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Characteristic ERK1/2 signaling dynamics distinguishes necroptosis from apoptosis

September 2021
iScience 24(9):103074

DOI:10.1016/j.isci.2021.103074

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Authors:

Benjamin Cappe

Aymeric Leray

Laboratoire Interdisciplinaire Carnot de Bourgogne

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ERK1/2 involvement in cell death remains unclear, although many studies have demonstrated the importance of ERK1/2 dynamics in determining cellular responses. To untangle how ERK1/2 contributes to two cell death programs, we investigated ERK1/2 signaling dynamics during hFasL-induced apoptosis and TNF-induced necroptosis in L929 cells. We observed that ERK1/2 inhibition sensitizes cells to apoptosis while delaying necroptosis. By monitoring ERK1/2 activity by live-cell imaging using an improved ERK1/2 biosensor (EKAR4.0), we reported differential ERK1/2 signaling dynamics between cell survival, apoptosis, and necroptosis. We also decrypted a temporally shifted amplitude- and frequency-modulated (AM/FM) ERK1/2 activity profile in necroptosis versus apoptosis. ERK1/2 inhibition, which disrupted ERK1/2 signaling dynamics, prevented TNF and IL-6 gene expression increase during TNF-induced necroptosis. Using an inducible cell line for activated MLKL, the final executioner of necroptosis, we showed ERK1/2 and its distinctive necroptotic ERK1/2 activity dynamics to be positioned downstream of MLKL.

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iScience

Article

Characteristic ERK1/2 signaling dynamics

distinguishes necroptosis from apoptosis

Franc¸ois Sipieter,

Benjamin Cappe,

Aymeric Leray, ...,

Pierre Vincent,

Peter

Vandenabeele,

Franck B. Riquet

franck.riquet@irc.vib-ugent.be

Highlights

ERK inhibition sensitizes

apoptosis but delays

necroptosis in L929 cells

Temporal dynamics of

ERK activity distinguishes

survival, apoptosis, and

necroptosis

Disrupting ERK activity

preventsTNFandIL6

gene expression in

necroptotic L929

Expression of pro-

necroptotic MLKL induces

ERK activity dynamics in

L929

Sipieter et al., iScience 24,

103074

September 24, 2021 ª2021

The Author(s).

https://doi.org/10.1016/

j.isci.2021.103074

OPEN ACCESS

iScience

Article

Characteristic ERK1/2 signaling dynamics

distinguishes necroptosis from apoptosis

Franc¸ ois Sipieter,

1,2,3,10,12

Benjamin Cappe,

1,2,12

Aymeric Leray,

Elke De Schutter,

1,2,5

Jolien Bridelance,

1,2

Paco Hulpiau,

7,11

Guy Van Camp,

5,6

Wim Declercq,

1,2

Laurent He

´liot,

Pierre Vincent,

Peter Vandenabeele,

1,2

and Franck B. Riquet

1,2,3,13,

SUMMARY

ERK1/2 involvement in cell death remains unclear, although many studies have

demonstrated the importance of ERK1/2 dynamics in determining cellular re-

sponses. To untangle how ERK1/2 contributes to two cell death programs, we

investigated ERK1/2 signaling dynamics during hFasL-induced apoptosis and

TNF-induced necroptosis in L929 cells. We observed that ERK1/2 inhibition sensi-

tizes cells to apoptosis while delaying necroptosis. By monitoring ERK1/2 activity

by live-cell imaging using an improved ERK1/2 biosensor (EKAR4.0), we reported

differential ERK1/2 signaling dynamics between cell survival, apoptosis, and nec-

roptosis. We also decrypted a temporally shifted amplitude- and frequency-modu-

lated (AM/FM) ERK1/2 activity proﬁle in necroptosis versus apoptosis. ERK1/2

inhibition, which disrupted ERK1/2 signaling dynamics, prevented TNF and IL-6

gene expression increase during TNF-induced necroptosis. Using an inducible

cell line for activated MLKL, the ﬁnal executioner of necroptosis, we showed

ERK1/2 and its distinctive necroptotic ERK1/2 activity dynamics to be positioned

downstream of MLKL.

INTRODUCTION

Biological cues are processed by the cell through spatial and temporal biochemical signaling dynamics

of signaling networks. The encrypted information is then decoded to elicit cellular processes. An

increasing trend in cell biology reveals an additional layer of complexity in cell signaling where the

cell encrypts information based on the temporal modulation of signaling activities (Levine et al., 2013;

Purvis and Lahav, 2013). This signal transmission modus resides in several features such as pulse ampli-

tude, frequency, duration, and onset. The extracellular signal-regulated protein kinases 1 and 2 (ERK1/2)

have been early examples of how signaling dynamics features drive cellular responses, such as prolifer-

ation and differentiation (Albeck et al., 2013;Aoki et al., 2013;Johnson and Toettcher, 2019;Ryu et al.,

2016). While there is compelling evidence of ERK1/2 involvement in different cell death modalities, ERK1/

2 controversial effects made its understanding in cell death daunting. Thus, our study seeks to clarify the

complex state of the art around ERK1/2 in cell death by investigating its temporal modulation of activity

in two different cell death processes. Using an improved version of the ERK1/2 Kinase Activity Reporter

(EKAR4.0), we investigated at the single-cell level how ERK1/2 signaling dynamics are encoded during

apoptosis and necroptosis.

Besides ERK1/2 roles in various cell survival processes (e.g., cell cycle progression, proliferation, differen-

tiation) (Kohno and Pouyssegur, 2006;Wortzel and Seger, 2011), its involvement in different cell death mo-

dalities remains obscure. ERK1/2 activation was reported to be involved in different cell death programs,

such as apoptosis, necroptosis, autophagy, and ferroptosis (Cagnol and Chambard, 2010;Yagoda et al.,

2007;Zhuang and Schnellmann, 2006), either as a pro-death or a pro-survival signaling effector. In tumors,

ERK1/2 was shown to promote the expression of anti-apoptotic proteins while repressing pro-apoptotic

proteins, contributing to cell survival and tumorigenesis (Balmanno and Cook, 2009). In addition, Degterev

et al. demonstrated that effectors of necroptosis, in this instance Receptor Interacting Protein Kinase 1 and

-3 (RIPK1 and RIPK3), could directly promote inﬂammatory gene expression independently of lipopolysac-

charides (LPS)-induced necroptosis in bone marrow-derived macrophage (BMDM) cells, and this process is

mediated in part by ERK1/2 (Najjar et al., 2016). Besides, an increasing number of studies have reported

Molecular Signaling and Cell

Death Unit, Department of

Biomedical Molecular

Biology, Ghent University,

Ghent, Belgium

Molecular Signaling and Cell

Death Unit, VIB Center for

Inﬂammation Research,

Technologiepark 71,

Zwijnaarde, 9052 Ghent,

Belgium

Universite

´de Lille, Lille,

France

Laboratoire

Interdisciplinaire Carnot De

Bourgogne, UMR 6303

CNRS-Universite

´de

Bourgogne Franche-Comte

´,

21078 Dijon, France

Center of Medical Genetics,

University of Antwerp and

Antwerp University Hospital,

Prins Boudewijnlaan 43/6,

Edegem, 2650 Antwerp,

Belgium

Center for Oncological

Research, University of

Antwerp and Antwerp

University Hospital,

Universiteitsplein 1, Wilrijk,

2610 Antwerp, Belgium

Data Mining and Modeling

for Biomedicine (DaMBi), VIB

Center for Inﬂammation

Research, Ghent, Belgium

Team Biophotonique

Cellulaire Fonctionnelle,

Laboratoire de Physique des

Lasers, Atomes et Mole

´cules

(PhLAM), CNRS UMR 8523,

59655 Villeneuve d’Ascq,

France

Sorbonne Universite

´, CNRS,

Neurobiology of Adaptative

Processes, UMR8256, 75005

Paris, France

Present address: Institut

Jacques Monod, CNRS UMR

7592, Universite

´Paris

Diderot, 75205 Paris, France

Present address: Howest

University of Applied

Sciences, Campus

Rijselstraat, 8200 Brugge,

Belgium

Continued

iScience 24, 103074, September 24, 2021 ª2021 The Author(s).

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

OPEN ACCESS

pro-death functions of ERK1/2 activity in apoptosis, autophagy, and senescence depending on cell type

and cell stimulation intensity [for review (Cagnol and Chambard, 2010)].

Tumor Necrosis Factor (TNF)-induced necroptosis relies on the serine-threonine kinases RIPK1 and RIPK3

(Vandenabeele et al., 2010) and the pseudo kinase Mixed-Lineage Kinase Like (MLKL) (Sun et al., 2012).

Nowadays, necroptosis is deﬁned as a RIPK1/RIPK3/MLKL-mediated cell death program (Pasparakis and

Vandenabeele, 2015). Inappropriate necroptosis is linked to ischemia–reperfusion-induced injury in the

brain, kidney, and heart (Oerlemans et al., 2012), inﬂammatory (Duprez et al., 2011;Takahashi et al.,

2012,2014), or degenerative disorders (Ofengeim et al., 2015), and tumor metastasis (Chen and Mellman,

2017). Necroptosis promotes inﬂammation by activating proinﬂammatory cytokine gene expression and

releasing immunogenic molecules (Murai et al., 2018;Wallach et al., 2011;Zhu et al., 2018). Building up

such a cellular stress response profoundly impacts the outcome of a pathological situation (Fulda et al.,

2010). Therefore, it is crucial to identify molecular effectors that speciﬁcally modulate the signaling towards

necroptosis (Vanlangenakker et al., 2010;Wallach et al., 2016).

While conﬂicting ﬁndings on ERK1/2 activity during necroptosis in L929 cells were reported (Cho et al.,

2011;Vanlangenakker et al., 2010), we decided to mainly use the murine ﬁbrosarcoma L929 cellular subline

(L929sAhFas) since it is TNF-sensitive and stably expresses the human pro-apoptotic receptor Fas. Treat-

ment of L929sAhFas (hereafter referred to as L929) with hFas ligand (hFasL) or TNF will speciﬁcally induce

apoptosis via the caspase-8-dependent proteolytic pathway or RIPK1-dependent necroptosis, respectively

(Vercammen et al., 1998). Here, we report that direct ERK1/2 inhibition while sensitizing hFasL-induced

apoptosis delays TNF-induced necroptosis in L929 cells. Our results suggest that ERK1/2 is involved, in

a RIPK1-kinase activity-independent manner, in necroptosis-activated cell-autonomous functions via the

increase of pro-inﬂammatory cytokines gene expression. By ﬂuorescent imaging using our optimized

ERK1/2 Kinase Activity Reporter 4.0 (EKAR4.0), we revealed distinct amplitude- and frequency-modulated

(AM/FM) ERK1/2 activity signaling dynamics in L929 depending on the triggered cellular process: survival,

hFasL-induced apoptosis, or TNF-induced necroptosis. We propose a model in which the early onset of

AM/FM ERK1/2 activity dynamics mediates the proinﬂammatory cytokine gene expression increase in

TNF-induced necroptosis in L929 cells. Finally, using genetic models of RIPK1- and RIPK1/3-independent

necroptosis, the results of this study propose ERK1/2 and its distinctive necroptotic ERK1/2 activity

signaling dynamics to be positioned downstream of MLKL.

RESULTS

ERK1/2 inhibition delays TNF-induced necroptosis but sensitizes hFasL-induced apoptosis

First, we investigated the impact of MAPK/ERK1/2 signaling pathway inhibition in apoptosis and necrop-

tosis and opted for a systematic inhibition of different MAPK/ERK1/2 cascade layers using different chem-

ical inhibitors in L929 cells (Figure S1A).

In TNF-induced necroptosis, direct inhibition of ERK1/2 (SCH772984) (Morris et al., 2013)and90kDaribo-

somal S6 kinase (RSK) (BI-D1870) signiﬁcantly delayed necroptosis (Figure 1A) in a dose-dependent manner

for ERK1/2 inhibition (Figure 1B). Similar trends were observed in the presence of a pan-caspase inhibitor

(TNF + zVAD) (Figure 1C) but not as pronounced when compared to TNF alone (Figure S1Evs. Figure 1A),

suggesting that in necroptosis sensitizing conditions due to caspase-8 inhibition, the contribution of ERK1/

2 is less outspoken. There was still a dose-dependent effect of ERK1/2 inhibition in the presence of zVAD,

albeit not as big as in Figure 1B. Conversely, mitogen-activated protein kinase kinase (MEK1/2), ERK1/2,

and RSK inhibition potently sensitized hFasL-induced apoptosis (Figure S1F), increasing cell death onset.

In hFasL-induced apoptosis, direct ERK1/2 inhibition also sped up the process in a dose-dependent

manner (Figure 1D).

In TNF-induced necroptosis, pretreatment with a Raf inhibitor (AZ628) or two different MEK1/2 inhibitors

(PD184352 and U0126) had no signiﬁcant effect (Figure 1A). Of note, no cytotoxic effect was seen during

ERK1/2 and RSK inhibition in the absence or presence of the pan-caspase inhibitor zVAD (Figures S1C

and S1D). As a control, pretreatment of L929 with Nec1S, a speciﬁc inhibitor of RIPK1 kinase activity (Deg-

terev et al., 2008), completely blocked RIPK1 kinase-dependent TNF-induced necroptosis (Figures 1Aand

1E). Also, in all inhibitory conditions, no switch to apoptosis was observed as determined by the absence of

caspase enzymatic activity (Figures 1A–1C, right panels) while the phosphorylation of MLKL wasstill detect-

able, conﬁrming the occurrence of necroptosis (Figure 1E). Finally, we conﬁrmed by western blot in L929

These authors contributed

equally

Lead contact

*Correspondence:

franck.riquet@irc.vib-ugent.

https://doi.org/10.1016/j.isci.

2021.103074

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2iScience 24, 103074, September 24, 2021

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Figure 1. Effect of direct ERK1/2 inhibition in TNF-induced necroptosis and hFasL-induced apoptosis

(A) L929 wildtype cells were pretreated for 1 h with the indicated compounds, all targeting the MAPK/ERK signaling pathway (Figure S1A) or RIPK1 activity

(grey curves) before TNF (red) or control (blue) treatment.

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iScience 24, 103074, September 24, 2021 3

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the level-by-level inhibition of the MAPK/ERK1&2 pathway using the different chemical inhibitors used in

this study (Figure S1G).

While our ﬁndings concerning U0126 were consistent with an earlier study from our laboratory (Vanlange-

nakker et al., 2010), they were discrepant with another report showing marked protection of TNF-induced

necroptosis in both L929 and Jurkat cells in the presence of 50 mM U0126 (Cho et al., 2011). To clarify this

situation, we stimulated L929 cells with TNF in the presence of increasing concentrations of U0126 (10, 20,

and 50 mM). Our data indicated that MEK1/2 inhibitor U0126 protects against TNF-induced necroptosis in a

concentration-dependent manner and with a potent and signiﬁcant inhibition at high concentration

(50 mM, 2-fold reduction), as previously shown (Cho et al., 2011)(Figure S1B). However, such a high

dose of U0126 was reported in vitro to inhibit ERK1/2 (Favata et al., 1998), which led us to use advanced

and FDA-approved MEK1/2 speciﬁc inhibitors Selumetinib and Trametinib. No signiﬁcant effect to either

TNF-induced necroptosis or hFasL-inducedapoptosis in L929 cells was observed, except for Selumetinib at

1mM, which surprisingly lightly sensitized TNF-induced necroptosis (Figure S2A). No cytotoxic effect of Se-

lumetinib and Trametinib was seen.

In the context of necroptosis, direct ERK1/2 inhibition in Jurkat FADD

/

cells recapitulated the same dose-

dependent effect on TNF-induced necroptosis seen in L929 (Figure S2B). The same effect was observed using

RSK and advanced MEK1/2 inhibitors in these experimental conditions (Figures S2B and S2C). In contrast, direct

ERK1/2 inhibition (20 mM) had no signiﬁcant impact on triggered necroptosis in HT29 cells (stimulatedby TNF +

BV6, an inhibitor of apoptosis proteins (IAP), and zVAD), while protection could be seen using Selumetinib

(Figures S2D and S2E). Moreover, necroptosis in HT29 cells was still drastically reduced upon RSK inhibition

(Figure S2D). Note that to induce necroptosis in HT29we used zVAD, a known sensitizer of necroptosis, which

explains why the inhibitory effect of ERK1/2 inhibition was not as marked similarly to what we showed in L929 in

the presence of zVAD (Figure 1C). Again, this suggests that in necroptosis sensitizing conditions, the contribu-

tion of ERK1/2 is less outspoken. In the context of apoptosis, SCH772984 protected Jurkat FADD

/

against

UV-irradiation-induced apoptosis, which engages many different apoptotic pathways besides death recep-

tor-induced apoptosis. Finally, in TNF + BV6-induced apoptosis in HT29 while MEK1/2 inhibition had no

impact, a sensitizing effect was observed in the presence of ERK1/2 inhibitor(Figure S2D) and to a lesser extent

upon RSK inhibition, but overall in line with our ﬁndings in L929 cells. Altogether, our ﬁndings show a dual role

for ERK1/2 in regulated cell death modalities. While ERK1/2 functions as a pro-death modulator in TNF-induced

necroptosis, it mainly displays a pro-survival role in hFasL-induced apoptosis.

Speciﬁc spatio-temporal ERK1/2 activation proﬁles in TNF-induced necroptosis

Early transient and late sustained ERK1/2 phosphorylation proﬁles during necroptosis

Evaluation of ERK1/2 phosphorylation patterns during TNF-induced necroptosis in L929 cells by western

blot (WB) showed a biphasic phosphorylation proﬁle of ERK1/2 (Figure 2A). First, TNF provoked a rapid

and transient ERK1/2 phosphorylation between 5 and 15 min, followed by sustained ERK1/2 phosphoryla-

tion starting 2 h after TNF stimulation (Figures 2AandS3A) with no ERK1/2 isoform-speciﬁc contribution

(Figure S3A). Pretreatment with Nec1S did not affect the duration of the early transient phosphorylation

pattern while abolishing the late sustained ERK1/2 phosphorylation (Figure 2B). These results demonstrate

that the former is RIPK1 kinase activity-independent, while the latter may directly or indirectly be associated

with it. We did not observe any modiﬁcation of ERK1/2 or MEK1/2 phosphorylation proﬁles in serum-

induced ERK1/2 phosphorylation upon RIPK1 inhibition (Nec1S) (Figure S3B), suggesting that Nec1S is

not interfering with the MAPK/ERK1/2 pathway. Next, to test if the sustained ERK1/2 phosphorylation de-

pended on RIPK1 kinase activity, inhibitors were added 3h30 after TNF stimulation for a 30-min duration

(Figure 2C). We observed that Nec1S did not interfere with the late sustained ERK1/2 phosphorylation

whileitwassensitivetobothMEK1/2andERK1/2inhibitions(Figure 2C), indicating that RIPK1 does not

play a role directly in the late sustained ERK1/2 phosphorylation.

Figure 1. Continued

(B and C) SCH772984 dose-response in TNF-induced necroptosis, in the presence (C) or the absence (B) of the pan-caspase inhibitor zVAD-fmk (zVAD).

(D) Sensitizing effect of SCH772984 in hFas induced apoptosis. For each panel, cell death was measured as (i) function of time by SytoxGreen (SG

) positivity

(A–D, left graph), and (ii) plotted at 10 h after treatment (A–D, middle graph). Caspase-3 activity was assessed by DEVD-AMC cleavage efﬁciency (A–D, right

graph). Data are presented as mean GSEM of at least two independent experiments. Statistical signiﬁcance was determined using two-way ANOVA

followed by Tukey’s post hoc test. Signiﬁcance between samples is indicated as follows: *p < 0.05; **p< 0.01; ***p< 0.001.

(E) L929 cells were pre-treated or not with the indicated compounds or DMSO for 30 min before TNF treatment. After 6 h, cells were then lysed and

immunoblotted as indicated on the left of each blot.

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4iScience 24, 103074, September 24, 2021

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Altogether, these data show that inhibition of RIPK1 activity did not affect early ERK1/2 phosphorylation

but lowered the overall level of sustained ERK1/2 phosphorylation, likely by interfering with necroptosis

initiation.

Biphasic and compartmentalized activated ERK1/2 proﬁles during TNF-induced necroptosis

To examine the subcellular localization of activated ERK1/2, immunostainings of diphosphorylated ERK1/2

(activated dpERK1/2) and total ERK1/2 (tERK1/2) in TNF-stimulated L929 cells pretreated or not with Nec1S

Figure 2. Spatio-temporal patterns of activated ERK1/2 during TNF-induced necroptosis in L929

(A and B) L929 cells were serum-deprived (1% FCS) for 12 h and then pretreated (B) or not (A) for 30 min with Nec1S (10 mM)

and subsequently stimulated with TNF for the indicated time points. Cells were then lysed and immunoblotted as

indicated on the left of each blot.

scheme (C, upper) for immunoblotting. Corresponding molecular weights are indicated on the right of each blot.

(D) L929 cells were serum-deprived (FCS 1%) for 12h and then pretreated or not with Nec1S (10 mM) for 30 min and

subsequently stimulated with TNF. Cells were then ﬁxed and processed for immunoﬂuorescence, at the indicated time

points, with an antibody against total ERK1/2 (tERK1/2, green) and di-phosphorylated activated ERK1/2 (dpERK1/2, red)

and stained for DNA with Hoechst (blue). DMSO (vehicle) was added as control at a volume equivalent to 10 mMNec1S

treatment condition. Scale bar: 50 mm.

(E and F) Scatter plots showing levels of phosphorylated ERK1/2 (E) and subcellular distribution (concentration index of

ERK1/2 distribution between the nucleus and the cytoplasm) of total ERK1/2 (F) at the indicated time points in cells

undergoing necroptosis (DMSO, blue) or in which necroptosis was inhibited by Nec1S (green). Data are presented as

mean G95 CI of at least two independent experiments, with at least 300 single cells analyzed per condition. Statistical

signiﬁcance was determined using one-way ANOVA followed by Tukey’s post hoc test. Signiﬁcance between samples is

indicated as follows: *p< 0.05; **p< 0.01; ***p< 0.001; NS, not signiﬁcant. Note that all differences were signiﬁcant but not

indicated.

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GHI

Figure 3. Benchmarking EKAR4.0, our improved version of EKAR_EV biosensor, in living cells

(A–C) Biosensor reference experiments: HeLa cells expressing EKAR4.0 (A), EKAR-EV (B) or its mutant version, EKAR-EV

(T > A) (C) were time lapse-imaged by FRET microscopy and treated with PMA (0.5 mM) at t = 10 min and then with U0126

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were performed. At the basal level (TNF 0 min), ERK1/2 displayed a basal phosphorylation level evenly

distributed in the cell (Figures 2D–2F). Pretreatment of non-stimulated cells with Nec1S alone produced

a signiﬁcant depletion of total nuclear ERK1/2 in unstimulated conditions while attenuating the ERK1/2

phosphorylation level (Figures 2D–2F), suggesting that RIPK1 kinase activity during non-stimulated condi-

tions impacts ERK1/2 localization in the nucleus. After 10 min of TNF stimulation, phosphorylated ERK1/2

accumulated in the nucleus. One hour after TNF stimulation, ERK1/2 phosphorylation level returned to its

initial level and was mainly cytoplasmic (Figure 2F). Both observations were irrespective of Nec1S pre-treat-

ment (Figure 2F). At 4 h after TNF stimulation, ERK1/2 overall phosphorylation level increased and phos-

phorylated ERK1/2 remained mainly cytosolic. In contrast, Nec1S pretreat ment at this time point prevented

TNF-induced ERK1/2 phosphorylation level and produced an ERK1/2 localization pattern similar to that of

the non-stimulated control condition (TNF 0 min). The latter observation could be expected since cells

cannot undergo necroptosis in the presence of Nec1S and are essentially in a survival modus.

Together, our data show a biphasic ERK1/2 activation proﬁle in TNF-induced necroptosis, with an early

transient ERK1/2 activation wave yielding an ERK1/2 nuclear accumulation followed by a late sustained

ERK1/2 activation proﬁle mainly cytoplasmic. While both were shown to be RIPK1 independent, our results

favor the late cytoplasmic signal to depend on the necroptosis process.

EKAR4.0, an improved FRET reporter for ERK1/2 activity detection

Improving KAR engineering and biosensor reversibility by MultiSite Gateway 4.0 cloning

Testing two of the various versions of ERK1/2 FRET biosensors, EKAR-EV (Komatsu et al., 2011)and

EKAR-EV-TVV (Vandame et al., 2014) in HeLa and L929 cells (Figures 3BandS4A), we observed that

the ratio decrease reported by these biosensors upon ERK1/2 inhibition was relatively slow, and there-

fore poorly efﬁcient in reporting ERK1/2 activity decrease (i.e., biosensor dephosphorylation), as already

mentioned in the literature (Regot et al., 2014). We reasoned that the obligated linker composition

required for KAR engineering (Table S1) resulting from the recombination cloning method (MultiSite

Gateway Technology) (Jones et al., 2014) might alleviate this shortcoming by adding ﬂexibility. We ﬁrst

evaluated how such modiﬁcations, using a four-molecular brick design (Figure S4B), would affect KAR

performance. This approach was ﬁrst tested with PKA biosensors of the AKAR family, using AKAR3-EV

as a template (Komatsu et al., 2011). We constructed ‘AKAR3-EV GW4.0’ version that we named

AKAR4.0 and compared them together and against the mutant version of AKAR3-EV (AKAR-EV(T > A))

(Figures S5A–S5I). We found AKAR4.0 to report almost three times faster than AKAR-EV on the decrease

in PKA activity. Taken together, our data indicate that the obligated linker modiﬁcations during complete

Figure 3. Continued

(20 mM) at t = 34 min. Top of each panel: Schematic representation of ERK1/2 Kinase Activity Reporter (EKAR)

construct used. The small red rectangles in (A) map the positions of obligated attB linkers between each FRET

biosensor domain. For the substrate peptide sequence, red letters indicate the phosphorylation site, blue letters

indicate amino-acid substitutions to increase the WW domain’s afﬁnity, and green letters indicate ERK1/2 docking

site. Bottom left of each panel: Pseudo-color FRET/CFP ratio images of ERK1/2 activity at the steady-state (t = 0 min),

upon PMA addition (t = 20 min) and U0126 addition (t = 40 min) are shown. Right of each panel: Graph presenting the

FRET/CFP ratio values of every single cell normalized to the averaged baseline FRET/CFP ratio value throughout

reference experiments. All individual cells (grey curves), as well as the mean (red curve) and the 25th and 75th

percentile range (blue curves) from at least 30 cells, are plotted as a function of time. CV

bas

,CV

act

,andCV

inh

indicate

the coefﬁcient of variation for cells at the baseline, cells in ERK1/2 activated state, and cells in the ERK1/2 inhibited

state, respectively. Imaging parameters were kept identical for the different biosensors tested. Cells were imaged for

a total duration of 70 min at the rate of 1 acquisition every 2 min.

(D–I) Characterization and comparison of different EKAR FRET biosensors responses were performed based on the

following parameters: ratio changes (Rmin, Rmax) (D), gain (E), basal FRET/CFP value without any stimulation (F), and

ﬁtted curves to determine the speed of activation upon ERK1/2 activation (G, I) and the reversibility of the biosensor upon

MEK1/2 inhibition (H, I). Fitted curves were plotted based on Tau

, which is the time value representing half of the

measured duration of the inhibition or activation phase. Tau

was calculated by applying a non-linear regression curve

on ration values over the complete set of individual cells during the activation or inhibition phase. The nonlinear

regression was calculated between t = 8 min and t = 24 min for the activation and between t = 36 min and t = 66 min for the

inhibition and plotted as ﬁtted curves. All calculations were performed using GraphPad PRISM 8. Data are mean GSD

from at least two independent experiments. Scale bar: 20 mm. The number of cells analyzed (n) is indicated on the lower

right corner of the graph on the right panel (A–C). Statistical signiﬁcance was determined using two-way ANOVA followed

by Tukey’s post hoc test. Signiﬁcance between samples is indicated as follows: *p< 0.05; **p< 0.01; ***p< 0.001; ns, not

signiﬁcant.

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KAR engineering via the MultiSite Gateway 4.0 cloning alleviate the slowness in reporting downregula-

tion of kinases (Regot et al., 2014).

EKAR4.0, an improved FRET reporter for enhanced monitoring of ERK1/2 activity dynamics

The same KAR toolkit was used to build ‘EKAR-EV GW4.0’ aka EKAR4.0 (Figure 3A). Compared to EKAR-EV

(Figures 3A–3C), we found EKAR4.0 to report three times faster on the decrease in ERK1/2 activity and

exhibit a 10% increase in dynamic range (Figures 3D–3I). At the time, a comparison in HeLa cells of

EKAR4.0 to others FRET biosensors for monitoring ERK1/2 activity from the same design, except EKAR3

(Sparta et al., 2015), showed EKAR4.0 to overall perform better (Figures S6A–S6G). Of note, when

compared to EKAR-EV (Komatsu et al., 2011) and t-EKAR-EV-vv (Vandame et al., 2014), EKAR4.0 also per-

formed better in L929 cells (Figure S4A).

A structural modeling approach of EKAR4.0 favors higher ﬂexibility of the biosensor

The successful optimization of the EKAR-EV may be attributed in part to the modiﬁcation of linker compo-

sition and/or length between each of the functional elements of EKAR-EV, as reported by Griesbeck et al.

(Thestrup et al., 2014). Knowing that the non-radiative energy transfer efﬁciency between ﬂuorescent pro-

teins (FPs) depends on the relative distance and orientation between the donor and acceptor FPs (Sipieter

et al., 2013), we compared EKAR-EV and EKAR4.0 molecular models by using Fusion Protein MODeller

(FPMOD) to predict their structure (Pham et al., 2007). In the context of EKAR4.0, the generated 3D models

of this ERK1/2 biosensor suggested increased ﬂexibility (Figure S6H) which led us to hypothesize that the

increase in ﬂexibility of the ‘FP1-PAABD’ polypeptide might explain the improvement of EKAR4.0 dynamic

range. Although not modeled, linker length and composition might also affect the closed conformation of

EKAR4.0, thus favoring phosphatases accessibility to the phosphorylated substrate and therefore speeding

up dephosphorylation.

Early AM/FMERK1/2 activity dynamics during TNF-induced necroptosis

Distinct temporal ERK1/2 activity dynamics in hFasL- and TNF-induced cell deaths

Based on the opposite effects of direct ERK1/2 inhibition bySCH772984 observed in hFasL- and TNF-

treated L929 cells (Figures 1A–1D), we hypothesized that ERK1/2 activity dynamics would be different be-

tween apoptosis and necroptosis in L929 cells. For this reason, we investigated using EKAR4.0 whether

ERK1/2 would present a different temporal activity signature depending on the modality of programmed

cell death. We observed isolated and stochastic bursts of ERK1/2 activity in non-stimulated cells (Figures

4Aand5B), which conﬁrmed the previously reported behavior of ERK1/2 activity in non-stimulated cells (Al-

beck et al., 2013;Aoki et al., 2013). Since these variations occurred asynchronously, the mean of ERK1/2

activity at the population level displayed a steady-state level (Figure 4A), consistent with WB analysis (Fig-

ure 2A). Direct ERK1/2 inhibition by SCH772984 reduced the ERK1/2 activity means at the lower level

(Figure S6A).

We then monitored ERK1/2 activity in L929 during cell death processes. At the population level, stimulation

with hFasL (apoptosis condition) induced a slow and progressive signiﬁcant increase in FRET/CFP ratio

starting 3 h after stimulation and reaching a plateau after 6 h (Figure 4B). Looking at the single-cell proﬁles,

almost all dying cells exhibited an increase in ERK1/2 activity to reach a plateau (Figure 4B). Of note, no

ERK1/2 activity increase was observed in cells dying rapidly (Figure 4B, ﬁrst cells). This observation is

compatible with the observed ERK1/2 protective role noticed in hFasL-induced apoptosis in L929 cells (Fig-

ure 1D), suggesting that increased ERK1/2 activity in this condition might maintain cells alive for more

extended periods following hFasL stimulation. Upon TNF stimulation, resulting in necroptosis, the

ERK1/2 activity proﬁle was temporally shifted at the population level compared to hFasL-induced

apoptosis, with a signiﬁcant increase in FRET/CFP ratio occurring earlier, 1h30 after TNF stimulation and

ramping-up for another 1h followed by an elevated plateau (Figure 4C). Conclusively, ERK1/2 activity pro-

ﬁles were readily different between TNF-induced necroptosis and hFasL-induced apoptosis in L929. How-

ever, it is unclear whether these ERK activity proﬁles are linked to a differential ERK1/2 modulating role

during necroptosis and apoptosis in L929 cells (Figures 1Band1D).

At the population level, in cells pretreated with RIPK1 inhibitor Nec1S, stimulation with TNF did not induce

sustained ERK1/2 activation beyond the initial transient burst of ERK1/2 activity (Figure 4D). These results

are consistent with those of the WB analysis (Figure 2). Besides, Nec1S pretreatment did not interfere with

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Figure 4. Differential temporal activation of ERK1/2 in TNF-induced necroptosis compared to hFasL-induced

apoptosis

(A–E) biosensing experiments: L929 expressing EKAR4.0 were treated with DMSO as a control (A), hFasL (B), TNF (C), and

Nec1S orSCH772984 (added 1h in pretreatment) + TNF (D and E, respectively). Samples were time lapse-imaged by FRET

microscopy for 30 min before treatment and 14.5 h after stimulation at the rate of 1 acquisition every 4 min. The FRET/CFP ratio

for at least 100 cells in each cellular context (survival, apoptosis, and necroptosis) is displayed using a kymographic

representation in which each row represents one cell. Cells were ordered by the duration of cell life, from top to bottom. Data

shown are from at least two independent replicates. The time scale is at the bottom of each kymograph, and the color-coded

ﬁre LUT denotes FRET ratio changes and, therefore, reﬂects the changes in ERK1/2 activation over time. Black color indicates

dead cells or missing points during the automatic analysis. Associated time-lapse images for each condition at different time

points (0, 2, 4, and 6h) are displayed. The color-coded LUT reports on both FRET ratio changes and ﬂuorescence intensity (i1 =

50; i2 = 1300). Scale bar: 20 mm. EKAR4.0 signal in each condition was then averaged. The mean (dark curve) and the SEM (grey

errors bars) are plotted as a function of time. The number of cells analyzed is reported on each graph (n).

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Figure 5. Differential patterns of EKAR4.0 pulse dynamics in TNF-induced necroptosis and hFasL-induced

apoptosis compared to survival

(A) For each condition, similarly to the FRET/CFP ratio, a binary kymographic representation displays the corresponding

EKAR4.0 pulse dynamics pattern.

(B) Automatic detection of ERK1/2 activity pulses was performed using a peak detection algorithm, and regions of

increased ERK1/2 activity are identiﬁed by red dots on one representative FRET/CFP ratio proﬁle for one cell and each

condition, plotted against time. The green line corresponds to the time of stimulation. At the bottom of each graph, a

corresponding heatmap of EKAR4.0 signal is shown with binary values (0–1 or dark blue to yellow, respectively), meaning a

shift of ERK1/2 activity between the ‘‘OFF’’ state (EKAR4.0 FRET

OFF

) to the ‘‘ON’’ state (EKAR4.0 FRET

). Black indicates

dead cells.

pulse number per time fraction, pulse duration (min), and FRET

state of ERK1/2 (calculated for each cell, normalized to

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ERK1/2 stochastic activation in cells protected from TNF-induced necroptosis (TNF + Nec1S), conﬁrming

the WB analysis (Figure S3B). Upon SCH772984 addition in TNF-treated cells, the average basal level of

ERK1/2 activity was reduced (Figure 4E), and ERK1/2 activity variations were completely inhibited. This

trend was comparable to control conditions in the presence of SCH772984 + DMEM (Figure S7A, bottom

panel). Using the mutant version of EKAR4.0, similar results were obtained upon TNF stimulation of L929

cells (Figure S7A, top panel).

Taken together, live-cell imaging with EKAR4.0 in our cellular context conﬁrmed that cells exhibited iso-

lated and stochastic pulsatile ERK1/2 activation in survival conditions. Our results show that temporally

distinct patterns of ERK1/2 activation are associated with the different stimulated cell death processes.

Indeed, we noticed a modulation of both the temporality and the amplitude of the ERK1/2 activity signal.

While an early onset of fast ramping-up of ERK1/2 activity followed by a plateau occurred in TNF-induced

necroptosis, a progressive and later ramping-up of ERK1/2 activity was recorded during hFasL-induced

apoptosis.

Increased amplitude- and frequency-modulated ERK1/2 activity dynamics during cell deaths

The frequency of ERK1/2 activity is a crucial determinant of cell fate decisions (Albeck et al., 2013;Aoki

et al., 2013,2017;Ryu et al., 2016). To resolve the distinct dynamical proﬁles for ERK1/2 activity between

hFasL-induced apoptosis and TNF-induced necroptosis, we investigated the encoding of ERK1/2 activity

dynamics. To determine whether the frequency and/or amplitude of ERK1/2 activity could be modulated

between survival and programmed cell deaths in L929 cells, we quantiﬁed ERK1/2 activity pulses at the sin-

gle-cell level with high temporal resolution from FRET oscillations data series using a robust FRET analysis

workﬂow.

To provide analog-to-digital conversion of the biological signal which is discrete in time and amplitude, we

identiﬁed ERK1/2 activity pulses (Figure 5B) using a custom-made Matlab-based semi-automated scoring

routine (see Materials & Methods section). First, we determined an experimental threshold from conditions

where theoretically few or no pulse of ERK1/2 activity should be detected: using the mutant version of

EKAR4.0 (T > A) or EKAR4.0 in the presence of ERK1/2 inhibitor in L929 (Figure S6A). This threshold was

used to generate binary kymographs of ERK1/2 activity pulses from the FRET/CFP ratio kymographs pre-

viously shown (Figures 5AandS7A).

Focusing on the pulse modulation, we observed as expected spontaneous and stochastic pulses of ERK1/2

activity in survival (Figures 5A and 5B). Analysis of the aligned proﬁles revealed a temporal shift in the onset

of ERK1/2 pulses between apoptosis and necroptosis, which can be readily seen in the representative sin-

gle-cell data (Figure 5B) and at the cell population level (Figure 5A). In contrast to survival, we found an

increase of ERK1/2 activity pulses starting 1 h 30 min after TNF-induced necroptosis and 3hafter

hFasL-induced apoptosis (Figures 5A and 5B). Not surprisingly, ERK1/2 pulses dynamics, reminiscent of

ERK1/2 burst of stochastic activity, remained unaffected upon RIPK1 activity inhibition (Figure 5A) and com-

parable to survival conditions.

Next, we extracted quantitative parameters to decrypt the encoding of ERK1/2 activity signal. Those are

automatically calculated based on the statistical analysis of ERK1/2 pulses per cell for each condition

and integrated into our FRET analysis workﬂow (manuscript in preparation). A very similar approach was

performed to characterize cell-to-cell variability in ERK1/2 pulse dynamics in cell proliferation (Albeck

et al., 2013;Aoki et al., 2013). The metrics bundle includes a fraction of time spent in the ‘‘activated’’

ERK1/2 and ‘‘deactivated’’ ERK1/2 states (EKAR

and EKAR

OFF

, respectively), pulse amplitude, pulse fre-

quency, and pulse width.

Figure 5. Continued

the cell life duration of each cell, and expressed as a percentage of time of the cell lifespan). These parameters were

calculated 1h after the start of imaging with a 2h time interval for each cell and each condition. Data are presented as

radar plots in which each spot corresponds to the mean of at least two independent experiments. Dark spots and

lines indicate the maximum for each parameter, and the min and max values are reported as follows: pulses width

(6–10 min); pulses number (4–6); pulses amplitude (1.1–1.4); FRET

(20–35%). Statistical signiﬁcance was determined

using two-way ANOVA followed by Tukey’s post hoc test. Signiﬁcance between samples is indicated as follows:

*p< 0.05; **p< 0.01; ***p< 0.001; ****p< 0.0001; ns, not signiﬁcant.

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Over the entire time span of the experiment, the analysis indicated apparent differences between cell

death programs and survival, while limited ones could be seen between hFasL-induced apoptosis and

TNF-induced necroptosis (Figure S7B). Compared to survival, both cell deathprograms increased the over-

all EKAR

fraction, peak amplitude, and peak period. Of note, only the EKAR

was statistically different

between survival, apoptosis, and necroptosis. Under Nec1S in TNF-induced necroptosis, all metrics re-

verted to values seen in survival condition. No matter the experimental condition, the ERK1/2 pulse width

remained unchanged (mean pulse width: 10 min) except for TNF sti mulation, where a slight but signiﬁcant

increase was observed compared with survival but not with apoptosis condition (Figure S7B).

A cellular threshold of basal ERK1/2 activity level was shown to determine cell fate decision between

growth arrest and death response (Hong et al., 2018). For this reason, we asked whether the basal

ERK1/2 activity level could serve a similar purpose in our context. Correlation analysis concluded on no

signiﬁcant relationship between basal FRET level and death time in both TNF-induced necroptosis and

hFasL-induced apoptosis in L929 cells (Figures S7CandS7D). So, besides the EKAR

metric, the global

quantitative analysis did not show any differences in ERK1/2 pulses dynamics between apoptosis and nec-

roptosis in L929 cells.

At the cellular level, signaling operates sequentially in a time-resolved manner with distinct phases; each

phase commanding to speciﬁc but coordinated cellular output based on the integration of external cues

with pulsatile dynamics at the heart of this temporal organization (Levine et al., 2013;Purvis and Lahav,

2013). Thus, we opted for time-resolved analysis of ERK1/2 activity pulses. We analyzed ERK1/2 activity

pulses data in time blocks of 2 h, corresponding to the maximum value for the peak period in survival con-

dition (Figure S7B). The analysis started 1 h after stimulation to avoid any effect due to the stimulation itself.

By contrast with the complete time span analysis, the time-resolved analysis reveals differences between all

the conditions (Figure 5C). At ﬁrst (time frame 1 h–3 h after stimulation), we observed an early distinction of

ERK1/2 pulsatile activity during necroptosis, while it remains similar between apoptosis and survival. The

‘‘active’’ ERK1/2 fraction was signiﬁcantly higher, as were the pulse amplitude and duration (pulse width).

In apoptosis, only the pulse amplitude slightly increased compared to survival. However, no signiﬁcant dif-

ferences were visible concerning the pulse number, indicating that pulse frequency seemed unaffected at

that stage. Later in the process, between 3 h and 5 h after stimulation, ERK1/2 pulses activity proﬁles were

similar between apoptosis and necroptosis. We measured a signiﬁcantly higher EKAR

fraction, pulse

amplitude, and pulse number compared to survival condition. Surprisingly, it was only during the following

time frame (5-7h after stimulation) that apoptosis became distinguishable from either necroptosis or sur-

vival. Indeed, while necroptosis and apoptosis shared a signiﬁcant high EKAR

fraction and pulse number

compared to survival, pulse amplitude during cell death signaling was signiﬁcantly boosted compared to

survival and became signiﬁcantly different between apoptosis and necroptosis.

To more carefully investigate ERK1/2 signaling dynamics features in a time window preceding the death

events, dead cells identiﬁed using propidium iodide staining from experimental data presented in Figure 4

were systematically backtracked in all three cellular contexts (survival, apoptosis, and necroptosis) (Figures

S8A–S8C). Of note, long-term imaging at high temporal resolution can lead to cells dying accidentally. In

WB-like representation, the light red area preceding cell death (30 min) corresponds to the disintegration

phase (DP) coinciding with a known burst of ROS production, as it was already observed and quantiﬁed in a

previous study from our lab (Vanden Berghe et al., 2010). These data do not allow to see apparent differ-

ences between cell death programs, except a global increase in basal ERK1/2 activity in cell death

programs relative to survival condition, as already shown in Figure 4.Again,aspreviouslydescribed(Fig-

ure S7B), no signiﬁcant differences were visible between apoptosis and necroptosis for the complete time

span analysis, except for pulse width (Figure S9A).

Time-resolved analysis of EKAR4.0 signal in single cells was performed in time blocks of 2 h starting 30 min

before PI positivity (Figure S8D). Overall, we showed a higher pulse amplitude (Figure S8E), pulse number

(Figure S8F), and EKAR

fraction (Figure S8G) in apoptosis and necroptosis compared to survival condi-

tion. Interestingly, we observed a clear distinction of ERK1/2 dynamics in necroptosis compared to

apoptosis in L929 cells. Comparing all temporal frames, pulse amplitude but not pulse number gradually

increased in apoptosis compared to necroptosis (Figure S8E). By contrast to apoptosis, pulse number but

not pulse amplitude gradually increased in necroptosis (Figure S8F). In addition, cell death observed in the

control condition displayed the same ERK1/2 activity proﬁle detected in apoptosis, reﬂecting the type of

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Figure 6. Fourier transform-based analysis helps crack a distinct ERK1/2 activity code in necroptosis in L929 cells

(A) Fourier analysis of the ERK1/2 oscillations extracted from raw FRET/CFP ratio data of Figure 4. For each condition

described in Figure 4, the corresponding power spectral density (PSD) for each frequency (106) is displayed by a

kymographic representation (from 1h to 15h after the beginning of imaging) in which each row represents one cell. Lower

(0.000018 Hz) and higher (0.002 Hz) frequencies are indicated in blue (left) and red (right), respectively. Color-coded LUT

denotes the amplitude of PSD (5 to +35) for each frequency.

(B) For each condition, averaged data are represented where the mean PSD amplitude is plotted against frequency.

5

Hz; Medium (M); High (H) f > 6 310

4

Hz frequencies), the mean PSD

amplitude (mean GSEM) for each biological condition is compared.

(D) Fourier analysis of the ERK1/2 oscillations extracted from FRET/CFP ratio data was then performed on three temporal

frames with a 2h time-interval for each condition: 1h–3h, 3h–5h, and 5h–7h (from left to right).

(E) For each temporal frame and for each frequency range (Low (L) f < 5 310

5

Hz;Medium(M);High(H)f>6310

4

frequencies), and the mean PSD amplitude (mean GSEM) for each biological condition is displayed. Grey color on the

kymographs.

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cell death in these cells. Altogether, ERK1/2 activity dynamics analysis centered around the time of death

conﬁrmed and reﬁned our previous ﬁnding, emphasizing ERK1/2 activity dynamics to be more Amplitude-

Modulated during hFasL-induced apoptosis and more Frequency-Modulated during TNF-induced nec-

roptosis in L929 cells.

Frequency domain analysis conﬁrms differences in ERK1/2 activity dynamics between necroptosis

and apoptosis

While this time-resolved analysis of metrics has beenapplied before (Albeck et al., 2013;Aoki et al., 2013), it

relies on data thresholding which could be considered biased. In addition, pulses analysis converted the

signal from analog to digital binary, which narrows the resolution. Yet, another argument of a limitation

of the temporal domain analysis in these circumstances is that all frequencies within the biological signal

are de facto intertwined and not revealed. The higher temporal sampling resolution of our biosensing data

and the nature of ERK1/2 signal allowed us to quantifythe oscillatory component of ERK1/2 activity through

frequency domain using Fourier analysis. Fourier analysis of such FRET biosensing data has already been

used to quantify calcium oscillations (Candeo et al., 2017). To this aim, Fourier transformation was applied

to our EKAR ratiometric data in order to generate different power spectral density (PSD) spectra(Figure 6A).

The PSD describes the strength of the variations as a function of frequen cy. In other words, it shows at which

frequency range (Hz or min

1

) variations are strong or weak (power, dB).

PSD has been represented for each cell as kymographs within the entire frequency range (Figure 6A),

together with the mean PSD Gstandard deviation curves (Figure 6B) for each condition. At the global

level, three observations can be made: (1) L929 in survival conditions (unstimulated and Nec1S + TNF)

exhibited a similar PSD proﬁle, (2) the PSD of TNF-induced necroptosis in SCH772984-pretreated L929

cells was strongly affected as expected, (3) PSD proﬁles of L929 cells stimulated with hFasL and TNF

were comparable but higher than those in survival conditions. Note, however, that PSD in cell death pro-

grams was intertwined within the medium frequencies (Figure 6B). To appreciate more precisely these

differences, we divided the entire frequency range into three main frequency bands corresponding to

three different oscillatory components: low (LF, less than 0.05 mHz), which mainly represent the back-

ground noise originating from the system, medium (MF, between 0.05 and 0.6 mHz), and high (HF,

more than 0.6 mHz) frequency bands (Figure 6B). Thus, the period of the MF oscillations ranged between

27 and 166 min and corresponded to the period range of ERK1/2 pulses registered for each condition as

presented in Figure S7B.ThemeanPSDGstandard deviation histograms within each frequency band

have been represented for each condition (Figure 6C). Results showed a signiﬁcant difference in both

theMFandHFbandsinprogrammedcelldeathconditions than survival, validating the results obtained

previously (Figures 5CandS7B).

Then, we performed a time-resolved analysis of PSD for each condition (Figures 6D–6F). Importantly, in the

time frame 1h–3h after stimulation, PSD in both the MF and HF bands was signiﬁcantly different in necrop-

tosis from that of survival and apoptosis (Figures 6E and 6F, left panels). Therefore, in this particular time

frame, we could discriminate apoptosis from necroptosis in L929 cells based on PSD analysis of frequency-

modulated ERK1/2 activity. As expected, the time frame 3h–5h of TNF- and hFasL-stimulated L929 cells

shared a similar PSD in both the MF and HF bands, signiﬁcantly different from that of survival or TNF +

Nec1S conditions. Later, in the time frame 5h–7h after stimulation, PSD results were comparable to those

from the time frame 3h–5h.

In sum, spectral decomposition analysis of ERK1/2 activity pulses supported in an unbiased way our previ-

ous observations and indicated that a speciﬁc signature of ERK1/2 activity dynamics discriminates

apoptosis from necroptosis in L929 cells, especially early on. TNF stimulation triggered early amplitude

and frequency modulation of ERK1/2 activity mainly in two freq uency bands, distinct from hFasL stimulation

showing a delayed activation.

Figure 6. Continued

(F) For each frequency range (Low (L) f < 5 310

5

Hz;Medium(M);High(H)f>6310

4

Hz frequencies), the mean PSD

amplitude (meanGSEM) for each biological condition is compared. Lower (0.00013 Hz) and higher (0.002 Hz) frequencies

are indicated in blue (left) and red (right), respectively. Statistical signiﬁcance was determined using two-way ANOVA

followed by Tukey’s post hoc test. Signiﬁcance between samples is indicated as follows: *p < 0.05; **p < 0.01; ***p <

0.001; ****p < 0.0001; ns, not signiﬁcant.

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Disruption of ERK1/2 activity dynamics prevents pro-inﬂammatory cytokine gene expression

during TNF-induced necroptosis

Several studies already proposed that temporal differences in frequency- and amplitude-modulated ERK1/

2 activity could be decoded by downstream transcription factors driving, therefore, a differential gene

expression pattern (Albeck et al., 2013;Muta et al., 2018;Wilson et al., 2017). Necroptosis can trigger

inﬂammation by inducing pro-inﬂammatory gene expression in a cell-autonomous manner (Najjar et al.,

2016;Zhu et al., 2018). Recently, Degterev et al. demonstrated in BMDM cells that this process is mediated

in part by ERK1/2 (Najjar et al., 2016). Thus, we hypothesized that ERK1/2 activity could impact pro-inﬂam-

matory cytokines gene expression proﬁles during necroptosis in L929 cells. To this aim, we characterized

the transcriptional changes in TNF-stimulated L929 cells by focusing on a small panel of genes involved

in survival, inﬂammation, and signaling. As expected from previous studies (Martens et al., 2017), mRNA

expression of nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor, alpha (IkBa)

and A20, known as regulators of inﬂammation and cell survival, were upregulated upon TNF-induced nec-

roptosis and insensitive to ERK1/2 inhibition (Figure S10A).

All pro-inﬂammatory cytokines or chemokines tested – TNF, monocyte chemoattractant protein 1 (MCP1),

chemokine (C-X-C motif) ligand 1 (Cxcl1), and interleukin-6 (IL-6) – showed an increase in mRNA expression

upon TNF-treatment. Of those, only TNF and IL-6 were signiﬁcantly sensitive to ERK1/2 inhibition 2 h after

TNF stimulation. Of note, both basal (unstimulated conditions) and TNF-stimulated gene expression were

impacted by ERK1/2 inhibition. Since our results showed the AM/FM ERK1/2 activity to be speciﬁcally up-

regulated in the time frame 1h–3h after TNF stimulation in L929 cells, we investigated the impact of ERK1/2

inhibition on TNF and IL-6 mRNA expression 4 h after TNF stimulation. Results indicated IL-6 mRNA expres-

sion to increase further, while TNF slightly decreased (Figure S10B). Next, to assess the consequence of a

blockage of the upregulated AM/FM ERK1/2 activity on the expression of those particular genes, a similar

experiment was performed except that ERK1/2 inhibition was performed 2 h after TNF treatment (within

the time frame 1h–3h after TNF stimulation) (Figure S10C). The later TNF and IL-6 mRNA levels were again

strongly reduced, similar to the ERK1/2 inhibition before TNF stimulation, indicating that the observed

transient ERK1/2 activation occurring between 5 and 15 min was not implicated in TNF and IL-6 gene

expression.

Taken together, the role of ERK1/2 activation in TNF and IL-6 gene expression was conﬁrmed since ERK1/2

inhibition strongly inhibited basal and TNF-stimulated gene expression in L929 cells. The effect of ERK1/2

inhibition in combination with TNF stimulation caused a greater decrease in TNF and IL-6 gene expression

compared to ERK1/2 inhibition alone. Similar to the reported oscillations of p38 activity regulating pro-in-

ﬂammatory gene expression (Tomida et al., 2015), we speculate that the AM/FM ERK1/2 activity dynamics

might affect TNF and IL-6 gene expression.

Necroptotic ERK1/2 activity dynamics is recapitulated in RIPK1/3 independent necroptosis

model

ERK1/2 inhibition protects against RIPK3- and MLKL-induced necroptosis

To map the position of ERK1/2 with respect to the canonical RIPK1/RIPK3/MLKL necroptotic signaling

pathway, we took advantage of genetic models in which necroptosis is induced independently of any re-

ceptor activation. These models developed in our lab were derived from either L929 or the murine colon

carcinoma CT26 cell line in which the expression of downstream necroptotic effectors are controlled by

a doxycycline (Dox) inducible system. For RIPK1-independent necroptosis, we used BALB/c mouse-derived

CT26 cells Dox-inducible RIPK3 (hereinafter referred to as CT26 mRIPK3) (Aaes et al., 2016). Concerning

RIPK1/3-independent necroptosis, L929 cells were knocked out for Mlkl gene expression and reconstituted

with mMLKL phospho-mimetic form (S345D), which expression can be triggered upon Dox treatment

yielding the following cell line L929 Mlkl

/-

Dox-inducible mMLKL(S345D)-Flag (hereinafter referred to as

L929 iMLKL(S345D)).

As previously described (Figure S1A), a systematic chemical perturbation of all layers of the MAPK/ERK1/2

cascade was performed in these cell lines in the presence of Dox (Figures 7A and S11A). Importantly, direct

ERK1/2 inhibition using SCH772984 showed a substantial and signiﬁcant decrease in RIPK1 and RIPK1/3 inde-

pendent necroptosis in CT26 mRIPK3 (Figure S11A) and L929 iMLKL(S345D) (Figure 7A), respectively. Interest-

ingly, while similar results to that of direct ERK1/2 inhibition were observed when MEK1/2 was inhibited using

PD184352, this was not the case for U0126 mediated MEK1/2 inhibition in our RIPK1-activity-independent

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Figure 7. Direct ERK1/2 inhibition efﬁciently impairs necroptosis driven by MLKL S345D overexpression in L929

cells

(A) MLKL-deﬁcient L929 reconstituted with Dox-inducible phosphomimetic S345D mutant MLKL expression were used.

These cells were pretreated for 1 h with the indicated compounds before Dox (red) or DMEM (blue) stimulation (grey

curves). Cell death was measured as a function of time by SytoxGreen (SG

) positivity (kinetic experiment, left), 18 h after

Dox stimulation (A, left histogram), and caspase-3 activity was assessed by DEVD-AMC cleavage efﬁciency (A, middle

histogram) 18 h after Dox stimulation. Drugs-induced toxicity measurements (SG

positivity) 18 h after DMEM stimulation

(A, right histogram). The cell death data are presented as mean GSEM of at least two independent experiments.

Statistical signiﬁcance was determined using two-way ANOVA followed by Tukey’s post hoc test. Signiﬁcance between

samples is indicated as follows: *p< 0.05; **p< 0.01; ***p< 0.001; ns, not signiﬁcant.

(B) Cells were pretreated or not for 1 h with the indicated compounds (10 mM) and subsequently stimulated with Dox. Cells

were then lysed and immunoblotted as indicated on the left of each blot. Corresponding molecular weights are reported

on the right of each blot. Both left and right parts came from the same blot; a cut was performed to remove non-relevant

conditions.

for 30 min before treatment (control & Dox) and 14.5 h after stimulation at the rate of 1 acquisition every 4 min. From left to

right, the raw FRET/CFP ratio of individual cells is displayed by a FRET/CFP ratio kymographic representation (C, left),

averaged raw FRET/CFP ratio (mean GSEM) as a function of time (C, right).

(D) Corresponding quantitative analyses of ERK1/2 pulse dynamics are displayed by a binary kymographic representation

of ERK

and ERK

OFF

states (D, left). As described in Figure 5,ERK1/2activityisidentiﬁedbyreddotsonone

representative FRET/CFP ratio proﬁle for each condition, plotted against time.

(E) Time-resolved quantitative analysis of EKAR4.0 pulse dynamics based on parameters identiﬁed in Figure 5 were

calculated 1h after the start of imaging with a 2-h time interval, for each cell and for each condition (blue line: survival; red:

necroptosis triggered by Dox-induced S345D MLKL expression). Temporal frame data analysis is presented as radar plots

similar to Figure 5C. Dark spots and lines indicate the maximum for each parameter, while the center indicated the

minimum. Min and max values are reported as follows: pulses width (6–15 min); pulses number (0.5–3); pulses amplitude

(1.0–1.6); FRET

(4–16%). Statistical signiﬁcance was determined using two-way ANOVA followed by Tukey’s post hoc

test. Signiﬁcance between samples is indicated as follows: *p< 0.05; **p< 0.01; ***p< 0.001; ****p< 0.0001; ns,not

signiﬁcant. Experimental and analysis protocols, as well as graphical representations, are identical to Figures 4 and 5.

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necroptosis model. Indeed, MEK1/2 inhibition by U0126 showed no signiﬁcant effect or a moderate delay of cell

death, which was reminiscent of our observations in TNF-induced necroptosis in L929 (Figure 1A and (Vanlan-

genakker et al., 2010)). These ﬁndings advocated for ERK1/2 engagement during necroptosis signaling in CT26

and L929 and downstream of activated MLKL in the latter.

Interestingly, the RSK inhibitor BI-D1870 consistently protected against necroptosis with a 2-fold and up to

a 10-fold decrease in CT26 mRIPK3 and L929 iMLKL(S345D), respectively (Figures S11Aand7A). This RSK

inhibition effect was very much comparable to the one recorded previously in TNF-induced necroptosis in

L929 cells (Figure 1A). Note that in all experimental conditions, neither caspase activation as expected nor

chemical compounds cytotoxicity was detected (Figures 7AandS11A). Also, inhibitors used at the speci-

ﬁed concentrations did not dampen the Dox-driven production of RIPK3 and MLKL (Figure 7B). As antici-

pated, RIPK1 inhibition using Nec1S did not prevent RIPK1 and RIPK1/3 independent necroptosis (Figures

7AandS11A). In sum, using our genetic models, we showed that inhibition of MEK1/2, ERK1/2, and its

downstream effector RSK amounted to signiﬁcant inhibition of both RIPK1- and RIPK1/3-independent

necroptosis.

Necroptotic ERK1/2 activity dynamics is recapitulated downstream of MLKL

Based on our previous results using genetic models of necroptosis, we hypothesized that TNF-stimulated

AM/FM ERK1/2 activity dynamics could also be observed in cells undergoing necroptosis independent of

RIPK1 and RIPK3 activities. We ﬁrst imaged L929 iMLKL(S345D) expressing EKAR4.0 to report on ERK1/2

activity without stimulation. ERK1/2 activity was maintained at basal level with the detection of isolated

and stochastic bursts of ERK1/2 activity (Figure 7C), comparable to the unperturbed condition in L929 cells

(Figure 4A), conﬁrming thereby that knocking out MLKL did not impact basal ERK1/2 activity (Yoon et al.,

2017). Interestingly, in comparison to Figure 4C, ERK1/2 activity proﬁle in necroptosis was temporally

changedwithanincreaseinFRET/CFPratiooccurringabout2hafterDoxstimulationandmainlyfollowed

by a sustained ERK1/2 activity level for the remainder of the experiment as shown by ERK1/2 activity mean

average population representation (Figure 7C). Of note, RIPK1 inhibition did not affect ERK1/2 activity dy-

namics in these experimental conditions (data not shown), reinforcing the notion that the AM/FM ERK1/2

activity in L929 cells is independent of RIPK1 activity.

Applying the metrics bundle analysis strategy over the entire time span of this experiment again indicated

apparent differences between MLKL(S345D)-driven necroptosis and survival. Importantly, we found that

the AM/FM ERK1/2 activity signature observed in TNF-induced necroptosis in L929 cells (Figure 5)was

recapitulated in Dox-stimulated L929 iMLKL(S345D) (Figures 7D, S7BandS11B). Indeed, the EKAR

frac-

tion and the frequency of ERK1/2 activity pulses almost doubled, and pulse amplitude was also raised (Fig-

ure S11B). Next, time-resolved analysis of ERK1/2 activity pulses in time blocks of 2 h revealed a distinct

ERK1/2 activity signature in Dox-stimulated cells compared to survival only in the time frame 3h–5h after

stimulation (Figure 7E). Metrics of ERK1/2 activity are similar to those observed in TNF-induced necroptosis

in L929 cells in the time frame 1h–3h, conﬁrming the previously observed temporal shift of the AM/FM

ERK1/2 activity upon Dox-induced MLKL(S345D) production. Taking together, these results in RIPK1/3 in-

dependent necroptosis in L929 iMLKL(S345D) are reminiscent of our ﬁndings in TNF-induced necroptosis

in L929 cells (Figure 5D). Therefore, these observations suggest that necroptosis is associated with a spe-

ciﬁc temporally regulated AM/FM ERK1/2 activity signature in L929 cells, which appears to be part of the

execution process, and more precisely, downstream of MLKL.

DISCUSSION

This study has identiﬁed and exempliﬁed ERK1/2 modulator function in programmed cell death. Our ﬁnd-

ings indicate that direct ERK1/2 inhibition delayed TNF-induced necroptosis while it sensitized hFasL-

induced apoptosis in L929 cells. Biochemical data revealed a RIPK1-activity independent biphasic trend

of ERK1/2 activity upon TNF stimulation at the population level, with an early nuclear transient and a

late cytoplasmic sustained ERK1/2 activity proﬁle.

Using live-cell imaging with a much-improved FRETbiosensor to monitor ERK1/2 activity, we have reported

that in TNF-induced necroptosis and hFasL-induced apoptosis, ERK1/2 activity dynamics display ampli-

tude and frequency-modulation contrasting with the ERK1/2 activity dynamics recorded in survival. Using

a robust FRET analysis workﬂow, our measurements shed light on ERK1/2 activity implication to discrimi-

nate among necroptosis and apoptosis in L929 cells both at early and late stages of cell death. By

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comparison with hFasL, TNF-stimulated cells underwent a rapid change in ERK1/2 pulsatile activity charac-

terized by modulation of pulses amplitude and duration. At later time points, ERK1/2 pulse modulation

between necroptosis and apoptosis remained comparable and could only be discriminated by a higher

amplitude modulation during apoptosis, clearly indicating an increase in signal strength. Also, time-

resolved analysis together with a spectral decomposition of ERK1/2 pulsatile activity demonstrated that

an early onset of AM- and FM- ERK1/2 activity pulses are characteristic of TNF-induced necroptosis by com-

parison to hFasL-induced apoptosis in L929 cells.

Beyond the encoding phase that links cell death stimulus to a speciﬁc ERK1/2 activity proﬁle, we also inves-

tigated the decoding phase that ties the ERK1/2 signal to a particular cellular outcome. ERK1/2 activity

recorded in TNF-induced necroptosis in L929 cells was found to be associated with an increase of pro-in-

ﬂammatory cytokines gene expression. However, only TNF and IL-6 were signiﬁcantly sensitive to ERK1/2

inhibition 2 h after TNF stimulation. Besides, using genetic models of RIPK1- and RIPK1/3-independent

necroptosis, our results advocated for an MEK/ERK/RSK axis engagement, actually downstream of acti-

vated MLKL. Moreover, we found that the uncovered AM/FM ERK1/2 activity signature was recapitulated

in a genetic model of RIPK1/3-independent necroptosis. Based on our results, we could hypothesize that a

distinct AM/FM ERK1/2 activity signature would regulate pro-inﬂammatory cytokines gene expression

downstream of activated MLKL during necroptosis in L929 cells.

Uncovering pulsatile dynamics of signaling molecules has provided unprecedented insights into the

biochemical signal encoding towards cell fate. In survival conditions, ERK1/2 activity pulses occur sponta-

neously and are mainly due to autocrine epidermal growth factor (EGF) signaling (Aoki et al., 2013,2017;

Hiratsuka et al., 2015). In apoptotic and necroptotic L929 cells, these pulses increased in duration and

decreased spacing because individual bursts in ERK1/2 activity were no longer distinguishable. While pulse

amplitude was reported in EGF-stimulated conditions (Albeck et al., 2013;Aoki et al., 2013), ERK1/2 pulse

amplitude varied over time in hFasL- and TNF-stimulated L929 cells. As far as we know, quantitative mea-

surements of amplitude-modulated ERK1/2 activity dynamics have solely been shown experimentally in the

context of MEK1/2 inhibition in a dose-dependent manner (Albeck et al., 2013).

Fourier analysis of ERK1/2 oscillations within three main frequency bands (named LF, MF, and HF) offered

the possibility to perform unbiased statistical analysis of ERK1/2 pulsatile activity. Fourier based frequency

decomposition can be cast as a relevant approach in our ﬁeld to decrypt the context-speciﬁc ERK1/2

signaling code and can be applied to other kinases. Spectral decomposition analysis of ERK1/2 kinase ac-

tivity dynamics might reveal an additional layer of biochemical signal encoding, which could help rational

pharmacological targeting of diseases associated with MAPK/ERK1/2- and other kinases aberrant signalin g.

Speciﬁcity in signal transduction is determined by the cell’s computational capacity to encode and decode

a signal and is likely applicable to most signaling pathways (Madsen and Vanhaesebroeck, 2020). Next, we

explored the functional relevance of this AM/FM ERK1/2 activity in TNF-induced necroptosis in L929 cells.

Necroptosis promotes proinﬂammatory cytokine gene expression in a cell-autonomous manner (Najjar

et al., 2016;Zhu et al., 2018) and the release of immunogenic molecules (Murai et al., 2018). Pursuing

this paradigm, we found ERK1/2 to be a transcriptional activator of pro-inﬂammatory cytokine gene expres-

sions such as TNF and IL-6. This proposed function of ERK1/2 in the context of TNF-induced necroptosis is

associated with speciﬁc ERK1/2 activity dynamics. Our results align with that of Najjar et al. exposing ERK1/

2 as a critical regulator promoting inﬂammatory gene expression in LPS-induced necroptosis in BMDM

cells (Najjar et al., 2016).

Interestingly, ERK1/2 functioned in LPS-induced necroptosis in BMDM cells as an uncoupling modulator

that directly mediated RIPK1/3 dependent inﬂammation, independently of MLKL. Could there be a corre-

lation between speciﬁc ERK1/2 pulsatile activity dynamics and pro-inﬂammatory cytokine gene expression

patterns? This has been demonstrated before by Saito et al. in 2014 reporting on the role of interleukin-1

beta (IL-1b)-stimulated p38 pulsatile activity in controlling pro-inﬂammatory cytokines gene expression

proﬁle (Tomida et al., 2015). They found the p38 pulsatile activity to control efﬁcient expression of genes

such as IL-6, interleukin 8 (IL-8), and cyclooxygenase-2 (COX-2).

Considering that AM/FM ERK1/2 activity dynamics would mediate the increase in pro-inﬂammatory cyto-

kines gene expression proﬁle in programmed cell death, what could be the pulse generator? In 2013, Aoki

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et al. showed Raf activity as a generator of ERK1/2 activity pulses in NRK-52E cells by impacting pulses

width. Here, the pulse width was signiﬁcantly impacted in both TNF- and MLKL-induced necroptosis in

L929, and at the same time, inhibition of Raf did not signiﬁcantly modulate neither necroptosis nor

hFasL-induced apoptosis in L929 cells. Despite the RIPK1 kinase activity-independent nature of AM/FM

ERK1/2 activity in TNF-induced necroptosis, but based on the fact that Najjar et al. mapped ERK1/2 down-

stream of RIPK1/RIPK3 platform upon LPS-induced necroptosis in BMDM cells (Najjar et al., 2016), we

tested RIPK1 as a potential candidate for the generation of ERK1/2 activity pulses. Nec1S alone or

Nec1S together with TNF did not affect ERK1/2 activity pulses width, excluding RIPK1 as a pulse generator.

In line with this, we found the impact of ERK1/2 inhibition recapitulated in RIPK1/3 independent necropto-

sis in L929 cells. Since the speciﬁc AM/FM ERK1/2 activity signature could be monitored in a model of

MLKL-induced necroptosis, we came to consider other potential scenarios downstream of MLKL that could

explain our ﬁndings.

MLKL depletion was shown to affect endosomal transport independently of RIPK3 (Yoon et al., 2017)andto

provoke a slowdown of TNF and TNF receptor (TNFR) degradation, and this was also true for EGF and EGF

receptor (EGFR). In both cases, this boosts either TNF or EGF signaling activities, including enhanced gene

induction. During necroptosis, phosphorylated MLKL signiﬁcantly affects plasma membrane integrity, and

this process is now known to increase the load on the endosomal trafﬁcking (Vandenabeele et al., 2017).

The endosomal sorting complexes required for transport (ESCRT)-III machinery, which acts downstream

of MLKL, manages necroptotic cell death to allow for pro-inﬂammatory cytokine production and extracel-

lular-vesicle (EV) production and release towards emergency signaling purposes before cell demise (Gong

et al., 2017). Like the impact of MLKL depletion, overloading of the endosomal machinery during necrop-

tosis could amount to the slowdown of EGFR recycling with an enhancement of EGF signaling. On the

opposite, sustained ERK1/2 signaling could equally be caused by enhanced recycling of endocytosed

EGFR [for review (Mattissek and Teis, 2014)]. The low expression level of the ESCRT-I protein Tsg101

was reported to cause rapid recycling of EGFR to the cell surface and a subsequent increase in EGFR

signaling associated with a sustained ERK1/2 phosphorylation (Babst et al., 2000). A later report indicated

that only knock-down of ESCRT subunits 0andI proteins would contribute to this effect (Raiborg et al.,

2008). Finally, depending on the stressor, EGFR receptor recycling has been shown to potentiate cell death

or promote cell survival (Tomas et al., 2014).

Previous observations reported that targeted inhibition of EGFR (using Geﬁtinib) reduces ERK1/2 activity

pulses frequency while inhibition of MEK1/2 reduces their amplitude (Albeck et al., 2013). Including results

from this study, further investigation would be required to determine whether the interference with endo-

somal trafﬁcking downstream of MLKL during necroptosis leads to aberrant recycling of EGFR, which acts

as a pulse generator, boosting AM/FM ERK1/2 activity dynamics towards increasingpro-inﬂammatory cyto-

kine gene expression.

In a follow-up study, it might be possible to monitor necroptosis at the single-cell level, thanks to Nakano

et al. who recently ﬁlled a challenging gap in functional live-cell monitoring of necroptosis (Murai et al.,

2018). They produced a sensor for MLKL activation by RIPK3 based on FRET (SMART) biosensor (Murai

et al., 2018;Nakano et al., 2019). Using this SMART biosensor in future experiments would help correlate

the engagement of MLKL with AM/FM ERK1/2 activity proﬁle during either TNF- or MLKL-induced necrop-

tosis in L929 cells. Simultaneous recording of dual kinase activities in the same cell could be performed us-

ing our recently published method (Demeautis et al., 2017). While this would enlighten us about the

coincidence of MLKL activation and ERK1/2 pulsatile activity, it would not provide a strong causality link

between necroptosis initiation and AM/FM ERK1/2 activity-mediated pro-inﬂammatory cytokine gene

expression increase in TNF-stimulated L929 cells. Instead, this will be addressed by generating controlled

activation of ERK1/2 by repetitive pulsatile TNF stimulations using a microﬂuidic system (Ryu et al., 2015;

Tomida et al., 2015) and/or by applying synthetic ERK1/2 activity pulses using optogenetic (Aoki et al.,

2013;Zhou et al., 2017).

Limitations of the study

This report provided an accurate correlation between speciﬁc ERK1/2 signaling dynamics features during

TNF-induced necroptosis and hFasL-induced apoptosis in L929. However, this study does not provide a

causality link: (1) between ERK signaling dynamics features and programmed cell death; (2) between nec-

roptosis initiation and AM/FM ERK1/2 activity-mediated pro-inﬂammatory cytokine gene expression

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increase in TNF-stimulated L929 cells. In addition, while we observed a modulation of MAPK/ERK pathways

in several cell lines using chemical inhibitors, ERK activity signatures were only recorded in the L929 cellular

model.

This study does not link molecular signaling events. Cell death is asynchronous, and the time to death

varies across the cell population. The lack of ﬂuorogenic tools to monitor in real-time necroptosis hall-

marks such as caspase FRET biosensors in apoptosis hampered the detection of necroptotic speciﬁc

signaling events at the single living cells level. Therefore, beyond the known morphological features of

necroptosis, only the time to death using PI has been used as default conﬁrmation of necroptosis in

this report.

STAR+METHODS

Detailed methods are provided in the online version of this paper and include the following:

dKEY RESOURCES TABLE

dRESOURCE AVAILABILITY

BLead contact

BMaterials availability

BData and code availability

dEXPERIMENTAL MODEL AND SUBJECT DETAILS

BCell lines

dMETHODS DETAILS

BAntibodies, cytokines, and reagents

BDNA constructs

BWestern blotting

BImmunostaining

BCell death analysis

BLive cell imaging

BImage data analysis and data representation

B3D modeling & prediction of FRET efﬁciency changes

BPower spectral density (PSD) analysis

BGene expression analysis

dQUANTIFICATION AND STATISTICAL ANALYSIS

SUPPLEMENTAL INFORMATION

Supplemental information can be found online at https://doi.org/10.1016/j.isci.2021.103074.

ACKNOWLEDGMENTS

We thank the VIB BioImaging Core for training, support and access to the instrument park. This collabora-

tive work was encouraged by the CNRS Groupement de recherche (GDR) ‘‘Imabio’’ scientiﬁc community via

the biosensor workgroup initiative. FR acknowledges Nikon BELUX partnership. The authors would like to

thank Anne Schepers for technical assistance with gene expression study, Davy Gaillot, and John Albeck for

helpful discussions and for critical reading of the manuscript.

At the time of the project, FS was a postdoctoral fellow paid by Methusalem grant and FWO projects

(BOF16/MET_V/007, FWO G.0607.13N, FWO G.0C37.14N) and BC held a Ph.D. fellowship from the

FWO and was paid by FWO projects (FWO G.0C76.18N), both are a member of the Death Dynamics

Team (DDT) in the Vandenabeele unit. Research in the Vandenabeele group is supported by EOS

MODEL-IDI (FWO Grant 30826052), FWO research grants (G.0E04.16N, G.0C76.18N, G.0B71.18N,

G.0B96.20N), Methusalem (BOF16/MET_V/007), iBOF20/IBF/039 ATLANTIS, Foundation against Cancer

(F/2016/865, F/2020/1505), CRIG and GIGG consortia, and VIB. FR is a full associate professor at Lille Uni-

versity, a visiting research professor at Ghent University, and DDT leader. FR acknowledges funding from

Oseo - Ministe

`re de l’enseignement supe

´rieur et de la recherche via the national contest 2013 d’aide a

`la

cre

´ation d’entreprise de technologies innovantes cate

´gorie e

´mergence in the context of the KiBioS spin-

off project. This research was supported by the Agence Nationalepour la Recherche (ANR) via the G2Prog-

ress program (ANR-13- BSV2-0016-02) to FR.

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20 iScience 24, 103074, September 24, 2021

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AUTHOR CONTRIBUTIONS

FS, BC, EDS performed experiments, and JB made the L929 iMLKL(S345D) cell line. FS, BC, AL, EDS, GVC,

PH, JB, WD, LH, PVi, PVa, and FR contributed reagents. FS, BC, AL, EDS, PH, PVi, and FR analyzed the data.

AL and PVi developed FRET analysis software. FS, BC, and FR wrote the manuscript. PVa and PVi gave

extensive feedback on the advanced version of the manuscript. All authors discussed the results and com-

mented on the manuscript.

DECLARATION OF INTERESTS

The authors declare no conﬂict of interest.

Received: December 18, 2020

Revised: May 21, 2021

Accepted: August 30, 2021

Published: September 24, 2021

REFERENCES

Aaes, T.L.L., Kaczmarek, A., Delvaeye, T., De

Craene, B., De Koker, S., Heyndrickx, L., Delrue, I.,

Taminau, J., Wiernicki, B., De Groote, P., et al.

(2016). Vaccination with necroptotic cancer cells

induces efﬁcient anti-tumor immunity. Cell Rep.

15, 274–287.

Albeck, J.G., Mills, G.B., and Brugge, J.S. (2013).

Frequency-modulated pulses of ERK activity

transmit quantitative proliferation signals. Mol.

Cell 49, 249–261.

Aoki, K., Kumagai, Y., Sakurai, A., Komatsu, N.,

Fujita, Y., Shionyu, C., and Matsuda, M. (2013).

Stochastic ERK activation induced by noise and

cell-to-cell propagation regulates cell density-

dependent proliferation. Mol. Cell 52, 529–540.

Aoki, K., Kondo, Y., Naoki, H., Hiratsuka, T., Itoh,

R.E., and Matsuda, M. (2017). Propagating wave

of ERK activation orients collective cell migration.

Dev. Cell 43, 305–317.e5.

Babst, M., Odorizzi, G., Estepa, E.J., and Emr,

S.D. (2000). Mammalian tumor susceptibility gene

101 (TSG101) and the yeast homologue, Vps23p,

both function in late endosomal trafﬁcking. Trafﬁc

1, 248–258.

Balmanno, K., and Cook, S.J. (2009). Tumour cell

survival signalling by the ERK1/2 pathway. Cell

Death Differ. 16, 368–377.

Cagnol, S., and Chambard, J.-C.C. (2010). ERK

and cell death: mechanisms of ERK-induced cell

death - apoptosis, autophagy and senescence.

FEBS J. 277, 2–21.

Candeo, A., Doccula, F.G., Valentini, G., Bassi, A.,

and Costa, A. (2017). Light sheet ﬂuorescence

microscopy quantiﬁes calcium oscillations in root

hairs of Arabidopsis thaliana. Plant Cell Physiol

58, 1161–1172.

Chen, D.S., and Mellman, I. (2017). Elements of

cancer immunity and the cancer-immune set

point. Nature 541, 321–330.

Cho, Y., McQuade, T., Zhang, H., Zhang, J., and

Chan, F.K.M. (2011). RIP1-dependent and

independent effects of necrostatin-1 in necrosis

and T cell activation. PLoS ONE 6, e23209.

Degterev, A., Hitomi, J., Germscheid, M., Ch’en,

I.L., Korkina, O., Teng, X., Abbott, D., Cuny, G.D.,

Yuan, C., Wagner, G., et al. (2008). Identiﬁcation

of RIP1 kinase as a speciﬁc cellular target of

necrostatins. Nat. Chem. Biol. 4, 313–321.

Demeautis, C., Sipieter, F., Roul, J., Chapuis, C.,

Padilla-Parra, S., Riquet, F.B., and Tramier, M.

(2017). Multiplexing PKA and ERK1&2 kinases

FRET biosensors in living cells using single

excitation wavelength dual colour FLIM. Sci. Rep.

7, 41026.

Duprez, L., Takahashi, N., Van Hauwermeiren, F.,

Vandendriessche, B., Goossens, V., Vanden

Berghe, T., Declercq, W., Libert, C., Cauwels, A.,

and Vandenabeele, P. (2011). RIP kinase-

dependent necrosis drives lethal systemic

inﬂammatory response syndrome. Immunity 35,

908–918.

Favata, M.F., Horiuchi, K.Y., Manos, E.J., Daulerio,

A.J., Stradley, D.A., Feeser, W.S., Van Dyk, D.E.,

Pitts, W.J., Earl, R.A., Hobbs, F., et al. (1998).

Identiﬁcation of a novel inhibitor of mitogen-

activated protein kinase kinase. J. Biol. Chem.

273, 18623–18632.

Fritz, R.D., Letzelter, M., Reimann, A., Martin, K.,

Fusco, L., Ritsma, L., Ponsioen, B., Fluri, E.,

Schulte-Merker, S., van Rheenen, J., et al. (2013).

A versatile toolkit to produce sensitive FRET

biosensors to visualize signaling in time and

space. Sci. Signal 6, rs12.

Fulda, S., Gorman, A.M., Hori, O., and Samali, A.

(2010). Cellular stress responses: cell survival and

cell death. Int. J. Cell Biol. 2010, 1–23.

Gong, Y., Guy, C., Olauson, H., Becker, J.U.,

Yang, M., Fitzgerald, P., Linkermann, A., and

Green, D.R. (2017). ESCRT-III acts downstream of

MLKL to regulate necroptotic cell death and its

consequences. Cell 169, 286–300.e16.

De Groote, P., Grootjans, S., Lippens, S.,

Eichperger, C., Leurs, K., Kahr, I., Tanghe, G.,

Bruggeman, I., De Schamphelaire, W., Urwyler,

C., et al. (2016). Generation of a new gateway-

compatible inducible lentiviral vector platform

allowing easy derivation of co-transduced cells.

Biotechniques 60, 252–259.

Hiratsuka, T., Fujita, Y., Naoki, H., Aoki, K.,

Kamioka, Y., and Matsuda, M. (2015). Intercellular

propagation of extracellular signal-regulated

kinase activation revealed by in vivo imaging of

mouse skin. eLife 4, e05178.

Hong, S.K., Wu, P.K., and Park, J.I. (2018). A

cellular threshold for active ERK1/2 levels

determines Raf/MEK/ERK-mediated growth

arrest versus death responses. Cell. Signal. 42,

11–20.

Johnson, H.E., and Toettcher, J.E. (2019).

Signaling dynamics control cell fate in the early

drosophila embryo. Dev. Cell 48, 361–370.e3.

Jones, A.M., Danielson, J.A.h, Manojkumar, S.N.,

Lanquar, V., Grossmann, G., and Frommer, W.B.

(2014). Abscisic acid dynamics in roots detected

with genetically encoded FRET sensors. eLife 3,

e01741.

Kohno, M., and Pouyssegur, J. (2006). Targeting

the ERK signaling pathway in cancer therapy.

Ann. Med. 38, 200–211.

Komatsu, N., Aoki, K., Yamada, M., Yukinaga, H.,

Fujita, Y., Kamioka, Y., and Matsuda, M. (2011).

Development of an optimized backbone of FRET

biosensors for kinases and GTPases. Mol. Biol.

Cell 22, 4647–4656.

Levine, J.H., Lin, Y., and Elowitz, M.B. (2013).

Functional roles of pulsing in genetic circuits.

Science 342, 1193–1200.

Madsen, R.R., and Vanhaesebroeck, B. (2020).

Cracking the context-speciﬁc PI3K signaling

code. Sci. Signal. 13, 1–14.

Martens, S., Jeong, M., Tonnus, W., Feldmann, F.,

Hofmans, S., Goossens, V., Takahashi, N., Bra

¨sen,

J.H., Lee, E.W., Van der Veken, P., et al. (2017).

Sorafenib tosylate inhibits directly necrosome

complex formation and protects in mouse

models of inﬂammation and tissue injury. Cell

Death Dis. 8, e2904.

Mattissek, C., and Teis, D. (2014). The role of the

endosomal sorting complexes required for

transport (ESCRT) in tumorigenesis. Mol. Membr.

Biol. 31, 111–119.

OPEN ACCESS

iScience 24, 103074, September 24, 2021 21

iScienc

Article

Morris, E.J., Jha, S., Restaino, C.R., Dayananth, P.,

Zhu, H., Cooper, A., Carr, D., Deng, Y., Jin, W.,

Black, S., et al. (2013). Discovery of a novel ERK

inhibitor with activity in models of acquired

resistance to BRAF and MEK inhibitors. Cancer

Discov. 3, 742–750.

Murai, S., Yamaguchi, Y., Shirasaki, Y., Yamagishi,

M., Shindo, R., Hildebrand, J.M., Miura, R.,

Nakabayashi, O., Totsuka, M., Tomida, T., et al.

(2018). A FRET biosensor for necroptosis

uncovers two different modes of the release of

DAMPs. Nat. Commun. 9, 1–17.

Muta, Y., Fujita, Y., Sumiyama, K., Sakurai, A.,

Taketo, M.M., Chiba, T., Seno, H., Aoki, K.,

Matsuda, M., and Imajo, M. (2018). Composite

regulation of ERK activity dynamics underlying

tumour-speciﬁc traits in the intestine. Nat.

Commun. 9, 1–16.

Najjar, M., Saleh, D., Zelic, M., Nogusa, S., Shah,

S., Tai, A., Finger, J.N., Polykratis, A., Gough, P.J.,

Bertin, J., et al. (2016). RIPK1 and RIPK3 kinases

promote cell-death-independent inﬂammation

by toll-like receptor 4. Immunity 45, 46–59.

Nakano, H., Murai, S., Yamaguchi, Y., Shirasaki,

Y., Nakabayashi, O., and Yamazaki, S. (2019).

Development of novel methods that monitor

necroptosis and the release of DAMPs at the

single cell resolution. Cell Stress 3, 66–69.

Oerlemans, M.I.F.J., Liu, J., Arslan, F., Den

Ouden, K., Van Middelaar, B.J., Doevendans,

P.A., and Sluijter, J.P.G. (2012). Inhibition of RIP1-

dependent necrosis prevents adverse cardiac

remodeling after myocardial ischemia-

reperfusion in vivo. Basic Res. Cardiol. 107, 270.

Ofengeim, D., Ito, Y., Najafov, A., Zhang, Y., Shan,

B., DeWitt, J., Ye, J., Zhang, X., Chang, A.,

Vakifahmetoglu-Norberg, H., et al. (2015).

Activation of necroptosis in multiple sclerosis.

Cell Rep. 10, 1836–1849.

Pasparakis, M., and Vandenabeele, P. (2015).

Necroptosis and its role in inﬂammation. Nature

517, 311–320.

Pham, E., Chiang, J., Li, I., Shum, W., and Truong,

K. (2007). A computational tool for designing fret

protein biosensors by rigid-body sampling of

their conformational space. Structure 15,

515–523.

Purvis, J.E., and Lahav, G. (2013). Encoding and

decoding cellular information through signaling

dynamics. Cell 152, 945–956.

Raiborg, C., Malerød, L., Pedersen, N.M., and

Stenmark, H. (2008). Differential functions of Hrs

and ESCRT proteins in endocytic membrane

trafﬁcking. Exp. Cell Res. 314, 801–813.

Regot, S., Hughey, J.J., Bajar, B.T., Carrasco, S.,

and Covert, M.W. (2014). High-sensitivity

measurements of multiple kinase activities in live

single cells. Cell 157, 1724–1734.

Ryu, H., Chung, M., Dobrzy

nski, M., Fey, D., Blum,

Y., Lee, S.S., Peter, M., Kholodenko, B.N., Jeon,

N.L., and Pertz, O. (2015). Frequency modulation

of ERK activation dynamics rewires cell fate. Mol.

Syst. Biol. 11, 838.

Ryu, H., Chung, M., Dobrzy

nski, M., Fey, D., Blum,

Y., Lee, S.S., Peter, M., Kholodenko, B.N., Jeon,

N.L., Pertz, O., et al. (2016). Frequency

modulation of ERK activation dynamics rewires

cell fate. Mol. Syst. Biol. 12, 866.

Sipieter, F., Vandame, P., Spriet, C., Leray, A.,

Vincent, P., Trinel, D., Bodart, J.-F., Riquet, F.B.,

and He

´liot, L. (2013). From FRET imaging to

practical methodology for kinase activity sensing

in living cells. Prog. Mol. Biol. Transl. Sci. 113,

145–216.

Sparta, B., Pargett, M., Minguet, M., Distor, K.,

Bell, G., and Albeck, J.G. (2015). Receptor level

mechanisms are required for epidermal growth

factor (EGF)-stimulated extracellular signal-

regulated kinase (ERK) activity pulses. J. Biol.

Chem. 290, 24784–24792.

Sun, L., Wang, H., Wang, Z., He, S., Chen, S., Liao,

D., Wang, L., Yan, J., Liu, W., Lei, X., et al. (2012).

Mixed lineage kinase domain-like protein

mediates necrosis signaling downstream of RIP3

kinase. Cell 148, 213–227.

Takahashi, N., Duprez, L., Grootjans, S., Cauwels,

A., Nerinckx, W., DuHadaway, J.B., Goossens, V.,

Roelandt, R., Van Hauwermeiren, F., Libert, C.,

et al. (2012). Necrostatin-1 analogues: critical

issues on the speciﬁcity, activity and in vivo use in

experimental disease models. Cell Death Dis. 3,

e437.

Takahashi, N., Vereecke, L., Bertrand, M.J.M.,

Duprez, L., Berger, S.B., Divert, T., Gonc¸ alves, A.,

Sze, M., Gilbert, B., Kourula, S., et al. (2014). RIPK1

ensures intestinal homeostasis by protecting the

epithelium against apoptosis. Nature 513, 95–99.

Thestrup, T., Litzlbauer, J., Bartholoma

¨us, I.,

Mues, M., Russo, L., Dana, H., Kovalchuk, Y.,

Liang, Y., Kalamakis, G., Laukat, Y., et al. (2014).

Optimized ratiometric calcium sensors for

functional in vivo imaging of neurons and T

lymphocytes. Nat. Methods 11, 175–182.

Tomas, A., Futter, C.E., and Eden, E.R. (2014).

EGF receptor trafﬁcking: consequences for

signaling and cancer. Trends Cell Biol. 24, 26–34.

Tomida, T., Takekawa, M., and Saito, H. (2015).

Oscillation of p38 activity controls efﬁcient pro-

inﬂammatory gene expression. Nat. Commun. 6,

8350.

Vandame, P., Spriet, C., Riquet, F., Trinel, D.,

Cailliau-Maggio, K., and Bodart, J.-F. (2014).

Optimization of ERK activity biosensors for both

ratiometric and lifetime FRET measurements.

Sensors (Basel) 14, 1140–1154.

Vanden Berghe, T., Vanlangenakker, N.,

Parthoens, E., Deckers, W., Devos, M., Festjens,

N., Guerin, C.J., Brunk, U.T., Declercq, W.,

Vandenabeele, P., et al. (2010). Necroptosis,

necrosis and secondary necrosis converge on

similar cellular disintegration features. Cell Death

Differ. 17, 922–930.

Vandenabeele, P., Declercq, W., Van

Herreweghe, F., and Vanden Berghe, T. (2010).

The role of the kinases RIP1 and RIP3 in TNF-

induced necrosis. Sci. Signal. 3, re4.

Vandenabeele, P., Riquet, F., and Cappe, B.

(2017). Necroptosis: (last) message in a bubble.

Immunity 47, 1–3.

Vanlangenakker, N., Vanden Berghe, T., Bogaert,

P., Laukens, B., Zobel, K., Deshayes, K., Vucic, D.,

Fulda, S., Vandenabeele, P., and Bertrand,

M.J.M.M. (2010). cIAP1 and TAK1 protect cells

from TNF-induced necrosis by preventing RIP1/

RIP3-dependent reactive oxygen species

production. Cell Death Differ. 18, 656–665.

Vercammen, D., Beyaert, R., Denecker, G.,

Goossens, V., Van Loo, G., Declercq, W.,

Grooten, J., Fiers, W., and Vandenabeele, P.

(1998). Inhibition of caspases increases the

sensitivity of L929 cells to necrosis mediated by

tumor necrosis factor. J. Exp. Med. 187, 1477–

1485.

Wallach, D., Kovalenko, A., and Kang, T.B. (2011).

Necrosome’-induced inﬂammation: must cells

die for it? Trends Immunol. 32, 505–509.

Wallach, D., Kang, T.-B., Dillon, C.P., and Green,

D.R. (2016). Programmed necrosis in

inﬂammation: toward identiﬁcation of the

effector molecules. Science 352, aaf2154.

Wilson, M.Z., Ravindran, P.T., Lim, W.A., and

Toettcher, J.E. (2017). Tracing information ﬂow

from erk to target gene induction reveals

mechanisms of dynamic and combinatorial

control. Mol. Cell 67, 757–769.e5.

Wortzel, I., and Seger, R. (2011). The ERK cascade:

distinct functions within various subcellular

organelles. Genes Cancer 2, 195–209.

Yagoda, N., von Rechenberg, M., Zaganjor, E.,

Bauer, A.J., Yang, W.S., Fridman, D.J., Wolpaw,

A.J., Smukste, I., Peltier, J.M., Boniface, J.J., et al.

(2007). RAS-RAF-MEK-dependent oxidative cell

death involving voltage-dependent anion

channels. Nature 447, 864–868.

Yoon, S., Kovalenko, A., Bogdanov, K., and

Wallach, D. (2017). MLKL, the protein that

mediates necroptosis, also regulates endosomal

trafﬁcking and extracellular vesicle generation.

Immunity 47, 51–65.e7.

Zhou, X.X., Fan, L.Z., Li, P., Shen, K., and Lin, M.Z.

(2017). Optical control of cell signaling by single-

chain photoswitchable kinases. Science 355,

836–842.

Zhu, K., Liang, W., Ma, Z., Xu, D., Cao, S., Lu, X.,

Liu, N., Shan, B., Qian, L., and Yuan, J. (2018).

Necroptosis promotes cell-autonomous

activation of proinﬂammatory cytokine gene

expression article. Cell Death Dis. 9, 500.

Zhuang, S., and Schnellmann, R.G. (2006). A

death-promoting role for extracellular signal-

regulated kinase. J. Pharmacol. Exp. Ther. 319,

991–997.

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STAR+METHODS

KEY RESOURCES TABLE

REAGENT or RESOURCE SOURCE IDENTIFIER

Antibodies

Anti-ERK1 + ERK2 antibody Abcam Cat# ab17942

anti-b-actin Santa Cruz Biotechnology Cat# I-19

anti-RIPK1 BD-Biosciences Cat# 610459

anti-b-tubulin Abcam Cat# ab6046

anti-b-tubulin-HRP Abcam Cat# ab21058

Anti-MAP Kinase, Activated

(Diphosphorylated ERK-1&2) antibody

Sigma Aldrich Clone MAPK-YT, M9692

anti-MLKL Merck Millipore Cat# MABC604, Clone 3H1

Anti-phospho-MLKL (Ser345) Merck Millipore Cat# MABC1158, clone 7C6.1

Phospho-MEK1/2 (Ser217/221) Cell Signalling Cat# 9121

Anti-MEK1/2 Cell Signalling Cat# 9122

anti-RIPK3 Santa Cruz Cat# sc-374639

anti-RSK1/2/3 Cell Signalling Cat# 9355, clone 32D7

anti-phspho-p90-RSK (Ser380) Cell Signalling Cat# 9341

anti-GSK3bCell Signalling Cat# 12456, clone D5C5Z-XP

anti-phospho-GSK-3a/bCell Signalling Cat# 9331

anti-mouse Alexa Fluor 488 Thermo Fischer Cat# A-10667

HRP-conjugated anti-rabbit IgG Santa Cruz Biotechnology Cat# sc-2004

HRP-conjugated anti-mouse IgG Santa Cruz Biotechnology Cat# sc-2005

HRP-conjugated anti-goat IgG Santa Cruz Biotechnology Cat# sc-2020

HRP-conjugated anti-rat- IgG GE Healthcare Cat# NA935

Chemicals, peptides, and recombinant proteins

Recombinant mouse TNFaIn house production N/A

Anti-hFas ligand Upstate Cat# 05-201Clone CH11

Phorbol 12-myristate 13-acetate (PMA) Sigma Aldrich Cat# P8139

U0126 Sigma Aldrich Cat# U120

PD184352 Sigma Aldrich Cat# PZ0181

AZ628 Sigma Aldrich Cat# SML0785

BI-D1878 Sigma Aldrich Cat# 559286

Forskoline Tocris Cat# 1099/10

SCH772984 Selleckchem Cat# S7101

Selumetinib (AZD6244) Selleckchem Cat# S1008

Trametinib (GSK1120212) Selleckchem Cat# S2673

Necrostatin-1S (Nec1S) Laboratory of Medicinal Chemistry;

University of Antwerp

Cat# UAMC-02197

Propidium Iodide Sigma Aldrich Cat# P-4170

Sytox Green Thermo Fischer Scientiﬁc Cat# S7020

Doxycycline Hyclate (Dox) Sigma Aldrich Cat# D9891

Rneasy Mini Kit Qiagen Cat# 74104

Qiashredder Qiagen Cat# 79654

DNA-freeDNA Removal Kit Invitrogen Cat# AM1906

(Continued on next page)

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iScience 24, 103074, September 24, 2021 23

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Continued

REAGENT or RESOURCE SOURCE IDENTIFIER

SuperScriptIII First-Strand Synthesis

System for RT-PCR

Invitrogen Cat# 18080-51

qPCR MasterMix Plus for SYBR

Green I No ROX

Eurogentec Cat# RT-SN2X-03+NR

Ac-DEVD-AMC Pepta Nova Cat# 3171-V

Bio-Gel P-6 Micro Bio-Spin chromatography

columns

Biorad Cat# 732-6222

plasmid puriﬁcation columns Qiagen Cat# 28106, 28706, and 27106

PCR colony screening kit 5Prime Cat# MasterTaq Kit

Q5 DNA polymerase NEB Cat# M0491

Taq DNA polymerase NEB Cat# M0273

Klenow fragment NEB Cat# M0212

T4 Polynucleotide Kinase (PNK) NEB Cat# M0201

T4 DNA ligase NEB Cat# M0202

dNTPs NEB Cat# N0447

ATP NEB Cat# P0756

Restriction Enzymes, endonucleases NEB N/A

four-fragment Multisite Gateway Pro Plus

system

Life Technologies Cat# 12537-100

EDTA-free Complete protease inhibitor

cocktail tablets

Roche Diagnostics Belgium Cat# 11873580001

phosphatase inhibitor cocktail tablets Roche Diagnostics Belgium Cat# 04906837001, PhosSTOP

Luminata Classico Western HRP

Chemiluminescence Detection Reagents

Millipore Cat# WBLUC0500

Experimental models: Cell lines

HeLa ECACC Cat# 93021013

HEK 293T ATCC Cat# CRL-11268

L929sAhFas Generated in our unit N/A

HT-29 ATCC Cat# ATCC-HTB-38

inducible mRIPK3-CT26 Generated in our unit Aaes et al. (2016)

Fadd

-/-

Jurkat Generated in our unit N/A

Mlkl

-/-

L929sAhFas Generated in our unit N/A

inducible mMLKL(S345D)-L929sAhFas Generated in our unit N/A

Recombinant DNA

AKAR4.0 This study N/A

EKAR4.0 This study N/A

AKAR3-EV Kindly provided by the Michiyuki Matsuda

laboratory (Laboratory of Bioimaging and Cell

Signaling, Graduate School of Biostudies,

Kyoto University, Kyoto, Japan)

Komatsu, N., Aoki, K., Yamada, M., Yukinaga,

H., Fujita, Y., Kamioka, Y., and Matsuda, M.

(2011). Development of an optimized

backbone of FRET biosensors for kinases and

GTPases. Mol. Biol. Cell 22, 4647–4656.

AKAR-EV (T>A) This study N/A

EKAR-EV Kindly provided by the Michiyuki Matsuda

laboratory (Laboratory of Bioimaging and Cell

Signaling, Graduate School of Biostudies,

Kyoto University, Kyoto, Japan)

Komatsu, N., Aoki, K., Yamada, M., Yukinaga,

H., Fujita, Y., Kamioka, Y., and Matsuda, M.

(2011). Development of an optimized

backbone of FRET biosensors for kinases and

GTPases. Mol. Biol. Cell 22, 4647–4656.

(Continued on next page)

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24 iScience 24, 103074, September 24, 2021

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RESOURCE AVAILABILITY

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulﬁlled by

Franck. B. Riquet (franck.riquet@univ-lille.fr).

Materials availability

Plasmids generated in this study are available upon request to the Lead Contact with a completed Mate-

rials Transfer Agreement.

Data and code availability

All data reported in this paper will be shared by the lead contact upon request.

Custom computer code used to analyze FRET data set from long-term live-cell imaging experiments has

been deposited at the APP (Agence pour la Protection des Programmes) under the following terms: Cell-

TraFIC (Cellular Tracking in FRET Image aCquisition), IDDN.FR.001.230009.000.S.P.2017.000.21000 and on

GitHub (https://github.com/ayleray/CellTraFIC). In addition, FRET data set from short-term live-cell imag-

ing experiments has been analyzed using Ratioscope_8.31, running on IGOR Pro environment (DOI:

10.25493/5G5V-HBC.)

Any additional information required to reanalyze the data reported in this work paper is available from the

Lead Contact upon request.

Continued

REAGENT or RESOURCE SOURCE IDENTIFIER

EKAR-EV (T>A) This study N/A

EKAR2G-WT A gift from Olivier Pertz Addgene plasmid # 39813 ; http://n2t.net/

addgene:39813 ; RRID:Addgene_39813

EKAR2G-105/WT A gift from Olivier Pertz Addgene plasmid # 39818 ; http://n2t.net/

addgene:39818 ; RRID:Addgene_39818

EKAR-EV-VV Previously developed Vandame, P., Spriet, C., Riquet, F., Trinel, D.,

Cailliau-Maggio, K., and Bodart, J.-F. (2014).

Optimization of ERK activity biosensors for

both ratiometric and lifetime FRET

measurements. Sensors (Basel). 14, 1140–

1154.

pDG2-ﬂag-rtTA3-puro-mRIPK3 Previously generated in our unit Aaes et al. (2016)

pDG2-ﬂag-rtTA3-puro-mMLKL(S345D) Generated in our unit N/A

Software and algorithms

Statistical analyses GraphPad Prism PRISM 8 software, RRID: SCR_002798

qPCR analysis Biogazelle qBasePLUS, RRID: SCR_003370

FRET analysis (short experiment) IGOR Pro - Wavemetrics Ratioscope_8.31, DOI:10.25493/5G5V-HBC

FRET analysis (long experiment) Matlab, MathWorks CellTraFIC code, RRID: SCR_001622

CellTraFIC (Cellular Tracking in

FRET Image aCquisition)

APP Paris IDDN.FR.001.230009.000.S.P.

2017.000.21000

CellTraFIC (Cellular Tracking in FRET Image

aCquisition)

GitHub https://github.com/ayleray/CellTraFIC

Cell Death Analysis Fiji RRID: SCR_002285

Immunoﬂuorescence quantiﬁcation Cell Proﬁler RRID: SCR_007358

OPEN ACCESS

iScience 24, 103074, September 24, 2021 25

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EXPERIMENTAL MODEL AND SUBJECT DETAILS

Cell lines

HeLa cells (ECACC 93021013), HEK 293T (ATCC CRL-11268), L929sAhFas, HT-29, and CT26 cells were

cultured at 37Cunder5%CO

in Dulbecco’s Modiﬁed Eagle Medium (DMEM) supplemented with 10%

Fetal Bovine Serum (FBS, qualiﬁed, heat-inactivated, EU-approved, South America Origin), Na-Pyruvate

(400 mM), 2 mM L-Glutamine and 100 U/mL Penicillin/Streptomycin (Gibco, Thermo Fisher Scientiﬁc). Ju-

rkat FADD

/

were cultured similarly but in Roswell Park Memorial Institute (RPMI) medium. All cell lines

were regularly tested against mycoplasma contamination. L929sAhFas (referred to as L929) were previously

described (Vercammen et al., 1998). Mlkl

/-

L929sAhFas cells were generated by CRISPR/Cas9. These deﬁ-

cient cells were transduced with pDG2-ﬂag-rtTA3-puro-mMLKL(S345D), and clones were obtained by

limited dilution. The inducible RIPK3 murine colon carcinoma CT26 was produced as described previously

(Aaes et al., 2016). Brieﬂy, CT26 cells were transduced with pDG2-ﬂag-rtTA3-puro-mRIPK3, and clones were

obtained by limited dilution. Both constructions were cloned into a Flag-containing, doxycycline-inducible

pLenti6 derived vector, already described elsewhere (De Groote et al., 2016). Both inducible cell lines were

stimulated with doxycycline (1 mg/mL,Dox)toinducenecroptosis.

METHODS DETAILS

Antibodies, cytokines, and reagents

Recombinant mouse TNFa(TNF) produced in our laboratoryhas a speciﬁc biological activity of 6,12x10

IU/

mL and was used at 1000 IU/mL to stimulate L929 cells. Anti-Fas (hFasL, human, activating, clone CH11, #05-

201) was purchased from Upstate and used at a ﬁnal concentration of 500 ng/mL. Phorbol myristate acetate

(PMA), MEK1/2 inhibitors U0126 and PD184352, Raf inhibitor, and RSK inhibitor (BI-D1870) were purchased

from Sigma Aldrich, Forskolin (Fsk), MEK1/2 (Trametinib and Selumetinib), and ERK1/2 inhibitor

(SCH772984) from Selleckchem. All MEK1/2 and ERK1/2 inhibitors were used at 20 mM unless indicated

otherwise. RIPK1 inhibitor, necrostatin-1S (Nec1S) (UAMC-02197, synthesised by the Laboratory of Medic-

inal Chemistry; University of Antwerp) was used at 10 mM. Propidium iodide (PI) (Sigma Aldrich) and Sytox-

Green (Thermo Fisher Scientiﬁc) were used at 3 mMand5mM, respe ctively. Doxycycline Hyclate (Dox, Sigma

Aldrich) was used at 1 mg/mL. The following antibodies were used for western blot: anti-ERK1/2 (Abcam,

ab17942, 1:1000), anti-b-actin (Santa Cruz Biotechnology, I-19, 1:1000), anti-RIPK1 (BD Biosciences,

610459, 1:1000), anti-btubulin (Abcam, ab6046, 1:1000), anti- b-tubulin-HRP (Abcam, ab21058), anti-acti-

vated MAPK/ERK1&2 (diphosphorylated ERK1/2) antibody (clone MAPK-YT, Sigma Aldrich, M9692,

1:2000), anti-phosphoMEK1/2 (Cell Signalling, 9121), anti-MEK1/2 (Cell Signalling, 9122), anti-RIPK3

(Santa-Cruz, sc-374639), anti-MLKL (Merck Millipore, MABC604, Clone 3H1), anti-phospho-S345-MLKL

(Merck Millipore, MABC1158, clone 7C6.1), anti-RSK1/2/3 (Cell Signalling, 32D7, 9355), anti-phspho-p90-

RSK (Ser380) (Cell Signalling, 9341), anti-GSK3b(Cell Signalling, D5C5Z-XP, 12456) and anti-phospho-

GSK-3a/b(Ser21/9) (Cell Signalling, 9331). HRP-conjugated secondary antibodies were anti-rabbit IgG,

anti-mouse IgG, or anti-goat IgG (Santa Cruz Biotechnology, sc-2004, sc-2005, sc-2020 respectively,

1:10000).

DNA constructs

FRET biosensor plasmids AKAR3-EV and EKAR-EV were kindly provided by the Michiyuki Matsuda labora-

tory (Laboratory of Bioimaging and Cell Signaling, Graduate School of Biostudies, Kyoto University, Kyoto,

Japan). EKAR2G-WT (mTFP wild type and Venus wild type) and EKAR2G-105/WT (mTFP 105 and Venus wild

type) were a gift from Olivier Pertz (Addgene plasmid # 39813 ; http://n2t.net/addgene:39813 ; RRID:Addg-

ene_39813 and Addgene plasmid # 39818 ; http://n2t.net/addgene:39818 ; RRID:Addgene_39818 respec-

tively) (Fritz et al., 2013). t-EKAR-EV-VV was previously described (Vandame et al., 2014).

EKAR3-EV GW4.0 and AKAR3-EV GW4.0 were developed starting from the ERKAR3-EV and AKAR3-EV,

respectively, using the four-fragment Multisite Gateway Pro Plus system (Life Technologies). Brieﬂy, this

system allows four separate FRET biosensor bricks to be simultaneously transferred to a Destination vector

using Gateway BP and LR recombination reactions. The extended method is described in the Supplemen-

tary material. A schematic overview of the cloning strategy is presented in Figure S3B. Brieﬂy, the destina-

tion vector was generated starting from the original biosensors. The sensing domain was excised and

replaced by a reading frame (Rf) Gateway cassette. The pEntr vectors containing the ﬂuorescent proteins

and the PAABD + linker were generated by PCR ampliﬁcation of the elements from the original vectors,

followed by a BP recombination with the appropriate pDonr vectors. Speciﬁc sequences named attB

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26 iScience 24, 103074, September 24, 2021

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were included in the design of the PCR primers. Concerning the substrate, an oligonucleotide cassette was

inserted in the dedicated pEntr vector. Finally, to generate the pEx vector coding for the FRET biosensor,

we performed a quadruple LR reaction containing the different pEntr vectors containing the donor and

acceptor ﬂuorescent proteins, the PAABD with its linker, and the substr ate, according to the manufacturer’s

recommendations.

The identity of all recombined vectors was veriﬁed by restriction digestion proﬁling or by PCR colony

screening (MasterTaq Kit, 5Prime), and then validated by sequencing (Genoscreen, France). Restriction en-

donucleases, Q5 and Taq DNA polymerase, Klenow fragment, T4 Polynucleotide Kinase (PNK), T4 DNA

ligase, dNTPs, ATP, and speciﬁc buffers were all purchased from New England Biolabs. All oligonucleo-

tides were synthesized by Eurogentec (Belgium). Each complementary oligonucleotide designed to create

double-stranded cassettes was 5’-phosphorylated by T4 PNK and then puriﬁed using Bio-Gel P-6 Micro

Bio-Spin chromatography columns (#732-6222, Biorad). DNA fragments were puriﬁed on Qiagen plasmid

puriﬁcation columns (#28106, #28706, and # 27106, Qiagen).

Western blotting

After TNF stimulation, at speciﬁed time intervals, cells were washed twice in ice-cold PBS and scraped

usingice-coldRIPAlysisbuffer(50mMTris-HCl,pH 7.5; 150 mM NaCl; 1 mM EDTA; 0.5% sodium deoxy-

cholate; 1% Triton X-100 and 0.1% SDS) freshly supplemented with EDTA-free Complete protease inhibitor

cocktail tablets (no. 11873580001) and phosphatase inhibitor cocktail tablets (PhosSTOP, no. 04906837001,

Roche Diagnostics Belgium N.V.). Extracted proteins were separated on 12% SDS polyacrylamide gels or

4-12% Bis-Tris Plus Gels (ThermoFisher) and then transferred onto nitrocellulose membranes (Amersham

Bioscience). Membranes were blocked using TBS with 0.05% Tween20 (TBS-T) containing 5% non-fat dry

milk (Biorad) or in 2% BSA for phospho-antibodies. Luminata Classico Western HRP Chemiluminescence

Detection Reagents (Millipore) were used for antibody detection.

Immunostaining

At speciﬁed time intervals upon TNF stimulation, L929 cells were ﬁxed with 4% paraformaldehyde in PBS for

10 min. Afterward, cells were rinsed three times with PBS and permeabilized with 0.5% Triton X-100 in PBS

for 5 min. Cells were incubated with a blocking solution composed of 2% FBS, 5% normal goat serum, and

2% BSA in PBS for 1h at room temperature and then further incubated with primary antibodies in blocking

solution at 4C overnight. The antibodies used were anti-MAPK activated (Sigma Aldrich, 1:500). After

washing steps, cells were incubated in a blocking solution containing anti-mouse Alexa Fluor 488 (Thermo

Fisher Scientiﬁc, A-10667, 1:500) for 1h at room temperature in the dark. Finally, cells were incubated for

10 min in Hoechst 33258 (10 mg/mL), and then slides were mounted in ProLong Gold anti-fading reagent

(Thermo Fisher Scientiﬁc, P36930). Immunoﬂuorescence imaging was performed at room temperature

with an inverted confocal Leica TCS SP5 X microscope (DMI6000, Leica Microsystems). A 40X/1.25NA oil

immersion objective was used. Image size was 1024 x 1024 pixels, and the zoom factor was 1, for a pixel

size of 0.3788 mm. Lasers source was a white light laser (Koheras). Image quantiﬁcation was performed using

CellProﬁler.

Cell death analysis

Cell death was determined by measuring Sytox Green (5 mM, Life Technologies) and Ac-DEVD-AMC

(20 mM, Pepta Nova) intensity using a FLUOstar Omega ﬂuorescence plate reader (BMG Labtech) with exci-

tation/emission ﬁlters of 485/520 nm and 360/460 nm, respectively. Triton X-100 at a ﬁnal concentration of

0.1% was added at the end of time-lapse experiments for 1 h to obtain the maximal ﬂuorescence intensity,

corresponding to 100%. Cell death was also detected by monitoring Hoechst and PI ﬂuorescence intensity

using video-microscopy with excitation ﬁlters 395/25 and 575/25 and emission ﬁlters 440/40 and 632/60,

respectively. Quantiﬁcation was performed using Image J.

Live cell imaging

Cells were seeded on 8-well dishes ibiTreat (Ibidi) to reach 60% conﬂuence at the time of transfection. For

live-cell imaging, cells were cultured in Fluorobrite DMEM (Gibco, Thermo Fisher Scientiﬁc). Transfections

were performed using JetPrime reagent (Polyplus) according to the manufacturer’s instructions. The me-

dium was refreshed 4 h after transfection to reduce cytotoxicity. Cells were serum-deprived with a medium

containing 1% FBS for 12 h before experiments began. For long-term experiments, Hoechst 33342 (1 mM)

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was added in order to track the cells. On the day of microscopy experiments, cells were placed directly in-

side the microscope stage chamber under controlled temperature, CO

and humidity (top stage incu-

bator, Okolab) 1 h before starting cellular imaging to equilibrate. Wide-ﬁeld images were captured with

a Nikon TiE inverted microscope with a 20X/0.5NA objective and a DS-Qi2 CMOS camera (Nikon). Images

were acquired at intervals of 2 min (biosensor reference experiment) or 4 min (biosensor imaging) with the

Nikon NIS-Elements acquisition software using JOBS module (Nikon). A Lumencor Spectra X LED Light En-

gine (Lumencor) provided the excitation light source. Ratio imaging used a 440/30 excitation ﬁlter, a t440/

510/575rpc multi-band dichroic mirror, and two emission ﬁlters (ET480/40M (CFP) and AT545/30M (FRET)).

Lumencor provided excitation ﬁlters, and all dichroic mirrors and emission ﬁlters were obtained from

Chroma Technology. An automated emission ﬁlter wheel Lambda 10-B Smart Shutter (Sutter Instrument)

was used.

Image data analysis and data representation

Analysis of the ERK1/2 and PKA biosensors comparison was performed with custom routines written in the

IGOR Pro environment (Wavemetrics, available at https://doi.org/10.25493/5G5V-HBC). Long time-lapse

FRET biosensing measurements in the context of cell death study were analyzed using custom Matlab

(MathWorks, available at github.com/ayleray/CellTraFIC) routines for segmentation (with a standard con-

nected components algorithm), tracking (by comparing as a function of time the Euclidean distance of the

coordinates of the centroids of segmented cells) and quantiﬁcation of ratiometric FRET data. Based on

Hoechst staining for nuclear segmentation and cell tracking, we monitored ERK1/2 activity over time.

FRET intensity was calculated as a ratio of the CFP/YFP signal for each pixel on background-subtracted im-

ages. Pseudocolor images represent both the ratio and the ﬂuorescence intensity values, where warmer

colors denote high FRET and cooler colors low FRET efﬁciency. The calibration bar shows the ﬂuorescence

intensity horizontally and the ratio values vertically. The micron-scale of the images corresponds to the size

of the calibration bar.

We borrowed the semi-automated image analysis protocol previously described for enhanced FRET vari-

ations representation purpose and optimal reporting on both single-cell and population-level responses

(Sparta et al., 2015). We displayed a kymographic representation of ERK1/2 activity shown as a color range,

representative FRET pictures, and population-level trends. Kymographs represent a dynamic process on a

single panel and correspond to a FRET ratio-time scan, where the FRET ratio values are plotted along the

timeline for all images of the time-lapse stack. One line corresponds to one cell, and one column corre-

sponds to one time point. These lines are then stacked along the x-axis (time) for all the cells (y-axis).

Dead cells were detected by incubating cells using propidium iodide.When red ﬂuorescence was detected

in the red channel, image analysis was stopped. The cells were ordered from top to bottom based on their

living time span.

The variations of ERK1/2 activity (pulses) were detected using a Matlab-based, semi-automated scoring

routine. We ﬁrst determined an experimental threshold (T=0,15) corresponding to the condition where lit-

tle or no pulse of ERK1/2 activity should be detected, theoretically. Alternatively, results obtained with the

mutated version of ERK1/2 biosensor (T>A) could also be used and processed for pulse analysis to deﬁne

the threshold value. By systematically applying the threshold obtained following the ERK1/2 pulses analysis

from the SCH772984 stimulated cell condition, we generated binary kymographs of ERK1/2 activity pulses

over time for all remaining experimental conditions.

3D modeling & prediction of FRET efﬁciency changes

The different protein domains of the biosensor were modeled separately. A BLAST search against all

Protein Data Bank (PDB) structures was performed for YPET, WW domain, ERK1/2 substrate, and

eCFP. Multiple templates were selected for homology modeling with Modeller, and 10 models were

generated for each protein domain. The best model was selected by comparing scores (molpdf,

DOPE score, GA341 score) and Ramachandran plot assessment. Fusion Protein MODeller (FPMOD)

was used to predict FRET efﬁciency changes by sampling conformational space through rigid body rota-

tion using the pdb ﬁles of the modeled proteins as input and deﬁning ﬂexible linkers. Multiple confor-

mations were calculated for the N-terminal part (YPET+WW domain) and the C-terminal part (ERK+eCFP)

using different linkers.

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Power spectral density (PSD) analysis

ERK1/2 peaks were detected with a standard algorithm for ﬁnding local maxima based on the derivative of

the signal; the corresponding amplitude and time of each maximum were recorded. In order to facilitate

the visualization, we represent only the mean in Figure 6. At low frequency (f<0.1mHz), the PSD curves

decrease proportionally to 1/f

, corresponding to a Brownian noise (decrease of 20dB per decade) which

is produced by Brownian motion (or random walk process). For high frequency, we observe that PSD curves

decrease with a slope of 10 dB per decade, corresponding to the signature of pink noise (also called 1/f

noise). This kind of noise occurs in numerous biological systems (e.g., brain or heart) because it originates

from the ion channel’s opening-closing process.

Gene expression analysis

According to the manufacturer’s protocol, RNA was extracted from L929 cells using the RNeasy Mini Kit

(Qiagen, 74104) and Qiashredder (Qiagen, 79654). The DNA-freeDNA Removal Kit (Invitrogen,

AM1906) was used to remove contaminating DNA and added DNase from the RNA samples. The ﬁrst-

strand cDNA of L929 cells was synthesized from total RNA using SuperScriptIII First-Strand Synthesis

System for RT-PCR (Invitrogen, 18080-51). Plate-based real-time PCR ampliﬁcation and detection of the

synthesized cDNA was done on the Lightcycler 480 (Roche) with qPCR MasterMix Plus for SYBRGreen

I No ROX (Eurogentec, RT-SN2X-03+NR) according to the manufacturer’s guidelines. mRNA levels of mu-

rine A20, IkB-a, Tnf, Mcp1, Il-6, Cxcl-1 were normalized to multiple reference genes (Actb, Cdc40, Htatsf)

using qbase+ software and primers listed in Table S2.

QUANTIFICATION AND STATISTICAL ANALYSIS

Results are presented as means GSEM unless indicated otherwise in ﬁgure captions. Statistical analyses

were performed using PRISM 8 software (GraphPad). One-way and two-way ANOVA testswere performed,

followed by Tukey’s post hoc test. Alpha was set at 0,05. For qPCR, statistical signiﬁcance was determined

using an unpaired t-test. Signiﬁcance between samples is indicated as follows: *p < 0.05; **p < 0.01;

***p < 0.001; ns, not signiﬁcant.

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JianPiYiShen formula prevents cisplatin-induced acute kidney injury in mice by improving necroptosis through MAPK pathway

Article

Full-text available

Feb 2024

Background Acute kidney injury (AKI), characterized by necroptosis and activation of MAPK pathway, causes sudden declines in renal function. To date, efficacious treatments are lacking. JianPiYiShen Formula (JPYSF) has a protective effect on the kidneys. The aim of this study is to explore the mechanism of JPYSF in cisplatin-induced AKI. Methods Male C57/BL6J mice were divided into control group, cisplatin group and cisplatin + JPYSF group. Before establishing the model, the cisplatin + JPYSF group was administered JPYSF (18.35 g/kg/day) by gavage for 5 consecutive days. A single intraperitoneal injection of cisplatin (20 mg/kg) was used to establish AKI model. Measurement of renal function and H&E staining were performed to assess renal damage. WB, PCR, TUNEL staining and immunohistochemistry were used to detect related indicators of mitochondrial function, oxidative stress, necroptosis, inflammation and MAPK pathway. And one-way analysis of variance was used to compare group differences. Results Compared with the cisplatin group, JPYSF can attenuate AKI, reflected by the decrease in Scr and BUN levels, the improvement of renal tubular injury, and the downregulation of NGAL and KIM1. Cisplatin can induce mitochondrial dysfunction and oxidative stress, triggering necroptosis. In this study, JPYSF improved mitochondrial dysfunction to enhance oxidative stress, as manifested by upregulation of OPA1, PGC-1α, SOD and CAT, and downregulation of DRP1 and MFF. Then JPYSF showed a significant protective effect in necroptosis, as embodied by reduced number of TUNEL-positive cells, decreased the gene expression of RIPK3 and MLKL, as well as downregulation the proteins expression of P-RIPK1, P-RIPK3, and P-MLKL. Moreover, necroptosis can aggravate inflammation. JPYSF ameliorated inflammation by improving inflammatory and anti-inflammatory indexes, including downregulation of TNF-α, IL-6, MCP-1 and LY6G, and upregulation of IL-10. In addition, JPYSF also inhibited MAPK pathway to improve necroptosis by decreasing the expression of P-JNK and P-ERK. Conclusion Our data showed that JPYSF prevents cisplatin-induced AKI by improving necroptosis through MAPK pathway, which is related to the improvement of mitochondrial dysfunction, oxidative stress, and inflammation.

A guide to ERK dynamics, part 2: downstream decoding

Article

Full-text available

Dec 2023

Signaling by the extracellular signal-regulated kinase (ERK) pathway controls many cellular processes, including cell division, death, and differentiation. In this second installment of a two-part review, we address the question of how the ERK pathway exerts distinct and context-specific effects on multiple processes. We discuss how the dynamics of ERK activity induce selective changes in gene expression programs, with insights from both experiments and computational models. With a focus on single-cell biosensor-based studies, we summarize four major functional modes for ERK signaling in tissues: adjusting the size of cell populations, gradient-based patterning, wave propagation of morphological changes, and diversification of cellular gene expression states. These modes of operation are disrupted in cancer and other related diseases and represent potential targets for therapeutic intervention. By understanding the dynamic mechanisms involved in ERK signaling, there is potential for pharmacological strategies that not only simply inhibit ERK, but also restore functional activity patterns and improve disease outcomes.

Xenografted human iPSC-derived neurons with the familial Alzheimer’s disease APP mutation reveal dysregulated transcriptome signatures linked to synaptic function and implicate LINGO2 as a disease signaling mediator

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Jun 2024
ACTA NEUROPATHOL

Alzheimer’s disease (AD) is the most common cause of dementia, and disease mechanisms are still not fully understood. Here, we explored pathological changes in human induced pluripotent stem cell (iPSC)-derived neurons carrying the familial AD APPV717I mutation after cell injection into the mouse forebrain. APPV717I mutant iPSCs and isogenic controls were differentiated into neurons revealing enhanced Aβ42 production, elevated phospho-tau, and impaired neurite outgrowth in APPV717I neurons. Two months after transplantation, APPV717I and control neural cells showed robust engraftment but at 12 months post-injection, APPV717I grafts were smaller and demonstrated impaired neurite outgrowth compared to controls, while plaque and tangle pathology were not seen. Single-nucleus RNA-sequencing of micro-dissected grafts, performed 2 months after cell injection, identified significantly altered transcriptome signatures in APPV717I iPSC-derived neurons pointing towards dysregulated synaptic function and axon guidance. Interestingly, APPV717I neurons showed an increased expression of genes, many of which are also upregulated in postmortem neurons of AD patients including the transmembrane protein LINGO2. Downregulation of LINGO2 in cultured APPV717I neurons rescued neurite outgrowth deficits and reversed key AD-associated transcriptional changes related but not limited to synaptic function, apoptosis and cellular senescence. These results provide important insights into transcriptional dysregulation in xenografted APPV717I neurons linked to synaptic function, and they indicate that LINGO2 may represent a potential therapeutic target in AD.

A randomized in vitro investigation on the modulation of the TGF-β signaling pathway and induction of apoptosis in colorectal cancer cells by green seaweed Caulerpa racemosa

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Sep 2023

Non-canonical Golgi-compartmentalized Gβγ signaling: mechanisms, functions, and therapeutic targets

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Dec 2022
TRENDS PHARMACOL SCI

G protein Gβγ subunits are key mediators of G protein-coupled receptor (GPCR) signaling under physiological and pathological conditions; their inhibitors have been tested for the treatment of human disease. Conventional wisdom is that the Gβγ complex is activated and subsequently exerts its functions at the plasma membrane (PM). Recent studies have revealed non-canonical activation of Gβγ at intracellular organelles, where the Golgi apparatus is a major locale, via translocation or local activation. Golgi-localized Gβγ activates specific signaling cascades and regulates fundamental cell processes such as membrane trafficking, proliferation, and migration. More recent studies have shown that inhibiting Golgi-compartmentalized Gβγ signaling attenuates cardiomyocyte hypertrophy and prostate tumorigenesis, indicating new therapeutic targets. We review novel activation mechanisms and non-canonical functions of Gβγ at the Golgi, and discuss potential therapeutic interventions by targeting Golgi-biased Gβγ-directed signaling.

Keeping Cell Death Alive: An Introduction into the French Cell Death Research Network

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Full-text available

Jun 2022

Characteristic ERK1/2 signaling dynamics distinguishes necroptosis from apoptosis

Abstract