Benoît Bertrand

Morwen R M Williams

Manfred Bochmann

Gold(III) Complexes for Biological Applications: an Overview

Au(III) complexes have emerge as a particularly promising class of anticancer agents. The development of various types of ligands capable of stabilizing the Au(III) cation in physiological conditions (nitrogen-donor based, dithiocarbamate and cyclometalled ligands) has open the way for the exploration of their mechanisms of action. At the same time, the bioconjugation of Au(III) complexes has emerged as a potential way for improving the selectivity of this class of compounds for cancer cells over healthy tissues.

Introduction

The discovery of the anticancer properties of cis-diamminedichloroplatinum(II) (cisplatin) in the 1960s marked the start of the modern era in the discovery and use of metallodrugs. [1] However, among the thousands of platinum-based compounds tested for anticancer applications, only two of them, carboplatin and oxaliplatin, have reached worldwide approval. [2] Those platinum-based drugs are highly effective chemotherapeutic agents and they are widely used for the treatment of various types of cancer, in particular head and neck, testicular and ovarian cancers. [3] However, despite their clinical success, platinum-based metallodrugs do not specifically target cancer cells but also affect other rapidly dividing cells, such as those found in bone marrow and the gastrointestinal tract. This causes severe side effects which limit the administrable dose to patients. [4] Other drawbacks include acquired or intrinsic drug resistance as well as a limited spectrum of action. [5] All of these factors have driven the interest towards the development of metallodrugs based on other transition metals. [6] The pharmacologic properties of gold have been known since the end of 19 th century, and gold compounds have been tested against different pathologies, even though they are currently only used for the treatment of rheumatoid arthritis. [7] Auranofin, (2,3,4,6-tetra-O-acetyl-1-(thio-κS)--Dglucopyranosato)(triethylphosphine)gold(I), is an orally administrated anti-arthritis drug which has also been studied for its antiproliferative properties. [8] Based on those results, different Au(I) complexes bearing phosphine, [9] NHC, [10] alkynyl [11] and thiolate [12] ligands have been synthesized and tested as prospective anticancer drugs. Their mechanism of action has been investigated and Au(I) complexes are thought to trigger apoptosis via inhibition of selenium-and sulfur-containing enzymes such as thioredoxin reductase (TrxR), glutathione peroxidase, cysteine protease or glutathione-S-transferase. [13] Au(III) has a d 8 electronic configuration and is thus isoelectronic with Pt(II) and, like platinum, forms four-coordinated square-planar complexes, and it was therefore initially assumed that Au(III) would provide a good alternative for Pt(II) and show a similar mode of action as anti-cancer treatment. However, due to the reducing intracellular environment, the tendency of Au(III) centre to be reduced to Au(I) or to Au(0) particles makes the choice of ligands particularly relevant. [14] This review provides an overview and evaluation of the different classes of Au(III) complexes used as prospective anticancer drugs. The most recent evidence of their possible modes of actions will be discussed, along with current strategies aimed at increasing the selectivity of antiproliferative gold complexes towards their cellular targets.

Gold(III) complexes with chelating N-donor ligands

Some of the first gold(III) complexes examined for anticancer applications were pyridine-based complexes [AuCl3(Hpm)] and [AuCl2(pm)] (Hpm = 2-pyridylmethanol). They were tested in vitro against a panel of cancer cell lines and demonstrated comparable activities to cisplatin and NaAuCl4. The compounds were shown to interact closely with proteins such as albumin and transferrin but only weakly and reversibly with DNA. [15] While this shows that gold(III) compounds have potential, the facile substitution of the chloro ligands by water at pH above 2 rendered them too unstable in a physiological environment for medicinal purposes. [15] However, polydentate N-donor ligands successfully stabilize gold(III), such as ethylenediamine (en) (1), phenanthroline (phen) (2), diethylenetriamine (dien) (3), terpyridine (terpy) (4), macrocyclic ligands 1,4,8,11-tetraazacyclotetradecane (cyclam) (5) or aminoquinoline derivatives (6). [16,17] Some of these complexes are depicted in Figure 1. The most toxic compounds against the ovarian cancer cell lines A2780 appeared to be the least stable ones (2 and 5). Their activities were close to those of the respective ligands phen and terpy, which suggests that the toxicity of 2 and 5 may arise from the reduction of Au(III) and the subsequent release of the phen and terpy ligands. [16] The mechanism of action of those Au(III) complexes have been carefully investigated, starting with the potential interaction with DNA due to the electronic similarity with Pt(II).

Although such interactions were shown to be possible, they are weak and reversible, thus ruling out DNA as primary target of that class of complexes. [18] However, it is possible to increase the interaction of Au(III) complexes with DNA by modifying the ligands. Indeed, complex 6, with an aminoquinoline-based ligand, has been demonstrated to interact with DNA via an intercalation mechanism. [17] Complex 2 is a particularly good inhibitor of the water and glycerol channels aquaporins (AQPs). [19] Using homology models, a methionine residue (presenting a thioether functional group) inside the AQP7 channel and a cysteine residue (presenting a thiol functional group) have been identified as the possible interaction points of 2 with AQP7 (Figure 2) and AQP3, respectively. [20] Recently, it has been demonstrated that the coordination of the cysteine residue to Au(III) complexes induces a conformational change of the protein, leading to the shrinkage of the channel and so is mostly responsible for its inhibition. [21] A more detailed theoretical study of the interaction of this class of Au(III) complexes with AQP3 suggests that this interaction occurs after the replacement of a chloride ligand by a hydroxide. The thus generated [(N^N)Au(OH)Cl] + could form a non-covalent adduct with the outer part of AQP3, followed by the coordination of the deprotonated cysteine residue to the [(N^N)Au(OH)] 2+ fragment. This complex is further stabilized by π-stacking interactions from the phenyl ring of a phenylalanine residue with the aromatic rings of the (N^N) ligand. [22] The zinc-finger enzyme poly(ADP)ribose polymerase 1 (PARP-1) has been identified as another possible target of complex 2. According to mass spectrometry measurements inhibition occurs via displacement of zinc upon loss of all ligands coordinated to gold. [23] When reacted with model peptides mimicking (Cys2His2) and (Cys2HisCys) zinc-finger domains, 2 has been shown to displace Zn 2+ with the same efficacy in both cases, leading to the formation of a "gold-finger" while maintaining the +III oxidation state. The change from the tetrahedral environment of Zn(II) to the square-planar geometry of Au(III) led to important conformational modifications (see Figure 3) which are potentially responsible for the inhibition of the enzyme by 2. [24] Interestingly, the reaction of the (N^N^N)AuCl pincer complex 4 with a (Cys4) zinc-finger domain led to fast reduction to Au(I), together with formation of a Cys-Au(I)-Cys moiety and a disulphide bridge. [25] (authorization to be asked).

The dimeric oxo-bridged complex 9 is active in the low micromolar range against cisplatinsensitive (A2780/S) and cisplatin-resistant (A2780/R) ovarian cancer cell lines. Using UV-visible spectroscopy, complex 9 has been shown to be quite stable in aqueous solution, although some aquation leading to the formation of the monomeric bis-hydroxo complex could occur. However, 9 is easily reduced by natural reductants like ascorbic acid or glutathione. [26] Against a panel of 36 cancer cell lines 9 had high activity with a good selectivity score. Using COMPARE analysis, the toxicity pattern of 9 was correlated with one of several histone deacetylase (HDAC) inhibitors. [27] Proteomic studies carried out on A2780 cells using 2D-differential gel electrophoresis (2D-DIGE) showed that upon treatment with 9 only a small fraction of the entire proteome presented a modified expression. Most of these proteins were involved in redox homeostasis, pointing to oxidative stress as the potential primary mechanism of action for 9. Moreover, the close correlation of these results with those obtained with auranofin suggests that 9 might be reduced to a gold(I) species in the intracellular milieu. [28] This is in good agreement with data obtained by spectrophotometric and mass spectrometry techniques with different model proteins like human serum albumin (hSA), cytochrome c (cyt c) and ubiquitin (Ubq). In all cases, the interaction was found to occur via redox processes triggering the destruction of the dimeric architecture. [26] Complexes based on the 2,6-bis(benzimidazol-2-yl)pyridine (HN^N^NH) ligand (8, Figure 1), although perfectly stable in an aqueous medium, were quantitatively and quickly reduced by GSH with release of the HN^N^NH ligand and formation of the corresponding (GS)Au(I)-(NHC) complexes. The free HN^N^NH ligand shows strong fluorescence which is quenched on coordination to a gold(III) cation, so that ligand release can be used to monitor gold uptake into cells. Using fluorescence microscopy, the authors could indeed observe the characteristic blue fluorescence of free HN^N^NH after 10 min of incubation of Hela cells with 8, consistent with the uptake of 8 into the cells and its reduction to Au(I).

The compound with the highest anticancer activity was the one bearing the longest alkyl chain (n = 15), with a cytotoxic activity in the low micromolar range associated with the highest intracellular gold uptake.

Complex 8 reduced the size of Hela xenografts in mice in vivo, making these compounds suitable as dual agents for anticancer and thiol sensing applications. [29]

Gold(III) complexes with porphyrin ligands

Gold(III) porphyrin complexes are a class of compounds characterized by very high redox stability (Ep = -1.00 and -1.48 V vs Cp2Fe 0/+I ) in acetonitrile, but also by their stability in the presence of natural reductants such as gluthatione. The tetraphenylporphyrin complex 9 (Figure 4) was tested against a panel of human cancer cell lines including drug-resistant variants, such as the multi-drug resistant human oral carcinoma (KB-V1) and cisplatin-resistant human nasopharyngeal carcinoma (CNE1). 9 showed submicromolar activity across the board, suggesting a different mechanism of action from cisplatin. [30] In vivo studies on mice showed that 9 reduced nasopharyngeal carcinoma tumour growth without body weight loss or liver injuries. The mechanism of action of 9 involves reduction of mitochondrial membrane potential, which triggers the release of cytochrome c and apoptosis-activating factor leading to apoptosis. [31] The heat-shock protein 60 (Hsp60), an important chaperone involved in the transport and folding of mitochondrial proteins, has been identified as a major molecular target of 9. When replacing the Au(III) cation by a Pt(II) one or using a cyclometalated ligand led to a complete loss of the inhibitory activity, highlighting its dependence of the activity on both the Au(III) ion and the porphyrin ligand. [32] The PEGylated complexes 10a/b (Figure 4) have been shown to self-assemble in aqueous solution into core-shell micelles with diameters in the range of hundreds of nanometers. These structures accumulate faster and more selectively in the cancer cells than non-PEGylated analogues. This may potentially be due to the "enhanced permeability and retention" (EPR) effect (which refers to the higher permeability of cancer cells membranes as the result of their excessively fast development), although that effect is nowadays more and more debated. [33] In acidic conditions, which prevail in the intracellular space between cancer cells in solid tumours, the ester function in complex 10a can be cleaved within few hours, causing the release of the free Au(III) complex, while the amide in 10b is much more stable. Thus both 10a-and 10b-based micelles can enter cancer cells easily, but the release of the active monomeric Auporphyrin fragment is faster and more efficient for the ester 10a. This causes 10a to be much more cytotoxic than 10b. Moreover, the self-assembly capacity of 10a has been used to encapsulate and deliver the clinically-used anticancer drug doxorubicin into cancer cells, including into doxorubicin-resistant cells. [34] Figure 4: Structures of the porphyrin-based gold complexes investigated for anticancer applications.

Gold(III) complexes with dithiocarbamate ligands

Gold(III) dithiocarbamate (DTC) complexes are characterized by two Au-S bounds which stabilize the +III oxidation state, as demonstrated by cyclic voltammetry: 5 mV ≤ Ep(AuDTC) ≤ 125 mV (Ep = peak potential) compared to Ep = 1.29 V vs SCE for KAuX4 (X = Cl, Br) (Figure 5). Complexes 11a/b (Figure 4) have been tested in vitro against a series of human cancer cell lines including leukemia, cervical adenocarcinoma, colon adenocarcinoma and malignant melanoma. They demonstrated cytotoxicities up to nanomolar levels against leukemia (HL60), lymphoma (Daudi) and melanoma (MeWo) cells. This high antiproliferative activity was accompanied by a lack of cross resistance with cisplatin, suggesting a different mode of action. [35] In prostate cancer (PC3) cells in vitro, 11a has been demonstrated to trigger the overexpression of reactive oxygen species (ROS), to inhibit TrxR, and to induce apoptosis via mitochondrial membrane depolarization and activation of caspase-9. [36] Proteasome, an important actor of the protein degradation machinery in cells, has also been identified as a primary target of 11b both in vitro and in vivo. [37] In order to improve the selectivity of gold dithiocarbamate complexes, one strategy investigated involved the coupling of dipeptides to the Au(DTC)X2 scaffold, to facilitate the internalization of the compounds via peptide transporters. To this end, dipeptides were introduced on the DTC ligand to give compounds of type 12 (Figure 5). Complex 12 demonstrated strong antiproliferative activity against a panel of five cancer cell lines expressing the peptide transporters PEPT1 and PEPT2. [38] Cationic [Au(DTC)2] + complexes, e.g. 13, have also been synthesized and tested. They exert very similar cytotoxic activities than the mono-DTC analogues 15. A mass spectrometry study of the interaction of 13 and its mono-DTC analogue with sulfur-containing species revealed that in both cases the cationic [Au(DTC)2] + was bound to cysteine or GSH. This suggests that in all cases the [Au(DTC)2] + cation might be responsible for these interactions. [39] Gold complexes containing cyclometalated 2-phenylpyridine or 2,2′-bipyridyl as well as DTC ligands (Figure 5, 14 and 15) provide other types of coordinatively stable complexes. [40,41] Complex 14 effectively inhibits different deubiquitinases (DUBs). DUBs are proteases responsible for the cleavage of the bond between ubiquitin (Ub) and proteins. Ubiquitination of proteins serves as a "labelling" of proteins for their degradation by the proteasome. Thus, DUBs are involved in different cellular processes including cell cycle, chromatin remodelling or signalling pathways modified in cancer cells such as the ones involving p53 or transforming growth factor-β (TGF-β). [42] Beyond the deubiquitinase inhibition activity, 14 triggers apoptosis and has anti-angiogenesis properties in breast cancer cells MCF-7. [40] Replacing a C^N cyclometalated ligand by N^N = 2,2′-bipyridyl leads to 15, with a cytotoxicity in the sub-micromolar range. The anticancer activity of 15 is p53-independent and is apparently based on the mitochondrial apoptotic pathway. [41] This mechanism seems to be a common feature of the Au(DTC) complexes.

Gold(III) complexes with cyclometalated ligands (C^N)Au(III) complexes

Cyclometalated ligands are chelates with a covalent M-C and a coordinative M-E bond (E = N, O, P, S or Se) and are particularly successful in stabilizing Au(III) against reduction. C^N chelates are most widely used, alongside C^N^N, N^C^N and C^N^C pincer ligands. [43] The high stability of the cyclometalated Au(III) complexes has enabled the study of reaction intermediates, [44] fundamental organometallic reactions, [45] and development of photoemissive complexes. [46] The first report of the use of cyclometalated Au(III) complexes as potential anticancer agents described complexes based on the (C^N) ligand 2-(N,N-dimethylamino)methylphenyl (complexes 16 and 17, Figure 7). [47,48] Those complexes appeared more toxic than cisplatin on a panel of human cancer cells in vitro. The acetato and malonato derivatives (16c and 17b respectively) had the most interesting antiproliferative activity and were able to overcome cross-resistance with cisplatin when tested on ovarian cancer cells sensitive and resistant to cisplatin (A2780 and A2780-R respectively). [48] When tested in vivo on mice bearing tumour xenografs, these complexes had an anti-tumour activity comparable to cisplatin. [47,48] These complexes have been demonstrated to be potent inhibitors of the cysteine protease cathepsin B. [49] The replacement of the N,N-dimethylamino moiety by pyridyl, to give 18 and 19, offers a large palette of derivatization on both the phenyl and pyridine rings, and of the nature of the spacer between the rings (CH2, CR2, O, S, NH, NR, C=O). The thiosalicylato compound 19b has been identified as a particularly potent inhibitor of cathepsin B and K, with in vitro antiproliferative activity in the low micromolar range against a panel of human cancer cell lines. [50] The phosphine containing complex 18c and its dichloro analogue 18a were found to inhibit the zincfinger enzyme PARP-1 at nanomolar levels. [51] Further computational and mass spectrometry investigations have highlighted the ability of 18a to discriminate different zinc-finger domains and to have a higher affinity for the (CysCysHisCys) zinc-finger domain found in PARP-1, over the (CysCysHisHis) zinc-finger domain. Whereas the (N^N) complex 2 reacts with zinc-finger domains with the loss of the N^N ligand, the cyclometalated C^N complex 18a maintains the (C^N)Au(III) motif upon reaction with the zinc-finger domain (Figure 6). [52] This may open the way to the development of highly selective PARP-1 inhibitors. Uptake/efflux studies on precision-cut kidney slices using complex 18c and cisplatin have demonstrated that both active and passive mechanisms are involved and that in the case of 18c the efflux might occur predominantly through active processes. The major involvement of the organic cation transporters (OCTs) and the multidrug and toxin extrusion protein (MATE) transporters into the uptake mechanism of 18c could be ruled out. [53] The more rigid cyclometalated C^N ligand scaffold based on 2-phenylpyridine has also been explored to make anticancer organogold complexes with various ancillary ligands, including chloride, carboxylates, cyclobutanedicarboxylate, sulfur-containing ligands and phenyl derivatives (complexes 20 and 21, Figure 7). These complexes demonstrated antiproliferative properties similar to cisplatin on human leukemia (MOLT-4) and mouse tumour (C2C12) cell lines, with the cyclobutanedicarboxylato complex being the most promising compound of that series. [54] Coupling the lipophilic nature of the (C^N)Au fragment to the hydrophilic character of the biguanide ligand as in 22 led to a compound displaying a high in vitro antiproliferative effect on Hela cells associated with low toxicity towards normal lung fibroblasts. The compound reacts with GSH to form aggregates without reduction of the Au(III) center. Mechanistic studies on 22 pointed to endoplasmic reticulum (ER) swelling triggered by ER-stress induction, as evidenced by the up-regulation of ER-stress markers such as GRP78/Bip, CHOP and HSP70.

22 also displayed anti-angiogenic effects at sub-cytotoxic concentrations. [55] TrxR has also been identified as a possible intracellular target of analogues of complex 23 (Figure 6). Moreover, 23 was shown to increase the amount of intracellular ROS in Hela cells. [56] Imminophosphorane have been used as cyclometalated (C^N) ligands to generate analogues of 24.

The chloro ligands can easily be substituted, e.g. by as dithiocarbamates or phosphines. These compounds appeared particularly efficient against acute lymphoblastic leukemia cells (Jurkat). They are potent TrxR inhibitors and trigger apoptosis via a mitochondria-related pathway involving ROS production and mitochondrial membrane depolarization. [57] The hydrophobic/hydrophilic balance of these systems can easily be tuned by using a more water-soluble phosphine such as the 1,3,5-triaza-7-phosphaadamantane (PTA). [58]

(C^N^N)Au(III) complexes

Compound 25 (Figure 8) bearing a 6-(1,1-dimethylbenzyl)-2,2′-bipyridine ligand was shown to be stable toward reduction even in the presence of reductants like sodium ascorbate, while the (N^N)Au(III) (based on bipy or phen ligands, figure 1) were easily reduced under the same conditions. [16,59] 25 has been shown to be highly cytotoxic against A2780 ovarian cancer cells despite a weak interaction with DNA. Its mechanism of action is likely due to inhibition of enzymes via the direct interaction of the (C^N^N)Au(III) fragment with different proteins and enzymes, without reduction of the Au(III) cation. Such interactions have been described with bovin serum albumin (BSA), [60] cytochrome c, lysozyme [61] and the enzyme thioredoxin reductase. [62] Using mass spectrometry techniques, the preferred interaction of 25 with selenocysteines over histidine, cysteine, methionine and glutamate has been highlighted, along with the weak interaction with 9-ethylguanine and guanine-based nucleotides. This correlates with TrxR being a major target of 25. [63] Several lines of evidence point towards the involvement of the copper trafficking system into the uptake mechanism of gold complexes. Indeed, 34 has been shown to form stable adducts with the copper chaperone Atox-1 and to compete with the binding of copper(I). [64] 25 has also been identified as an inhibitor of the transmembrane transporter Na + /K + -ATPase [65] and the 20S proteasome sub-unit. [66] In order to observe the effects triggered by 25 on the expression of the proteins of a whole cell, proteomic analyses have been performed using 2D-DIGE methodologies. When comparing samples of A2780 ovarian cancer cells treated with 25 to samples of the untreated cells, the expressions of only few proteins were found to be modified (up-or down-regulated). These proteins could be identified by MALDI-TOF mass spectrometry techniques. [67] Most of the over-expressed proteins were shown to belong to the 'stress response and chaperones' functional class. The down-regulated proteins appeared to be involved in the glucose metabolism pathway and more generally speaking in energy production.

Treatment with 25 therefore seems to trigger cellular stress via the down-regulation of the glucose metabolism pathway.

Complex 26 (Figure 8), the xylidine analogue of 25, showed improved antiproliferative activity compared to 25 against A2780 and MCF-7 cells. [68] The COMPARE analysis of the results from a panel of 36 cell lines suggested the inhibition of mammalian target of rampamycin (mTOR), proteasome and DNA synthesis as potential mechanism of action. [27] TrxR has been identified as another potential target of 26 through the interaction with the cysteine and selenocysteine residues. [69] The dimeric oxo-bridged compound 27 (Figure 8) is reversibly hydrolysed to give two equivalents of 25. The latter has been identified as the binding fragments with various proteins (lysozyme and cytochrome c). [70] In the same way, hydrolysis of 27 to 25 leads to the stabilization of the non-canonical Gquadruplex structure of DNA. [71]

(C^N^C)Au(III) complexes

Pincer complexes (C^N^C)Au(III)X (X = halide, N-, C-or P-donor) based on the 2,6diphenylpyridine ligand, such as 28 (Figure 9), have shown promising anticancer properties in vitro. [72] This ligand platform is very stable and synthetically versatile and has opened the way to a large series of derivatives of type 29 with different ancillary ligands, including 1-methylimidazole, pyridine, triphenylphosphine, PTA and thio-β-D-glucose tetraacetate. [72,73] Modifications of the pincer ligand has also been explored by benzo-annelation of the phenyl rings (as in 2,6-dinaphthylpyridine) or by introducing another aromatic substituent on the pyridine (2,4,6-triphenylpyridine), although these modifications do not seem to significantly modify the antiproliferative properties. On the other hand, changing the ancillary ligands X seems to have a major impact on the biological properties of the complexes. The N-methylimidazole in 29 (R =H) enhances the ability of the complex to intercalate with double-stranded DNA and to interact with G-quadruplex DNA, making DNA a potential target for that compound, whereas the phosphine-containing analogues interact only weakly with DNA. In the same way 29 (L = N-methylimidazole) blocks the cell cycle in the S-phase i.e. when the DNA has been replicated and so when the amount of DNA is the highest, while the dimeric complex 30 was unable to do so even after 48 h of incubation. [72] Figure 9: Examples of cyclometalated (C^N^C)Au(III) complexes investigated for anticancer applications.

(C^N^C)Au complexes of bridging 1,3-bis(diphenylphosphino)propane (30, Figure 9) proved particularly effective in vitro against human hepatocellular carcinoma and human cervical epithelial carcinoma cells. [72,74] However, the cytotoxicity of these complexes appeared to be very close to that of the free diphosphine ligands themselves, which suggests that the complexes may simply act as carriers of the cytotoxic ligands. Based on that finding the more stable NHC analogues have also been tested and have shown much lower toxicity. Ligand lability therefore seems important for antiproliferative activity. [72,74] When tested in vivo on nude mice bearing hepatocellular carcinoma xenografts, 30 could reduce tumour growth more efficiently than cisplatin or doxorubicin, while no side-effects were observed. 30 has been shown to very effectively inhibit TrxR and induce ER stress leading to apoptosis. [74] The [(C^N^C)Au(NHC)] + complex 31 inhibits topoisomerase 1 (Topo1), an enzyme responsible for the unwinding of chromosomal DNA, by stabilizing the cleavable topo1/DNA complex with higher efficacy than the known Topo1 poison comptothecin. This stabilization of the cleavable topo1/DNA complex prevents the relegation after unwinding, leading to DNA strand breaks. [75] Complex 31 proved to be highly toxic against various human cancer cell lines in vitro and in vivo in nude mice bearing hepatocellular carcinoma xenografts (IC50 ~ 0.2 to 1.2 µM). [75] To gain a broader picture of the mechanism of action of this class of [(C^N^C)Au(NHC)] + complexes, 32 has been developed. This compound presents two complementary functional groups: a photolabile diazirine unit, and an alkynyl grafting function. When irradiated with UV light (λ = 365 nm), the diazirine releases N2 and covalently binds with its closest neighbour, which will be the biomolecule with which it interacts. Then the alkynyl group was used to graft an azido-biotin fragment by the usual click-reaction, which enables the detection of 32/protein conjugates with streptavidin-peroxidase. [76] Only six proteins were observed to be bound to 32; these were identified as heat shock protein 60 (HSP60), vimentin (VIM), nucleoside diphosphate kinase A (NDKA), nucleophosmin (NPM), nuclease-sensitive element binding protein (Y box binding protein, YB-1), and peroxiredoxin 1 (PRDX1) which are all reported possible anti-cancer targets. When the same experiment was carried out with Hela, NCI-H460 and HCT116 the same six proteins were identified. Proteomic analysis of Hela cells highlighted the modulation of the eukaryotic initiation factor 2 (eIF2) signalling pathway involved in the synthesis of proteins upon treatment with 32. Proteomic data are in good agreement with the identified targets since VIM, NPM and YB-1, three of the identified protein targets, are involved in eIF2 signalling pathway modulation. [76] Interestingly, proteins involved in protein synthesis have been similarly found to be down-regulated upon treatment with the (C^N^N)Au(III) complex 25 (Figure 8), pointing toward a general mode of action for cyclometalated Au(III) complexes. [67] Replacing the central pyridine ring by a pyrazine enhanced the photoemissive properties of the corresponding (C^N^C)Au(III) complexes and increases the tendency toward π-stacking and Hbonding. [77] This scaffold has been used to develop a library of complexes with various ancillary ligands including NHCs and the anti-cancer drug thioguanine (complexes 33 and 34). [78] The cytotoxic profile of complex 34 was very similar to that of the free thioguanine, suggesting that, as was the case with 30, the gold complex may act merely as the carrier of the antiproliferative agent.

The benzimidazol-2-ylidene complex 33 was found to be highly active in vitro against a panel of human cancer cell lines (IC50 between 0.3 and 8 µM). Interestingly, the caffeine-based analogue of 33 appeared 10 times less toxic on all studied cells lines. These differences in activity could be rationalized by reduced intracellular gold uptake, as measured by inductively coupled plasma-mass spectrometry (ICP-MS) techniques, which suggests that uptake occurs by passive diffusion across the cell membrane which is impeded by carbene ligands with very polar structural elements. The polarity and degree of functionalisation of the NHC ligands are therefore important for the anticancer properties. [78] Compound 33 proved to be active also against cisplatin-resistant cell lines, and it could be shown that 33 is resistant to reduction or substitution by GSH over a period of several days. There are several potential modes of action for such a complex; for example, 33 is able to stabilize selectively both the Gquadruplex and the i-motif structures of DNA, compared to the canonical double helix DNA structure, and it disrupts the protein-protein interaction between MDM2 and p53. [78] Indeed, using biochemical and molecular docking techniques and saturation transfer difference NMR spectroscopy, 33 has been shown to interact with MDM2 on the p53 interaction site as depicted in Figure 10. The concept of multinuclearity, i.e. the incorporation of several metal centres into a single molecule, has shown some promising results in terms of increasing the cytotoxicity of the compounds [79] or enabling their cellular localization (i.e. synthesis of theranostics) by incorporating a luminescent or radioactive metal. [80] Using an NHC ligand bearing a pentafluorophenol ester moiety has enabled a postmetalation introduction of a second metal complex with high yield and selectivity. [81] A similar strategy has been used to synthesize the series of homobimetallic Au(III)/Au(I) complexes 35 with different alkyl spacers between the two gold cations. [82] The complex with the longest linker showed activity only against the two breast cancer cell lines (MCF-7 and MDA-MB-231) but also showed a reduced activity against healthy fibroblasts. As for the monometallic complex 33 the differences in activity between the cell lines seem to be linked to differences in intracellular gold uptake. Compared to 33, the presence of the NHC-AuCl moiety in 35 did not decrease the interaction of this compound with the G-quadruplex structure of DNA, which suggests that the Au(I) fragment does not prevent the recognition of the (C^N^C)Au(III) moiety by cellular targets. [82] On the contrary, although NHC-AuCl are not reported to stabilize the Gquadruplex structure of DNA, [83] the introduction of the Au(I) fragment increased the interaction of 35 with the G-quadruplex structure of DNA compared to the Au(III) complex with the pending amino linker. [82] In order to enhance the selectivity of metal-based drugs toward cancer cells, a particular promising strategy appears to be the conjugation of the metal complex with a vector molecule which can be recognized by receptors and transporters that are over-expressed by cancer cells. This vector can be of different types: peptides, sugars, vitamins or aptamers. [84] However, the bioconjugation can be difficult to carry out and may lead to overall poor synthetic yields. This can limit the number of permutations that can be explored in terms of metal-based compounds, spacers and vectors. Thus, it is important to develop simple, high yielding and versatile bioconjugation methods. Reacting nucleophiles with an electrophilic isocyanide complex [(C^N^C)Au(C≡N-R)] + leads to the formation of the corresponding acyclic carbene complexes of type 36, as shown in Figure 11. The reaction has been tested with success with amines and amino esters to generate a series of complexes 36. [85] The compounds were tested against a panel of cancer cells and showed higher activity than cisplatin against A549, MCF-7 and MDA-MB-231. However, these acyclic carbene complexes proved more sensitive to reduction by GSH than the related complex 33 bearing a cyclic carbene ligand. Since 36 did not induce overproduction of ROS, interference with the mitochondrial redox activity was ruled out as potential mechanism.

Attaching a biotin fragment to the [(C^N^C)Au(NHC)] + backbone (complex 37, Figure 11), enabled its binding to avidin. The natural photoluminescence of the 38/avidin conjugate was enhanced by a factor of 45 in the presence of aggregated bovine serum albumin (BSA) among other proteins, while the presence of single stranded DNA among other DNA structures increased the luminescence intensity 30fold, making it a selective detector for those biomolecules. [86] Both 37 and the 37/avidin conjugate were more active than cisplatin against Hela, HepG2 and MDA-MB-231 cells. The 37/avidin conjugate was shown to trigger apoptosis without inducing generation of ROS. [86] Following the methodology developed for the synthesis of the homobimetallic complexes 35, the conjugation of biotin and 17α-ethynylestradiol via different alkyl spacers has been achieved in high yields leading to complexes 38 and 39, respectively. [82] Incubation of MCF-7 cells, expressing both biotin receptors (BR) and estrogen receptors (ER), with 38 and 39 led to a higher intracellular gold uptake compared to the non-conjugated complex 33. Moreover, the amount of intracellular gold was reduced in cells that did not express the biotin and estrogen receptors. This suggests that the two biomolecules could selectively deliver the [(C^N^C)Au(NHC)] + fragment to cancer cells provided these express the corresponding receptors. However, in spite of the increased metal uptake, the cytotoxic properties of 38 and 39 were reduced compared to the non-conjugated complex 33. When tested against a potential intracellular target of the pharmacophore 33, 38 and 39 showed no interaction with the DNA Gquadruplex structures. The results indicate that while vectors covalently bound to a [(C^N^C)Au(NHC)] + backbone can increase the uptake of antiproliferative agents their also disturb the ability of the metal-based pharmacophore to interact with its target resulting in an overall reduced activity. [80] Thus linking the vector to the [(C^N^C)Au(NHC)] + backbone via a "self-immolative" spacer, so that the gold fragment can be liberated from the vector after entering the cell may open the way to highly effective and selective gold-based anticancer therapy.

Conclusion

Although gold in the oxidation state +III in a physiological medium is often easily reduced to gold(I) or gold nanoparticles, the development of chelating ligands with nitrogen donors, cyclometalated structures and dithiocarbamates has opened the way to enable Au(III) complexes to be used for chemotherapeutic purposes. The mechanisms by which these compounds are able to exert antiproliferative effects are slowly beginning to emerge. Different trends have been highlighted, although details remain very sketchy. Improving the selectivity of the compounds for cancer cells over healthy tissue in order to limit the side effects remains a major challenge, although some promising results have been obtained.

More sophisticated synthetic approaches are required to fully utilise the potential of Au(III) complexes for the development of new generations of anticancer drugs.

Figure 1 :

 1 Figure 1: Examples of Au(III) complexes with chelating N-donor ligands investigated for anticancer applications.

Figure 2 :

 2 Figure 2: Molecular docking of complex 2 (green) inside the channel of human AQP7 (grey). Reproduced from ref. 20a with permission from the publisher. (authorization to be asked).

Figure 3 :

 3 Figure 3: Overlay of the calculated structures of the (Cys2His2) zinc-finger domains in the presence of Zn(II) (brown) and Au(III) (blue) cations. Reproduced from ref. 24 with permission from the publisher.

Figure 5 :

 5 Figure 5: Examples of Au(III) dithiocarbamate complexes investigated for anticancer applications.

Figure 6 :

 6 Figure 6: Calculated structure of the most stable isomer due to the reaction between the (Cys2HisCys) zinc-finger domain of PARP-1 and complex 18a. Reproduced from ref. 52 with permission from the publisher. (authorization to be asked).

Figure 7 :

 7 Figure 7: Examples of cyclometalated (C^N)Au(III) complexes.

Figure 8 :

 8 Figure 8: Cyclometalated (C^N^N)Au(III) hydroxide and amide complexes.

Figure 10 :

 10 Figure 10: Representation of 33 (gray) docked in MDM2 (green). Reproduced from ref. 78 with permission from the publisher. (authorization to be asked).

Figure 11 :

 11 Figure 11: Examples of cyclometalated (C^N^C)Au(III) complexes conjugated to biomolecules.


Stéphane Verger
email: stephane.verger@ens-lyon.fr

Guillaume Cerutti

Olivier Hamant

An Image Analysis Pipeline to Quantify Emerging Cracks in Materials or Adhesion Defects in Living Tissues

Keywords: Cracks, Cell adhesion, Image analysis, Mechanical properties, Tension, Github

Microcracks in materials reflect their mechanical properties. The quantification of the number or orientation of such cracks is thus essential in many fields, including engineering and geology. In biology, cracks in soft tissues can reflect adhesion defects, and the analysis of their pattern can help to deduce the magnitude and orientation of tensions in organs and tissues. Here, we describe a semiautomatic method amenable to analyze cell separations occurring in the epidermis of Arabidopsis thaliana seedlings. Our protocol is applicable to any image exhibiting small cracks, and thus also adapted to the analysis of emerging cracks in animal tissues and materials.

when repeated stress is applied or when the temperature fluctuates, causing material fatigue and eventually, failure. Microcracks can reveal the magnitude and direction of the principal stresses that the material is experiencing. This property is widely used in mechanical engineering and geology (e.g., [START_REF] Kowallis | Microcrack study of granitic cores from Illinois deep borehole UPH 3[END_REF][START_REF] Kranz | Microcracks in rocks: A review[END_REF][START_REF] Joseph | Environmental effects on the degradation behaviour of sisal fibre reinforced polypropylene composites[END_REF]. Microcracks are also present in biological structures, like bones. As in any material, they can lead to rupture (fatigue fracture). Such cracks also trigger signaling cascades involving osteoblasts and osteoclasts, resulting in bone remodeling (e.g., [START_REF] Mori | Increased intracortical remodeling following fatigue damage[END_REF]. Cracks can also be observed in soft tissues, notably as a result of cell to cell adhesion defects. This is particularly obvious in plant tissues, where cells do not migrate or intercalate (e.g., [START_REF] Bouton | QUASIMODO1 encodes a putative membrane-bound glycosyltransferase required for normal pectin synthesis and cell adhesion in Arabidopsis[END_REF]. Although many tools have been developed to study cracks in material sciences, they are often adapted to analyze long and thin cracks (e.g., [START_REF] Griffiths | Quantification of microcrack characteristics and implications for stiffness and strength of granite[END_REF]; they have not been customized for the analysis of emerging cracks in soft biological tissues. Here we describe a pipeline that detects and analyzes such cracks.

Our pipeline is based on a script that segments regions exhibiting a clear-cut pixel intensity contrast corresponding to cell separations or cracks. In our original publication, we stained Arabidopsis thaliana seedlings with propidium iodide (see [START_REF] Verger | A tension-adhesion feedback loop in plant epidermis[END_REF] for the staining procedure), a fluorescent molecule that specifically binds to pectins in the cell wall. After washing the dye off, the cell contours are clearly marked. In a wild-type plant, this reveals a continuous epidermis where cells are fully attached to one another. However, when performing the same staining on a mutant with cell adhesion defects, holes between cells are revealed: a bright signal between cells marks emerging separations between these cells [START_REF] Verger | A tension-adhesion feedback loop in plant epidermis[END_REF]. The contrast between the cells and the cracks is in fact strong enough to detect and quantify cell separations. After segmenting these cracks, a principal component analysis is performed on each of these segmented areas, yielding various information: area of the crack as well as its principal orientation (angle of the crack) and the shape anisotropy (derived from the eigen values and vectors calculated in the principal component analysis of the crack shape). These data can then be compared for multiple samples and sample series. In principle, other staining method may be used on any types of tissues or materials, as long as the contrast is strong enough for the script to detect the cracks. Note: See "Installation procedure" in "Procedure" for the installation of Software 2 to 5.

Software

Procedure

A. Image acquisition

Our image analysis pipeline was developed to detect and quantify cell separations in plant epidermis. Such images can be obtained using a confocal microscope and by either staining the cell wall or cell contour with a fluorescent dye, or by imaging plants expressing a fluorescent reporter of the cell contours (typically, a protein at the plasma membrane). Z-Stacks can be obtained and projected in 2D (e.g., using Fiji, max intensity). It is however crucial to obtain images in which there is a strong contrast between the cells and the "cracks" (i.e., the zone where cells are separated. In Figure 1C the regions corresponding to cells are of comparable pixel intensity as the gaps between cells, making it impossible to automatically distinguish them). Furthermore, because our pipeline works by segmenting the whole cracks, the cracks need to form a closed domain (For example, in Figure 1D, the joints marking the cracks (white zones), overlap with one another, making it impossible to distinguish them individually with our pipeline). Thus our pipeline can in principle work with any 2D grayscale image containing clear-cut closed cracks (see examples of suitable and non-suitable images in Figure 1). (plasma membrane reporter) cotyledon epidermis (see [START_REF] Verger | A tension-adhesion feedback loop in plant epidermis[END_REF]. Although such fluorescent reporter can provide suitable images for our analysis, in this particular case the contrast between the cracks and the cell content is too low to allow a segmentation of the cracks with our pipeline. D. Picture of cracks in rocks (credit photo: Pierre Thomas). In this case the cracks do not form closed domains as most of them overlap. In addition, the pixel intensity is very variable throughout the picture such that some cracks do not exhibit a strong differential in pixel intensity. White arrowheads point to examples of cracks or cell separations in these images.

B. Prerequisite: Image quality, preprocessing and threshold for "crack detection" in Fiji

In order to determine if your images are suitable for this image analysis pipeline you need to make sure that the cracks will be properly segmented by pixel intensity:

1. Load your 2D image in Fiji (Software 1).

2. Change your image type to 8-bit. Image > Type > 8-bit (Figure 2B) and/or pick the right channel from a multichannel (e.g., RGB) image (Image > Color > Split channels).

3. Optionally you may enhance the contrasts of your image using the "Enhance Contrast..." function (Process > Enhance Contrast…> Set "Saturated pixels" to 0.3%).

Note: You may use a different approach (e.g., increase exposition time or laser intensity during

image acquisition) to obtain enough contrast to segment the cracks.

4. Smooth your image with a median filter to remove noise (Process > Filters > Median… [Figure 2C]). Set the radius to a suitable value to reduce the noise, without blurring the image too much.

Here again, you may also use a different approach to reduce the noise of your image, as long as in the end, you obtain a smoother detection of the cracks.

5. Using the "Threshold..." tool, determine the suitable threshold that best separates the cracks from the surrounding regions (Figures 2D and2E).

CRITICAL STEP: Depending on the nature and quality of your image, it may be difficult or impossible to segment the cracks based on a pixel intensity threshold. If most of your images are in this situation, this image analysis pipeline is not adapted to your study.

6. If you are able to properly separate the cracks from the surrounding regions, you may proceed add "_XXXthld" at the end of the name (where XXX is the threshold value previously determined in Step B5 as suitable to segment the cracks (e.g., "sample_1_162thld.tif". See Figures 2D-2F).

The value before "thld" is the threshold value that will be used for the image segmentation later on (it has to be three digit long).

7. You can pre-process as many images as you need before going further with the analysis. They will all be automatically processed if they are grouped in a folder. Note that in order to quantify and compare cracks orientation in multiple images, the images should be in a consistent orientation. The corresponding arborescence has to be organized as follows: A "main" directory (later on referred as "updir" in the script), containing subdirectories (e.g., different mutants or growth conditions), each containing all the corresponding images (Figure 2F). During the installation (LINUX, MAC and WINDOWS) you will be asked a number of choices.

You can set the directory of your choice when asked (e.g., ~/.miniconda). Make sure to answer YES when asked to add conda to your PATH.

At this point, you should have miniconda installed. Test your installation by closing your current

terminal window and running conda in a new terminal to make sure the command is found: conda 4. Download and extract the "cell_separation_analysis" repository from Github (following the link in Software 5, or here). On the Github page, click on the "Clone or download" green button at the right of the page. Then download and extract the zip.

Note: This folder contains the script ("Cell_separation_analysis.py"), a dependencies installation file for miniconda ("cell-sep-env.yml") and test sample images ("Test_files").

5. In a terminal, navigate to the "/cell_separation_analysis-master" folder that you have extracted.

cd path/to/Cell_separation_analysis-master/ Note: Input the actual path that leads to the folder "/Cell_separation_analysis-master". This should run the script and generate its output in the "/Test_files" folder of "/Cell_separation_analysis-master".

D. Running the script

In order to run the script with your own images, you need to edit the "parameter" section of the script.

To do so, open the "Cell_separation_analysis.py" file in a text editor. Scroll down to the section called "parameters" where there are 7 entries that you may modify (see Figure 3): 1. Set the directory where you placed the images to analyze. Remember that your folder has to be organized in a specific way: A "main" directory corresponding here to "updir", containing subdirectories (e.g., different mutants or growth conditions), each containing all the corresponding images (Figure 2F). You can either enter the full path to the directory (e.g., /Home/Path/to/updir/), or simply enter ./updir/ (where you replace "updir" by the actual name of your folder). In the latter case, you will have to navigate to the parent folder of your "updir" in Ipython before running the script (as described in Step C8).

2. Set the pixel size. This is required in order to perform analyses of crack area. Usually, for confocal images this information can readily be found by loading the original image in Fiji and looking at its properties (Image > Properties… > Pixel width or height). The unit of length should be micron. For other types of images, you will need to determine the actual pixel size, for example in Fiji, using an internal scale (Analyze > Set Scale…).

3. Determine the minimum and maximum area of crack. This step corresponds to a filter as it eliminates areas which are too small ("min_area_of_crack", e.g., background noise) as well as the global background of the image ("max_area_of_crack", e.g., the empty space around the tissue). These values are in pixel number. You may have to try several times with different values in order to determine the right parameters empirically.

4. Set the threshold type. The cracks can be of either a higher or lower pixel intensity than the surrounding region. Thus the threshold can be set as "min" or max". The "max" will detect and segment zones with lower signal intensity (i.e., the crack is darker than the surrounding region) and the "min" will detect and segment zones with higher signal intensity (i.e., the crack is lighter than the surrounding region). "Data analysis" section to determine if you should run these analyses. These are by default set to "False" which mean they will not be performed. Replace "False" by "True" if you want them to be performed.

6. Once all the parameters are correctly set, save the script and run it as described in Step C8.

Data analysis

1. As explained in the background section, this script can generate different outputs. From the detected cracks in each image, the script will generate three images: a pixel intensity inverted version of the image, the same image with an overlay of the segmented areas, and the same image with an overlay of the anisotropy and principal angle of the area, directly saved as vectorial PDFs. It will also generate a .csv file containing, for each segmented cracks, the label number, center position, area in pixels and micrometer square, the main orientation (angle) of the crack, the shape anisotropy, the eigen values and vectors. Finally for each image, a polar histogram representing the distribution of crack orientations in the image is created and saved as a vectorial PDF (see output generated in the test of the script in the previous step and Figure 4). It will generate a polar histogram representing the distribution of cracks orientation for all the images of a sample series pooled together, and save it as a vectorial PDF. It will also output a .txt file containing for each image the circular mean angle (between 0° and 180°), the resultant vector length (an estimation of the coordinated directionality of the cracks, between 0 and 1; a value of 0 means that the crack orientations are homogeneously distributed whereas, a value of 1 means that all the cracks have the same orientation) and the mean anisotropy of the crack shapes. At the end of the text file, a global analysis of all the images of a sample series is generated: circular mean angle, resultant vector length and shape anisotropy for the pooled values of all the images of the sample series. It will also output the result of an RAO's spacing test, which assesses whether the angles are uniformly distributed (statistically no preferential angle orientations), or if there is a significant angular bias.

3. The script also offers the possibility to compare different type of samples. You can compare the total area of the cracks in two sets of samples (e.g., 10 images of mutants 1 compared with 10 images from mutants 2) (see Step D5, "Global_Output_Size = True"). This will run statistical tests on the compared samples to determine if the average area of cracks in images of one sample series is different from that of another sample series. The choice of statistical test depends on the normality and the variance of the data. First, a Shapiro's test for population normality is run on both sample series. If at least one of the sample series does not have a normally distributed population, a non-parametric Wilcoxon rank sum test is run to test whether the samples are statistically different. Otherwise, if both sample series are normally distributed, a Bartlett's test for equal variances is run. If both sample series have equal variance, a Student's t-test is run and otherwise a Welch's t-test is run to test whether the samples are statistically different. A summary of these tests is saved as a .txt file, and a boxplot of this comparison is saved as a vectorial PDF.

4. You may then perform further analyses depending on your needs, using the different output files containing all the raw data.

Notes

1. The most critical step in this protocol is to check the suitability of your images as described in "Procedure B". If most of your images do not pass this test, this image analysis pipeline may not

Figure 1 .

 1 Figure 1. Suitable image type. A. Z-projection (maximal intensity) of a confocal image stack from a propidium iodide stained light-grown hypocotyl from the qua1-1 mutant with cell adhesion defects (see Verger et al., 2018). B. Cracks in rocks (credit photo: Pierre Thomas). In A and B the cracks are marked by a high pixel intensity contrast and form closed domains. C. Zprojection (maximal intensity) of a confocal image stack from a qua1-1 pPDF1::mCit:KA1

Figure 2 .

 2 Figure 2. Image preprocessing in Fiji. A. Z-projection (maximal intensity) of a confocal image stack from a propidium iodide stained light-grown hypocotyl from a qua1-1 mutant with cell adhesion defects (see Verger et al., 2018). B-C. Images are preprocessed in imageJ: The image is converted to 8-bit (B) and a median blur with a radius of 3 is applied to reduce the noise and ease the segmentation (C). D-E. The threshold tool is used to determine the suitable threshold for segmentation in the pipeline. (D) Red zones will be segmented as cracks. (E) The threshold is adjusted in order to segment the cracks properly (e.g., here to a value of 162). F. Example of file arborescence required for the pipeline to process the image series. G. Z-projection (maximal intensity) of a confocal image stack from a propidium iodide stained light-grown hypocotyl from a wild-type seedling showing no cell adhesion defects and thus not suitable to detect cracks with our pipeline (see Verger et al., 2018). H-I. Because the differences in pixel intensity are locally small (unlike the qua1-1 mutant with bright cell separation signals in panel A-D), we are unable to segment properly the image either with a threshold value of 50 (H) or 45 (I) as an example.

6 .

 6 In the same terminal, create a new conda environment using the provided YAML file that lists all the software dependencies: conda env create -f cell-sep-env.yml Note: This will install Python and all the required dependencies. It may take a few minutes to complete the installation. 7. When the installation is complete, in the same terminal, activate the environment: source activate cell-sep-env Note: You will have to run this command every time you want to use the cell separation analysis program, just after opening a new terminal window. 8. You can then check your installation and whether the script runs properly by running the script on our test images. In a terminal, run: ipython This will launch ipython. In ipython, navigate to the "/cell_separation_analysis-master" folder that you have downloaded: cd path/to/Cell_separation_analysis-master/ In the python console, type: %run Cell_separation_analysis.py

Figure 3 .

 3 Figure 3. Parameter settings. Cell_separation_analysis.py Python script opens in a text editor, displaying the "parameters" section. The parameters can be modified according to your own requirements and the file can be saved before running the script.

5 .

 5 You may then decide to run more global analyses if you have multiple sample types ("Global_Output_Size") and multiple images by sample type (Global_Polarhist_output). See www.bio-protocol.org/e3036 Vol 8, Iss 19, Oct 05, 2018 DOI:10.21769/BioProtoc.3036 Copyright Verger et al. This article is distributed under the terms of the Creative Commons Attribution License (CC BY 4.0).

Figure 4 .

 4 Figure 4. Output of the cell separation analysis pipeline. A. Pixel intensity inverted version of the image (Figure 2C). B. Same image as in (A) with an overlay of the segmented areas that are identified and labeled using different colors to ease visualization. C. Same image as in (B) with a representation of the vectors resulting from the principal component analysis of the crack shapes (Red crosses). To improve the visual output, the eigen vector that are mapped on the images are multiplied by a factor 2 and by the square root of the corresponding eigen value. D.

Acknowledgments

This work was supported by the European Research Council (ERC-2013-CoG-615739 ''MechanoDevo''). We would like to thank Pierre Thomas (Professor at ENS de Lyon) for providing us the pictures in Figures 1B and1D. This protocol was adapted from the published study (Verger et al., 2018).

be suited for your study. Conversely, it is important to realize that it is also rare to be able to segment 100% of the objects that you would identify as cracks visually. You should then decide what is an acceptable yield in your case.

2. Following our recommended installation procedure you should in principle be able to run our image analysis pipeline on any system (LINUX, MAC, and WINDOWS). However, the script has so far only been tested on a computer running Ubuntu 14.04 and Python 2.7. We recommend testing the macro with our sample images (Step C8) and check if the output images look similar to what is reported in our original publication [START_REF] Verger | A tension-adhesion feedback loop in plant epidermis[END_REF].

Competing interests

The authors declare no conflict of interest or competing interests.