sequence-
based reagent FST_F Ohta etal., 2015 PCR primers TGTGCCCTGACAGTAAGTCG
Sequence-
based reagent FST_R Ohta etal., 2015 PCR primers GTCTTCCGAAATGGAGTTGC
Sequence-
based reagent S100B_F Dix etal., 2016 PCR primers AGGGAGGGAGACAAGCACAA
Sequence-
based reagent S100B_R Dix etal., 2016 PCR primers ACTCGTGGCAGGCAGTAGTA
Sequence-
based reagent SOX9_F Loh etal., 2016 PCR primers CGTC AACG GCTC CAGC AAGAACAA
Sequence-
based reagent SOX9_R Loh etal., 2016 PCR primers GCCG CTTC TCGC TCTC GTTC AGAAGT
Sequence-
based reagent TRPV4_F Luo etal., 2018 PCR primers AGAA CTTG GGCA TCAT CAACGAG
Appendix 1 Continued
Appendix 1 Continued on next page

Research article Stem Cells and Regenerative Medicine
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Reagent type
(species) or
resource Designation
Source or
reference Identifiers Additional information
Sequence-
based reagent TRPV4_R Luo etal., 2018 PCR primers GTTC GAGT TCTT GTTC AGTTCCAC
Sequence-
based reagent TBP_F
Adkar etal.,
2019 PCR primers AACCACGGCACTGATTTTCA
Sequence-
based reagent TBP_R
Adkar etal.,
2019 PCR primers ACAGCTCCCCACCATATTCT
Peptide,
recombinant
protein Vitronectin; VTN- N
Thermo Fisher
Scientic Cat #: A14700
Peptide,
recombinant
protein Activin R&D Systems Cat #: 338- AC
Peptide,
recombinant
protein
Fibroblastic growth factor 2;
FGF2 R&D Systems Cat #: 233- FB- 025/CF
Peptide,
recombinant
protein
Bone morphogenetic protein
4; BMP4 R&D Systems Cat #: 314- BP- 010CF
Peptide,
recombinant
protein
Human transforming growth
factor-
β
3; TGF
β
3 R&D Systems Cat #: 243- B3- 010/CF
Peptide,
recombinant
protein Type II collagenase
Worthington
Biochemical Cat #: LS00417 Activity 225u/ML
Commercial
assay or kit Fluo- 4 AM
Thermo Fisher
Scientic Cat #: F14201
Commercial
assay or kit Fura Red AM
Thermo Fisher
Scientic Cat #: F3021
Commercial
assay or kit
Quant- iT PicoGreen dsDNA
Assay Kit; PicoGreen
Thermo Fisher
Scientic Cat #: P7589
Commercial
assay or kit Total RNA Purication Plus Kit Norgen Biotek Cat #: 48400
Commercial
assay or kit Fast SYBR green
Thermo Fisher
Scientic Cat #: 4385610
Commercial
assay or kit Histostain Plus Kit
Thermo Fisher
Scientic Cat #: 858943
Commercial
assay or kit AEC substrate solution Abcam Cat #: ab64252
Chemical
compound,
drug Y- 27632
STEMCELL
Technologies Cat #: 72304
Chemical
compound,
drug ReLeSR
STEMCELL
Technologies Cat #: 053263872
Chemical
compound,
drug CHIR99021 Reprocell Cat #: 04- 0004- 02
Chemical
compound,
drug SB505124 Tocris Bioscience Cat #: 3263
Chemical
compound,
drug Dorsomorphin; DM Reprocell Cat #: 04- 0024
Chemical
compound,
drug PD173074 Tocris Bioscience Cat #: 3044
Chemical
compound,
drug Wnt- C59
Cellagen
Technologies Cat #: C7641- 2s
Appendix 1 Continued
Appendix 1 Continued on next page

Research article Stem Cells and Regenerative Medicine
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Reagent type
(species) or
resource Designation
Source or
reference Identifiers Additional information
Chemical
compound,
drug Purmorphamine Reprocell Cat #: 04- 0009
Chemical
compound,
drug 1- Thioglycerol Millipore Sigma Cat #: M6145
Chemical
compound,
drug 2- Mercaptoethnol; 2- ME
Thermo Fisher
Scientic Gibco; Cat #: 21985023
Chemical
compound,
drug L- Ascorbic acid; ascorbate Millipore Sigma
Cat #: A89
60
Chemical
compound,
drug L- Proline; proline Millipore Sigma Cat #: P5607
Chemical
compound,
drug ML329 Cayman Chemical Cat #: 2248
Chemical
compound,
drug Dexamethasone; Dex Millipore Sigma Cat #: D4902
Chemical
compound,
drug GSK1016790A; GSK101 Sigma- Aldrich Cat #: G0798
Chemical
compound,
drug GSK205 AOBIOUS Cat #: AOB1612 1263130- 79- 5
Chemical
compound,
drug Sulnpyrazone Sigma- Aldrich Cat #: S9509- 5G
Chemical
compound,
drug
1,9- Dimethylmethylene blue;
DMMB Sigma- Aldrich Cat #: 341088
Software,
algorithm pClamp software suite Molecular Devices RRID:SCR_011323
Software,
algorithm Fiji software – ImageJ This paper RRID:SCR_002285; version 2.1.0
Used to analyze uorescence confocal
imaging of calcium signaling
Software,
algorithm MATLAB – Hertz model
Darling etal.,
2006
Used to analyze AFM data to determine
modulus
Software,
algorithm bcl2fastq llumina RRID:SCR_015058
Software,
algorithm
Ensembl release 76 primary
assembly with STAR
Dobin etal.,
2013 RRID:SCR_002344; version 2.5.1a
Software,
algorithm Subread:featureCount Liao etal., 2014 RRID:SCR_012919; version 1.4.6- p5
Software,
algorithm Salmon Patro etal., 2017 RRID:SCR_017036; version 0.8.2
Software,
algorithm RSeQC Wang etal., 2012 RRID:SCR_005275; version 2.6.2
Software,
algorithm DESeq2 R package Love etal., 2014 RRID:SCR_015687
Software,
algorithm Pheatmap R package Kolde, 2015 RRID:SCR_016418
Software,
algorithm ggplot2 R package Wickham, 2009 RRID:SCR_014601
Software,
algorithm GraphPad Prism, version 9.1
GraphPad
Software, Boston,
MA RRID:SCR_002798; version 9.1.0
Appendix 1 Continued
Appendix 1 Continued on next page

Research article Stem Cells and Regenerative Medicine
Dicks etal. eLife 2023;12:e71154. DOI: https://doi.org/10.7554/eLife.71154 33 of 34
Reagent type
(species) or
resource Designation
Source or
reference Identifiers Additional information
Software,
algorithm VennDiagram R package
Chen and
Boutros, 2011 RRID:SCR_002414
Software,
algorithm g:proler
Raudvere etal.,
2019 RRID:SCR_006809
Software,
algorithm tidyverse R package
Altman and
Krzywinski, 2017 RRID:SCR_019186
Software,
algorithm Cytoscape String
Doncheva etal.,
2019; Shannon
etal., 2003 RRID:SCR_003032
Other Essential 8 Flex Media; E8
Thermo Fisher
Scientic Gibco; Cat #: A2858501
hiPSC medium (see Materials and
methods: hiPSC culture)
Other
Iscove’s Modied Dulbecco’s
Medium, glutaMAX; IMDM
Thermo Fisher
Scientic Gibco; Cat #: 31980097
Mesodermal differentiation medium (see
Materials and methods: Mesodermal
differentiation)
Other
Ham’s F- 12 nutrient mix,
glutaMAX; F12
Thermo Fisher
Scientic Gibco; Cat #: 31765092
Mesodermal differentiation medium (see
Materials and methods: Mesodermal
differentiation)
Other Penicillin–streptomycin; P/S
Thermo Fisher
Scientic Gibco; Cat #: 15140122
Mesodermal and chondrogenic
differentiation medium supplement (see
Materials and methods: Mesodermal
differentiation, Chondrogenic
differentiation with 3D pellet culture)
Other
Insulin–Transferrin–Selenium;
ITS+
Thermo Fisher
Scientic Gibco; Cat #: 41400045
Mesodermal and chondrogenic
differentiation medium supplement (see
Materials and methods: Mesodermal
differentiation, Chondrogenic
differentiation with 3D pellet culture)
Other
Chemically dened concentrated
lipids
Thermo Fisher
Scientic Cat #: 11905031
Mesodermal differentiation medium
supplement (see Materials and methods:
Mesodermal differentiation)
Other
Dulbecco’s Modied Eagle
Medium/F12, glutaMAX; DMEM/
F12
Thermo Fisher
Scientic Cat #: 10565042
Chondrogenic differentiation
medium (see Materials and methods:
Chondrogenic differentiation with 3D
pellet culture)
Other
Modied Eagle Medium (MEM)
with nonessential amino acids;
NEAA
Thermo Fisher
Scientic Gibco; Cat #: 11140050
Chondrogenic differentiation medium
supplement (see Materials and methods:
Chondrogenic differentiation with 3D
pellet culture)
Other Fetal bovine serum; FBS Atlanta Biologicals Cat #: S11550
Neutralization medium (see Materials
and methods: Chondrogenic
differentiation with 3D pellet culture)
Other
Axopatch 1D patch- clamp
amplier and digitized with
Digidata 1320 digitizer Molecular Devices
Patch clamping equipment (see
Materials and methods: Patch clamping)
Other Soda lime glass Kimble Chase Cat #: 2502
Patch clamping equipment (see
Materials and methods: Patch clamping)
Other Sutter P- 86 puller Sutter Instruments
Patch clamping equipment (see
Materials and methods: Patch clamping)
Other HEPES
Thermo Fisher
Scientic Gibco; Cat #: 15630130
Calcium signaling medium (see Materials
and methods: TRPV4 agonists and
antagonists, Patch clamping)
Other Confocal microscope Zeiss LSM 880
Calcium signaling equipment (see
Materials and methods: Confocal
imaging of Ca2+ signaling)
Other
Optimal cutting temperature;
OCT Sakura Finetek Cat #: 4583
AFM materials (see Materials and
methods: AFM measurement of
neocartilage mechanical properties)
Other Cryolm Section- Lab Type: 2C(10)
AFM materials (see Materials and
methods: AFM measurement of
neocartilage mechanical properties)
Appendix 1 Continued
Appendix 1 Continued on next page

Research article Stem Cells and Regenerative Medicine
Dicks etal. eLife 2023;12:e71154. DOI: https://doi.org/10.7554/eLife.71154 34 of 34
Reagent type
(species) or
resource Designation
Source or
reference Identifiers Additional information
Other Atomic force microscopy; AFM Asylum Research Cat #: MFP- 3D Bio
AFM equipment (see Materials and
methods: AFM measurement of
neocartilage mechanical properties)
Other
Silicon cantilever with a spherical
tip
Novascan
Technologies
5μm diameter, k ~ 7.83N/m; AFM
materials (see Materials and methods:
AFM measurement of neocartilage
mechanical properties)
Other RIPA buffer
Cell Signaling
Technology Cat #: 9806S
Western blot materials (see Materials
andmethods: Western blot)
Other Protease inhibitor
Thermo Fisher
Scientic Cat #: 87786
Western blot materials (see Materials
and methods: Western blot)
Other
TidyBlot Western Blot Detection
Reagent:HRP; TidyBlot- Reagent-
HRP Bio- Rad Cat #: STAR209
1:1000; Western blot materials (see
Materials and methods: Western blot)
Other
10% sodium dodecyl
sulfate–polyacrylamide gel
electrophoresis gel with pre-
stained molecular weight
markers Bio- Rad Cat #: 161- 0374
Western blot materials (see Materials
and methods: Western blot)
Other iBright FL1000 Imaging System
Thermo Fisher
Scientic
Western blot equipment (see Materials
and methods: Western blot)
Other DNase Norgen Biotek Cat #: 25720
RNA sequencing materials (see Materials
and methods: Genome- wide mRNA
sequencing)
Other
RNA Clean- Up and
Concentration Kit Norgen Biotek Cat #: 43200
RNA sequencing materials (see Materials
and methods: Genome- wide mRNA
sequencing)
Other NovaSeq 6000 Illumina
RNA sequencing equipment (see
Materials and methods: Genome- wide
mRNA sequencing)
Other Safranin- O solution; Saf- O Millipore Sigma Cat #: HT904
Histology materials (see Materials and
methods: Histology)
Other
Harris hematoxylin with glacial
acetic acid; hematoxylin Poly Scientic Cat #: 212A16OZ
Histology materials (see Materials and
methods: Histology)
Other
Vector hematoxilyn QS
counterstain
Vector
Laboratories Cat #: H- 3404
Histology materials (see Materials and
methods: Histology)
Appendix 1 Continued