Hadrien De Blander

Laurie Tonon

Frédérique Fauvet

Roxane M Pommier

Christelle Lamblot

Rahma Benhassoun

Francesca Angileri

Benjamin Gibert

Maria Ouzounova

Anne-Pierre Morel
email: annepierre.morel@lyon.unicancer.fr

Alain Puisieux
email: alain.puisieux@curie.fr

RAS-induced transformation of mammary epithelial cells relies on ZEB1-dependent cellular reprogramming via a paracrine process

While uncommon in breast cancers, oncogenic activation of the RAS/MAPK signalling pathway is frequent in claudin-low (CL) tumours, a subtype of breast malignancies enriched in features of epithelial-mesenchymal transition (EMT), suggesting an interplay between RAS activation and EMT. Using inducible models of human mammary epithelial cells, we show that RAS-mediated transformation relies on cellular reprogramming governed by the EMT-inducing transcription factor ZEB1. The path to ZEB1 induction involves a paracrine process: cells entering a senescent state following RAS activation release proinflammatory cytokines, notably IL-6 and IL-1α which promote ZEB1 expression and activity in neighbouring cells, thereby fostering their malignant transformation. Collectively, our findings unveil a previously unprecedented role for senescence in bridging RAS activation and EMT over the course of malignant transformation of human mammary epithelial cells.

Introduction

The development of cancer is viewed as an evolutionary process, in which the malignant transformation of a normal cell involves a number of limiting genetic and epigenetic events 1 .

Among the most frequently activated oncogenes in cancer are the proto-oncogenes of the RAS family (KRAS, NRAS and HRAS), found mutated in 19% of all cancers [START_REF] Prior | The Frequency of Ras Mutations in Cancer[END_REF] . Nevertheless, a strong heterogeneity in the incidence of RAS mutations is observed according to the type of cancer. Being most prevalent in pancreatic cancer, colorectal cancer and lung adenocarcinoma, RAS appears to be seldom mutated in liver or breast cancers. This difference in RAS mutation frequency among cancer types suggests a cellular state of predisposition to withstand RAS transformation [START_REF] Puisieux | Cellular Pliancy and the Multistep Process of Tumorigenesis[END_REF] . Interestingly, although the RAS pathway is infrequently altered in breast cancers, our recent work has revealed that activation of RAS is in fact the most common genetic alteration found specifically in the "claudin-low" (CL) molecular subtype of breast tumours [START_REF] Pommier | Comprehensive characterization of claudin-low breast tumors reflects the impact of the cell-of-origin on cancer evolution[END_REF] .

CL tumours correspond to a rare form of triple-negative tumours that are highly enriched in mesenchymal traits and stem cell features [START_REF] Prat | Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer[END_REF][START_REF] Morel | EMT inducers catalyze malignant transformation of mammary epithelial cells and drive tumorigenesis towards claudin-low tumors in transgenic mice[END_REF][START_REF] Visvader | Mammary stem cells and the differentiation hierarchy : current status and perspectives[END_REF] . We and others have recently reported the diversity of CL tumours in terms of their genomic composition, gene expression and methylation patterns as well as the repertoire of active biological pathways [START_REF] Pommier | Comprehensive characterization of claudin-low breast tumors reflects the impact of the cell-of-origin on cancer evolution[END_REF][START_REF] Fougner | Claudin-low-like mouse mammary tumors show distinct transcriptomic patterns uncoupled from genomic drivers[END_REF] . We have further demonstrated that this diversity reflects the impact of the cell origin on the evolutionary process of these tumours. Indeed, a subgroup of CL tumours, called CL1, designated by a low level of genomic aberrations and a prominent stemness signature, appears to develop from the malignant transformation of a normal human mammary stem cell (MaSC) that has inherent mesenchymal features, while two others subgroups, CL2 and CL3, defined by lower stemness capacity and moderate to high levels of genomic aberrations, derive from luminal or basal-like breast cancers, respectively through an epithelial-mesenchymal transition (EMT) in response to microenvironmental signals and/or the gradual acquisition of oncogenic events (Pommier et al., 2020). Notably, all 3 subgroups of CL tumours show a high frequency of activation of the RAS-dependent signalling pathway. In the context of CL1, this observation suggests that MaSCs have a high susceptibility to malignant transformation by RAS, in line with the emerging notion of cellular pliancy (Puisieux et al., 2018). In the context of CL2 and CL3, it suggests that the activation of the RAS pathway may promote cellular plasticity during tumour development, through the induction of EMT. To further study the interplay between the oncogenic activation of RAS and cellular plasticity during breast tumorigenesis, we used inducible models of human mammary epithelial cells expressing a mutated form of RAS to analyse early steps of malignant transformation. We demonstrate that RAS-dependent transformation relies on the induction of ZEB1-dependent EMT. Notably, this transdifferentiation process is driven by a paracrine signalling mechanism involving the secretion of cytokines IL-6 and IL-1 from neighbouring RAS-activated senescent cells. Hence, we unveil an unforeseen pro-tumorigenic interplay between RAS activation, oncogene-induced senescence and EMT. Data Figs. 1a and1d). Likewise, a pan-cancer EMT signature was also clearly enriched in CL tumours (Fig. 1b and Extended Data Figs. 1b,e). Notably, the RAS/MAPK pathway was highly correlated with EMT, even in the CL cell lines, excluding any stroma contamination bias (Fig. 1c and Extended Data Figs. 1c,f).

RAS-activation in differentiated mammary cells induces a phenotypic switch associated

with EMT-TF expression. We wondered whether RAS activation in normal differentiated epithelial cells could induce the reactivation of EMT-inducing transcription factors (EMT-TFs), leading to the acquisition of cellular plasticity. To test this, we carried out kinetics analysis in a RAS inducible cellular model, HME-RASER, generated by introducing a 4-hydroxytamoxifen (4-OHT)-inducible form of HRAS G12V into human mammary epithelial (HME) cells. Seven days into RAS activation in HME-RASER, an increase in expression of EMT-TFs ZEB1 and ZEB2 as well as a concomitant decrease in the expression of ZEB negative regulator, the miR-200 mRNA family were revealed (Fig. 2a,b and Extended Data Fig. 2a). Moreover, a clear change in cellular morphology was noted at day 49 following RAS induction, with HME-RASER cells losing their cobblestone-like epithelial morphology and gaining a spindle-like morphology suggestive of mesenchymal features associated with an EMT process (Fig. 2c). It was previously demonstrated that cell commitment to EMT was associated with the acquisition of features of cancer stem cells (CSC) 11,12 13,14 . Therefore, the expression of CD24, CD44, EpCAM and CD104 was evaluated by flow cytometry analysis in HME-RASER cells ensuing RAS activation. The emergence of the three main phenotypes associated with breast CSC, namely CD24 -/low /CD44 + (Fig. 2c), CD24 -/low /CD44 + /EpCAM + and CD44 + /CD104 + cells (Fig. 2d and Extended Data Fig. 2b), were observed as early as day 7 following RAS activation, suggesting the onset of a reprogramming process. These results were confirmed using two other independent HME-RASER models (Extended Data Fig. 3a-c). The emergence of these CSC phenotypes was accompanied with an increased capacity to grow in soft agar, a gold-standard assay for cellular transformation (Fig. 2e). We next examined whether RAS activation affected the structural and morphological organization in 3D culture. Here, HME-RASER cells were grown in 3D organoid culture conditions for 10 days, without 4-OHT activation, in order to obtain a typical organoid structure prior to RAS activation. A disruption in the structural organization associated with the acquisition of EMT features was observed in 4-OHT-induced HME-RASER organoids compared to non-induced organoids (Fig. 2f).

Collectively, these data suggest that RAS activation in HME cells induces EMT-TF expression and enhances cellular plasticity and acquisition of stemness markers.

RAS-induced cellular plasticity and transforming ability are ZEB1-dependent.

To determine if RAS activation could drive cellular plasticity in the absence of EMT-TFs, a vector allowing the constitutive expression of miR-200c, a major negative regulator of ZEB1, ZEB2 and SNAI1, was introduced into HME-RASER cells [START_REF] Puisieux | Oncogenic roles of EMT-inducing transcription factors[END_REF][START_REF] Burk | A reciprocal repression between ZEB1 and members of the miR-200 family promotes EMT and invasion in cancer cells[END_REF] (Extended Data Fig. 4a-c). As expected, induction of ZEB1 and ZEB2 expression was dampened following RAS activation in this model (Extended Data Fig. 4d,e). miR-200c expression further prevented the emergence of CD24 - /low /CD44 + cells and the acquisition of properties associated with transformation and stemness (Fig. 3a-c). Indeed, induced HME-RASER-miR-200c cells were neither able to form colonies in soft-agar assays nor mammospheres in low-adherent culture conditions (Fig. 3b,c). Hence, inhibiting the expression of ZEB1 and ZEB2 by overexpressing miR-200c prevented the acquisition of cellular plasticity following RAS activation.

Because ZEB1 is a known master regulator of EMT in mammary epithelial cells, a CRISPR/Cas9 knock-down approach was used to determine its role in the context of RAS activation. Three independent ZEB1-inactivated clones of HME-RASER cells were generated (HME-RASER-CRISPR ZEB1#1, HME-RASER-CRISPR ZEB1#2 and HME-RASER-CRISPR ZEB1#3) (Extended Data Fig. 4f,g). ZEB1 inactivation alone inhibited the emergence of the CD24 -/low /CD44 + population as well as the acquisition of transformation and stemness properties (Fig. 3d-f). Interestingly, ZEB2 and SNAI2 expression was moderate in ZEB1inactivated clones, suggesting that their up-regulation was essentially ZEB1-dependent (Figure S4H).

Altogether, these data demonstrate that plasticity features acquired by differentiated HME cells following RAS activation strongly rely on ZEB1.

Dual fate following RAS activation: senescence or mesenchymal transition. Because only a sub-population of induced HME-RASER cells displays EMT features after RAS activation, we performed single-cell RNA sequencing (scRNAseq) analysis at different time points (D0, D3, D7, D14 and D20) with the aim of determining the mechanisms underlying cellular plasticity. Expression profiles were obtained for 12,357 genes in 445 cells (117 cells at D0, 83 cells at D3, 74 cells at D7, 82 cells at D14 and 89 cells at D20). To analyse transcriptional variability over the complete scRNAseq dataset, dimensions were reduced using Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) [START_REF] Becht | Dimensionality reduction for visualizing single-cell data using UMAP[END_REF] (Fig. 4a). A dimension reduction analysis and graph-based k-means clustering identified 5 clusters based on their differential gene expression, numbered 1 to 5 (Fig. Extended Data Fig. 5a). For each cluster, specific marker genes were extracted to search for enriched pathways using MSigDB (Supplementary Tables 12345). This allowed us to functionally annotate each cluster (Fig. 4b), resulting in clusters 1, 2 and 4 related to cell cycle phases (cluster1 to G1 phase, cluster 2 to G2/M phase and cluster 4 to S phase) and clusters 3 and 5 related to senescence and EMT, respectively (Fig. 4b,c). Interestingly, cluster 3, which represents less than 10% of cells at D0, constitutes more than 40% of cells at D7 while cluster 5 appears at D3 and is the most represented at D20 (Fig. 4d). We next interrogated a possible link between these two emerging clusters through evolutionary trajectory analysis. Given the uncertain origin of a potential trajectory, cluster 1 (related to G1 phase) was used as a starting point since it was the most represented at D0 (40%) (Fig. 4d). Trajectory analysis showed two different end points: cluster 3/senescence or cluster 5/EMT (Fig. 4e), suggesting that, following RAS activation, cells would either undergo senescence or acquire a mesenchymal phenotype. Trajectory analysis using cluster 2 or 4 as a starting point gave identical results with regards to the evolutionary outcome: senescence or EMT (Extended Data Fig. 5b). Interestingly, the rise of the senescence cluster preceded the rise of the EMT cluster (D7 vs D20, respectively; Fig. 4d), indicating a possible underlying causal relationship.

ZEB1-dependent plasticity is driven by oncogene-induced senescence. Based on scRNAseq data, we sought to gain further insights into a potential crosstalk between oncogene-induced senescence (OIS) and EMT. We therefore analysed the emergence of senescent cells, determined by SA-β-gal positive staining and ZEB1 expression in a time dependent manner.

We previously showed that ZEB1 expression distinctly increased from D14 onwards and was directly associated with the emergence of the CD24 -/low /CD44 + population (Fig. 2a-d). As shown in Fig. 4f, RAS-induced senescent cells appeared from D3 with a peak at D7. The initial emergence of OIS followed by the delayed occurrence of EMT led us to hypothesize that senescence may contribute to the induction of cellular plasticity. As a first approach to test this hypothesis, HME-RASER cells were treated with dasatinib, an inhibitor of the Bcr-Abl protein kinase and a known senolytic drug [START_REF] Zhu | The Achilles' heel of senescent cells: from transcriptome to senolytic drugs[END_REF][START_REF] Kirkland | Senolytic drugs: from discovery to translation[END_REF] . Dasatinib treatment significantly reduced the number of senescent cells at day 7 of RAS-activation, along with a 10-fold decrease in SA-βgal-positive cells (Fig. 5a). Notably, this senescence blockage was also associated with a decrease in both ZEB1 expression and the percentage of CD24 -/low /CD44 + cells at day 21 (Fig. 5b,c) in comparison to DMSO-treated cells, thus in support of ZEB1-dependent cellular plasticity being reliant on the onset of senescence. ZEB1-dependent cellular plasticity is driven by factors secreted by senescent cells. Upon entering senescence, cells produce a set of pro-inflammatory cytokines and chemokines, extracellular matrix proteins, growth factors and metalloproteinases composing the senescenceassociated secretory phenotype (SASP), which through secretory signalling, impacts the dedifferentiation of somatic cells [START_REF] Mosteiro | Tissue damage and senescence provide critical signals for cellular reprogramming in vivo[END_REF] . We therefore questioned if a secretory signalling process emanating from RAS-activated senescent cells may be responsible for ZEB1-mediated plasticity in neighbouring differentiated cells. To explore this hypothesis, we generated a cellular HME model (HME_d2GFP200) that expressed a destabilized GFP (d2GFP) including a 3′UTR containing five miR-200 target sequences (Extended Data Fig. 6a). This set-up was expected to highlight cells undergoing EMT through an increased GFP signal underlying lower levels of expression of miR-200 family members [START_REF] Toneff | The Z-cad dual fluorescent sensor detects dynamic changes between the epithelial and mesenchymal cellular states[END_REF] . To determine whether senescent cells promote EMT in neighbouring cells, RAS-induced HME-RASER cells tagged with dsRED (HME-RASER-dsRED cells) were co-cultured with HME_d2GFP200 cells (Fig. 6a). An enrichment of GFP high cells associated with an increase in ZEB1 expression and an emergence of a CD24 -/low /CD44 + phenotype was observed in HME_d2GFP200 cells (Fig. 6b-d and Extended Data Fig. 6b), revealing that ZEB1-reactivation can be driven by secreted factors.

Moreover, these co-cultured HME_d2GFP200 cells were able to form colonies in soft agar assay, suggesting that they had acquired cellular properties associated with transformation (Fig. 6e). Finally, we performed a transwell co-culture assay using HME_d2GFP200 cells and RASinduced HME-RASER-dsRED cells. The characterization of cells following co-culture confirmed the enrichment of a CD24 -/low /CD44 + phenotype in GFP high cells (Fig. 6f,g), thus evoking either an autocrine or paracrine signalling process rather than a juxtacrine one.

IL-6 and IL-1α secreted by senescent cells following RAS activation promote ZEB1-

induced cellular plasticity. Given that our data revealed a possible effect of secretory signalling on the acquisition of ZEB1-dependent cellular plasticity following RAS activation, we next aimed to narrow down the specific factors produced by senescent cells leading to this effect, focusing specifically on SASP constituents. Multiplex technology was used to quantify the concentrations of SASP factors in RAS-induced HME-RASER cell culture supernatants.

Quantifications revealed high levels of cytokines IL-6, IL-8, IL-1, IL-1 and granulocytemacrophage colony-stimulating factor (GM-CSF) (Fig. 7a). Experiments based on the direct addition of these cytokines to HME cells at the highest concentration found in cell culture supernatants showed the ability of IL-6, IL-1 and IL-1 to promote the acquisition of a plasticity phenotype (Fig. 7b). Interestingly, the combination of IL-6 and IL-1 had a cumulative effect on the emergence of CD24 -/low /CD44 + cells. Next, to evaluate the role of these cytokines in RAS-induced plasticity, we used a depletive approach with cytokine-neutralizing antibodies or IL-6R antibody (tocilizumab). Double inhibition of IL-6/IL-6R or the inhibition of IL-1 alone decreased the CD24 -/low /CD44 + population and ZEB1 expression as well as the transforming abilities of RAS-induced HME-RASER cells (Fig. 7c,d and Extended Data Fig. 7a).

Strikingly, combination treatments with different cytokines, showed that the concomitant inhibition of IL-6/IL-6R and IL-1 induced an almost 10-fold decrease in cellular plasticity and transformation capacities, suggesting that these two cytokines are major determinants of ZEB1mediated plasticity and transformation (Fig. 7c,d).

ZEB1-induced cellular plasticity relies on a paracrine effect mediated by senescent cells.

To further characterize the process involved in ZEB1-mediated plasticity after RAS activation, we used multi-fluorescent labelling kinetics to visualize the proliferative state of cells via anti-Ki67, the expression of cytokines IL-6 and IL-1α, and ZEB1 in HME-RASER cells (Fig. 7e).

We observed that at day 7 of RAS activation, 10% of RAS-induced HME-RASER cells showed a negative Ki67 profile and were double positive for IL-6 and IL-1α, consistent with a senescent state. Moreover, 31% of cells presented a negative Ki67 profile and were positive for either IL-6 or IL-1α, potentially indicating a pre-senescent state. None of these cells were positive for ZEB1. Fourteen days after RAS induction, 9% of cells were ZEB1 positive and less than 2% of them were both negative for Ki67 and positive for IL-6 and/or IL-1α (Fig. 7e). At day 21, 26% of cells were ZEB1 positive with the majority being Ki67 positive. Overall, these data suggest that cells entering OIS do not express ZEB1, consistent with the hypothesis of the involvement of a paracrine process driving EMT in neighbouring cells. Next, we co-cultured HME-d2GFP_200 cells with tamoxifen-induced HME-RASER-dsRED cells and treated them with IL-6/IL-6R and IL-1 neutralizing antibodies. The acquisition of cellular plasticity in HME-d2GFP_200 cells was strongly impaired following treatment (Extended Data Fig. 7b,c).

Altogether, these results reveal that ZEB1 activation after RAS induction is driven by a paracrine process involving the secretion of IL-6 and IL-1α cytokines from senescent cells.

Discussion

Despite their relative rarity, breast cancers of the claudin-low molecular subtype are a remarkable illustration of the influence of the cell of origin and the microenvironment on tumour development and progression. Indeed, their molecular characterization has recently revealed three main subgroups emerging from unique evolutionary processes [START_REF] Pommier | Comprehensive characterization of claudin-low breast tumors reflects the impact of the cell-of-origin on cancer evolution[END_REF][START_REF] Fougner | Claudin-low-like mouse mammary tumors show distinct transcriptomic patterns uncoupled from genomic drivers[END_REF] . The subgroup 1 of claudin-low tumours (CL1), characterized by stem cell features and low genomic instability, are presumed to develop from the malignant transformation of a normal mammary stem cell, whereas claudin-low subtypes 2 (CL2) and 3 (CL3) appear to evolve from luminal or basal tumours, respectively following aberrant induction of an EMT process in response to microenvironmental cues. Interestingly, all three subgroups are characterized by a high frequency of activation of the RAS-MAPK pathway, suggesting the existence of a mechanistic link between the activation of this oncogenic pathway and the mesenchymal characteristics of these tumours. To characterize this interplay, we have developed an in vitro approach for identifying the early and long-term consequences of RAS pathway activation in human mammary epithelial cells. Our observations show a causal link between the activation of the RAS-MAPK pathway and the induction of EMT, the nature of which is totally distinct from the usual view of the involvement of EMT in cancer, in terms of the phases of its involvement, the mechanisms underlying its induction, and its consequences during tumorigenesis.

In cancer, EMT is generally considered to be a process induced during tumour progression in response to microenvironmental signals such as growth factor signalling, hypoxia and immune response pathways [START_REF] Dongre | New insights into the mechanisms of epithelialmesenchymal transition and implications for cancer[END_REF] , which will promote key steps of the invasion-metastasis cascade [START_REF] Yang | Twist, a master regulator of morphogenesis, plays an essential role in tumor metastasis[END_REF] . Here, we unveil a causal link between RAS activation and EMT. In this context, inhibition of EMT prevents RAS-induced transformation of human mammary epithelial cells, suggesting a determinant role for this transdifferentiation process in early phases of tumorigenesis following an oncogenic event. It remains to be determined whether this notion is generalizable to other tumour types where activation of the RAS pathway is a frequent and an early event. However, it is noteworthy that in a mouse model of pancreatic cancer driven by Pdx1-cre-mediated activation of mutant Kras, homozygous deletion of Zeb1 significantly reduces features of acinar-ductal metaplasia (ADM) as well as the number and grading of PanIN, the main origins of pancreatic precursor lesions [START_REF] Krebs | The EMT-activator Zeb1 is a key factor for cell plasticity and promotes metastasis in pancreatic cancer[END_REF][START_REF] Liu | Different thresholds of ZEB1 are required for Ras-mediated tumour initiation and metastasis[END_REF] . The causal link between RAS pathway activation and EMT induction further provides a mechanistic explanation for the characteristics of CL2 and CL3 breast tumours since RAS-MAPK activation during the evolution of their originating luminal or basal tumours would indeed lead to the acquisition of mesenchymal characteristics inherent to CL subtypes. In support of this hypothesis, in transgenic mouse models, Kras activation in luminal mammary epithelial cells leads to EMT commitment and promotes claudin-low tumours [START_REF] Rädler | Highly metastatic claudin-low mammary cancers can originate from luminal epithelial cells[END_REF] . Collectively, these observations suggest that the induction of EMT during tumour development is not only a late consequence of exposure to microenvironmental signals that favor the metastatic dissemination of cancer cells, but may also constitute an early event of cellular adaptation following certain oncogenic insults, influencing tumorigenesis as well as tumour evolution and progression.

A critical finding of our study is the identification of a mechanistic link between oncogeneinduced senescence and EMT over the course of malignant transformation. Consistent with our data, recurrent signatures associated with EMT and senescence have been recently reported by using a pan-cancer single-cell RNA-seq approach [START_REF] Kinker | Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity[END_REF][START_REF] Gavish | The transcriptional hallmarks of intra-tumor heterogeneity across a thousand tumors[END_REF] . Cellular senescence is a stress response that is generally considered to irreversibly and stably arrest the cell cycle following a variety of intrinsic and extrinsic stressors such as shortening of chromosomal termini, DNA damage, oxidative stress, oncogenic insults or inactivation of tumour suppressors [START_REF] Campisi | Aging, cellular senescence, and cancer[END_REF] . Notably, oncogeneinduced senescence or senescence induced by loss of a tumour suppressor was verified in vivo in human and murine preneoplastic lesions [START_REF] Collado | Cellular senescence in cancer and aging[END_REF][START_REF] Gorgoulis | Cellular Senescence: Defining a Path Forward[END_REF][START_REF] Kuilman | Senescence-messaging secretome: SMS-ing cellular stress[END_REF] . Senescence is traditionally described as an innate anti-cancer mechanism since cells harbouring oncogenic mutations are prevented from proliferating and transforming [START_REF] Rodier | Four faces of cellular senescence[END_REF] . Moreover, in addition to decreasing replicative capacity, activation of senescence in different contexts and tissues leads to increased expression of inflammatory cytokines that elicit immune-mediated tumour clearance [START_REF] Xue | Senescence and tumour clearance is triggered by p53 restoration in murine liver carcinomas[END_REF][START_REF] Kang | Senescence surveillance of pre-malignant hepatocytes limits liver cancer development[END_REF] . However, recent studies challenge this conventional view, showing a paradoxical role for senescence emanating from its special secretory profile that can counterintuitively promote cancer stemness and aggressiveness [START_REF] Milanovic | Senescence-associated reprogramming promotes cancer stemness[END_REF][START_REF] Blander | Cellular plasticity: A route to senescence exit and tumorigenesis[END_REF] . In our study, single cell analysis revealed oncogene-induced senescence in a subset of RAS-activated cells and the subsequent induction of an EMT process in a separate subset. Senolytic treatment showed that senescent cells comprising the first subset play a causal role in the acquisition of plasticity by neighbouring cells. This duality in cellular outcomes recalls the work of the groups of Manuel Serrano and Han Li who reported that the activation of the 4 transcription factors Oct4, Sox2, Klf4, and c-Myc (OSKM) in mouse tissue triggers opposite cellular fates, namely, senescence and reprogramming, that coexist in vivo within separate subpopulations of cells [START_REF] Chiche | Injury-Induced Senescence Enables In Vivo Reprogramming in Skeletal Muscle[END_REF][START_REF] Mosteiro | Senescence promotes in vivo reprogramming through p16INK4a and IL-6[END_REF] . Consistent with a senescence response, overexpression of OSKM in human primary fibroblasts triggers replication stress and DNA damage characterized by the up-regulation of p53, p16 INK4a , and p21 CIP1 , impaired proliferation and formation of senescence-associated heterochromatin foci (SAHF) [START_REF] Banito | Senescence impairs successful reprogramming to pluripotent stem cells[END_REF] . With striking conceptual similarity, we have thus demonstrated that entry into senescence of a subset of mammary epithelial cells facing RAS-dependent oncogenic insult promotes the transdifferentiation of neighbouring cells.

Importantly, cellular senescence creates a tissue context that favours OSKM-driven reprogramming in surrounding cells through the paracrine action of the SASP, with IL-6 being a key mediator [START_REF] Chiche | Injury-Induced Senescence Enables In Vivo Reprogramming in Skeletal Muscle[END_REF][START_REF] Mosteiro | Senescence promotes in vivo reprogramming through p16INK4a and IL-6[END_REF] . A similar interplay also occurs in tissue aging and injury, where there is an accumulation of senescent cells that secrete pro-inflammatory cytokines, triggering OSKMdriven dedifferentiation and reprogramming in neighbouring cells. Quantification of a series of SASP constituents and functional assays enabled us to determine the factors inherent to senescent mammary epithelial cells that are responsible for triggering a similar reprogramming process in the cellular vicinity. Namely, we identified proinflammatory cytokines IL-6 and IL-1 as major mediators of senescence-induced EMT. Of note, IL-6 has previously been shown to trigger EMT via JAK-STAT3 or NF-κB signalling, leading to the activation of EMT-TFs 41- 48 . Accordingly, IL-6 overexpression and/or hyper-activation has been reported in several human cancers [START_REF] Grivennikov | IL-6 and Stat3 are required for survival of intestinal epithelial cells and development of colitis-associated cancer[END_REF][START_REF] Sansone | IL-6 triggers malignant features in mammospheres from human ductal breast carcinoma and normal mammary gland[END_REF][START_REF] Lederle | IL-6 promotes malignant growth of skin SCCs by regulating a network of autocrine and paracrine cytokines[END_REF][START_REF] Walter | Interleukin 6 secreted from adipose stromal cells promotes migration and invasion of breast cancer cells[END_REF] . Relatively fewer studies have directly examined the role of IL-1 in cellular reprogramming and in cancer development and progression. Nevertheless, IL-1 inactivation in pancreatic cancer impaired tumour progression and immune cell infiltration [START_REF] Lau | Uncoupling the Senescence-Associated Secretory Phenotype from Cell Cycle Exit via Interleukin-1 Inactivation Unveils Its Protumorigenic Role[END_REF] .

The oncogenic activation of KRas in IL-1 absence also led to a constant number of benign lesions, but above all, a decrease in dysplastic and neoplastic lesions [START_REF] Lau | Uncoupling the Senescence-Associated Secretory Phenotype from Cell Cycle Exit via Interleukin-1 Inactivation Unveils Its Protumorigenic Role[END_REF] . Finally, a recent work examining the relationship between HER2 expression in breast cancer, inflammation and expansion of cells presenting stemness properties highlighted an essential role for IL-1α [START_REF] Liu | HER2 Overexpression Triggers an IL1a Proinflammatory Circuit to Drive Tumorigenesis and Promote Chemotherapy Resistance[END_REF] . In light of these observations, further investigations will now be necessary to define the steps downstream of IL-6 and IL-1α secretion that led to the induction of ZEB1 expression.

We have previously shown in the model of breast tumorigenesis that EMT-TFs of the TWIST and ZEB families cooperate with mitogenic oncoproteins for malignant transformation by alleviating oncogene-induced senescence [START_REF] Morel | EMT inducers catalyze malignant transformation of mammary epithelial cells and drive tumorigenesis towards claudin-low tumors in transgenic mice[END_REF][START_REF] Ansieau | Induction of EMT by Twist Proteins as a Collateral Effect of Tumor-Promoting Inactivation of Premature Senescence[END_REF] . The present data suggest that oncogene-induced senescence of only a subset of cells is enough to trigger the bypass of this same oncosuppressive barrier in neighbouring cells via the action of released SASP factors, thereby favouring their malignant transformation. From a deterministic point of view, we are confronted here with a paradox: the act of self-sacrifice cannot be more beautifully described than by the selfless attempt of cells to undergo senescence in order to protect an organism as a whole; yet, this desperate act may be the critical element that promotes the transformation of neighbouring cells facing the same oncogenic stress. This notion supports the hypothesis that oncogene-induced senescence has contrasting cell-autonomous and non-cell autonomous effects in early tumorigenesis, providing novel clues to the ongoing debate on its tumour suppressor and protumorigenic effects.

Methods

Cell culture. Primary human mammary epithelial cells (HMECs) (Lonza) were immortalized by hTERT, and named HME thereafter [START_REF] Morel | EMT inducers catalyze malignant transformation of mammary epithelial cells and drive tumorigenesis towards claudin-low tumors in transgenic mice[END_REF] . HME-derived cells (HME-RASER, HME-CTRL, HME-RASER-miR-200c, HME-RASER-empty, HME-RASER-CRISPR ZEB1, HME-RASER-CRISPR SCR and HME_d2GFP200) were cultured in Dulbecco's Modified Eagle's Medium (DMEM)/HAMF12 medium with 1 % glutamax (Thermo Fisher Scientific) supplemented with 10% heat-inactivated fetal calf serum (Eurobio Scientific), 100 UI/mL penicillin, 100 µg/mL streptomycin (Thermo Fisher Scientific), 10 ng/ml human epidermal growth factor (EGF) (Peprotech), 0.5 mg/ml hydrocortisone (Sigma Aldrich) and 10 mg/ml insulin (Novorapid®, Novonordisk), at 37°C in 5% CO2/95% air. Breast cancer cell lines were obtained from the American Tissue Culture Collection (ATCC). MDA-MB-231, Hs578T and HEK293T cells were maintained in DMEM with 1 % glutamax (Thermo Fisher Scientific) with 10% heatinactivated fetal calf serum (Eurobio Scientific) and 100 UI/mL penicillin, 100 µg/mL streptomycin (Thermo Fisher Scientific). All cell lines were regularly tested negative using the MycoAlert mycoplasma detection kit (Lonza).

Lentiviral and retroviral infections. Lentiviral particles were produced using non-confluent Reagent (MerckMillipore) and 15 µg of retroviral expression vectors [START_REF] Morgenstern | Advanced mammalian gene transfer: high titre retroviral vectors with multiple drug selection markers and a complementary helper-free packaging cell line[END_REF] . At 48 hours posttransfection, the supernatant was collected, filtered, supplemented with 5 µg/ml of polybrene (MerckMillipore) and combined with 10 6 targeted cells for 6 hours. Cells were selected 48 hours following infection with puromycin (0.5 mg/ml) and/or neomycin (100 mg/ml). Kinetics, co-culture and transwell assay. For kinetics, 27 000 cells were seeded at day 0 in a 6-well plate with 2 ml of medium for all conditions. At day 4, medium was changed and at day 7, cells were trypsinized (0.05% trypsin, 0.53mM EDTA; Thermo Fisher Scientific) and this cycle was repeated until D 49. For co-culture, HME_d2GFP cells were mixed 20:80 with HME-RASER-dsRED cells. For 1 week-kinetics in a 6 well-plate, around 5 000 HME_d2GFP cells were mixed with 22 000 HME-RASER-dsRED cells per well. Cells were trypsinized at days 7 and 14. For transwell kinetics, 7000 HME_d2GFP cells were plated in the insert for 27 000 HME-RASER-dsRED cells per well at day 0. Cells were trypsinized at days 7 and 14.

Plasmids

Mammospheres . Cells were suspended in DMEM-F12 medium and counted. 10 000 cells were seeded into an ultra-low-attachment 24-well plate (Corning). In necessary cases, 4-OHT was added to each well every 4 days (without removing the old medium) and 100 µl of fresh medium was added every 3 to 4 days. Mammospheres were collected after 7-10 days by centrifugation (800g), washed two times in phosphate-buffered saline PBS, trypsinized (100 µl, 30 min) and dissociated mechanically through pipetting. Cells were assessed microscopically for single-cellularity. Next, cells were resuspended with 900 µl of fresh medium and sieved through a 40-μm sieve. 3 rounds of mammospheres formation of 7 to 10 days were conducted.

The number of spheres (nonadherent spherical clusters of cells with basal membrane) for each well was evaluated under the microscope after round 3. Quantification of formed mammospheres was performed microscopically by manual counting. Images of mammospheres were acquired using a ZEISS Axio Vert.A1 Inverted Microscope.

Organoids. Cell suspension was resuspended in Reduced Growth Factor Basement Membrane

Matrix BME (BioTechne). A 50 µl drop of this BME suspension containing 1000 cells was placed in the center of a well of a prewarmed ultra-low-attachment 96-well plate (Corning), allowed to harden at 37 °C for 20 minutes. Upon complete gelation, 200 µl of organoid medium supplemented with different factors was added to each well [START_REF] Sachs | A Living Biobank of Breast Cancer Organoids Captures Disease Heterogeneity[END_REF] The plate was incubated in a humidified 37 °C / 5% CO2 incubator. Culture medium was changed every two days. After 10 days, 4-OHT was added to the medium every 3 days in induced RAS condition. After 21 days of 4-OHT treatment or non-treatment, organoids were fixed in buffered formalin for multiplexed immunofluorescence staining.

Multiplexed immunofluorescence. For histological examination, tissue samples (organoids or cell pellets) were fixed in 10% buffered formalin and embedded in paraffin using the cytoblock kit (Microm Microtech France, E/7401150). 4-µm-thick sections of formalin-fixed, paraffinembedded tissue were prepared according to conventional procedures.

Multiplexed immunofluorescence was performed on a Bond RX automated immunostainer (Leica biosystems) using OPAL detection kits (AKOYA bioscience). Sequential immunofluorescence stainings were performed using:

-Organoids Panel: OPAL 520 (anti-CK5, Leica #NCL-L-CK5), OPAL 690 (anti-CK18, Agilent #M7010), OPAL 780 (anti-VIMENTINE, Agilent #M0725).

-Cells Panel: OPAL 520 (anti-IL-1 α, LSBio #LS-C33821), OPAL 620 (anti-IL-6, Abclonal #A0286), OPAL 570 (anti-ZEB1, Bethyl #IHC-00419), OPAL 690 (anti-Ki67 Mib1, Agilent #M7240).

Samples were counterstained with DAPI (Sigma Aldrich, D8417). Fluorescent slides were mounted using Prolong™ Gold Antifade Reagent (Invitrogen, Ref# P36930).

Sections were scanned using the Vectra POLARIS device (Akoya bioscience). An autofluorescence treatment of images was carried out using the Inform software (Perkin Elmer).

For analysis of cells, an evaluation of the staining was carried out using the HALO Image Analysis Software (Indica Labs). Cells were considered Ki67-positive if the fluorescence intensity value was > 30, IL-6-positive if the fluorescence intensity value was >25, IL-1αpositive if the fluorescence intensity value was >75 and ZEB1-positive if the fluorescence intensity value was > 32.

Soft-agar colony formation assay. A 2X DMEM/F12 powder including L-glutamine (Sigma Aldrich #D0547) was re-suspended with sterile water and completed with 1.2 g/L sodium bicarbonate (Sigma Aldrich #S5761). This prewarmed medium was supplemented with 20% heat-inactivated fetal calf serum, 200 UI/mL penicillin, 200 µg/mL streptomycin, 1mg/ml hydrocortisone, 20 ng/ml EGF and mixed 1 :1 with melted SeaPlaque Agarose (Lonza) solution to obtain a final 0.75% low-melting agarose base which was then overlaid with a suspension of cells in 0.45% low-melting agarose (10 000 cells/well). 6-well plates were incubated for 4 weeks at 37°C with 5% CO2/95% air, and colonies defined as a group of >20 cells were counted under the microscope. In experiments using GFP-tagged cells, only GFP-positive cells were counted. Experiments were performed in triplicate.

SA-β-Galactosidase staining. Cells were rinsed with PBS and fixed 4 minutes in 2% formaldehyde/0.2% glutaraldehyde, then washed twice with PBS and incubated at 37°C for 18 hours in a buffer containing 40 mM sodium phosphate (pH 6.0), 5 mM K4Fe(CN)6, 5 mM K3Fe(CN)6, 150 mM NaCl, 2 mM MgCl2 and X-Gal re-suspended with N,N-Dimethylformamide at 20 mg/ml and diluted in sterile water to obtain a 1 mg/ml final concentration. All reagents were purchased at Sigma Aldrich except for X-Gal powder (VWR).

Multiplex cytokine assay.

We assessed 31 cytokines in each sample with the Panel (human) kit (Meso-Scale Discovery (MSD)), which measures IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-7, ng/ml), IL-8 (10 ng/ml), IL-1α (1.5 ng/ml), IL-1β (50 pg/ml), GM-CSF (1 ng/ml), normal human IgG control (10 ng/ml). Cytokines were added every 2 days and medium was changed

Bioinformatics analysis on breast cancer dataset.

Samples and statistics. Breast tumour samples used in this study are from METABRIC [START_REF] Curtis | The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups[END_REF] and TCGA Research Network (https://www.cancer.gov/tcga). Cohorts and breast cancer cell lines are from CCLE database [START_REF] Barretina | The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity[END_REF] . All analyses, statistical tests and figures were realized using either the R software (version 3.6.1) or GraphPad Prism 8.0 (GraphPad Software Inc., San Diego, USA). All statistical tests are two-tailed.

Expression data processing. METABRIC microarray expression data from discovery and validation sets were extracted from the EMBL-EBI archive (EGA, http://www.ebi.ac.uk/ega/;

accession number: EGAS00000000083) (Normalized expression data files) [START_REF] Curtis | The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups[END_REF] . Normalized expression data per probe of the discovery set and the validation set were combined after independent normalization of each set with a median Z-score calculation for each probe. The expression levels of different probes associated with the same Entrez Gene ID were averaged for each sample in order to obtain a single expression value by gene.

TCGA BRCA RNAseq expression data were extracted as Fragments Per Kilobase Million Molecular breast cancer subtype assignment. For both tumours and cell lines, attribution of breast cancer molecular subtypes was performed using the R package genefu [START_REF] Gendoo | Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer[END_REF] . Basal-like, luminal A, luminal B, Her2 and normal-like subtype assignments were computed from 5 different algorithms (PAM50, AIMS, SCMGENE, SSP2006 and SCMOD2) [START_REF] Haibe-Kains | A three-gene model to robustly identify breast cancer molecular subtypes[END_REF][START_REF] Wirapati | Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures[END_REF][START_REF] Bernard | Supervised risk predictor of breast cancer based on intrinsic subtypes[END_REF][START_REF] Hu | The molecular portraits of breast tumors are conserved across microarray platforms[END_REF][START_REF] Paquet | Absolute Assignment of Breast Cancer Intrinsic Molecular Subtype[END_REF] . An assignment was considered final if defined by at least 3 different algorithms. In case of divergence between classifiers, PAM50 subtype attribution was used. For METABRIC and TCGA breast tumours, classification as a claudin-low subtype was defined by nearest centroid method. For this, the Euclidean distance between each tumour sample and the previously described claudin-low and non-claudin-low centroids for tumour samples were determined using the 1,667 genes defined by Prat et al. as significantly differentially expressed between claudin-low tumours and all other molecular subtypes [START_REF] Prat | Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer[END_REF] .

For CCLE breast cancer cell lines, claudin-low status assignment was performed using the ninecell line predictor via the R package genefu [START_REF] Gendoo | Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer[END_REF] .

Pathway analysis. Single-sample GSEA (ssGSEA) scores were computed through gsva R package [START_REF] Hänzelmann | GSVA: gene set variation analysis for microarray and RNA-Seq data[END_REF] using gene signatures from the molecular signature database (MSigDB) (msigdbr R package) [START_REF] Subramanian | Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles[END_REF] . Pan-cancer transcriptomic EMT signature defined by Tan et al. [START_REF] Tan | Epithelial-mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients[END_REF] was used to compute EMT scores for each sample.

Single cell transcriptome analysis.

Sample preparation. Full-length scRNAseq libraries were prepared using the Smart-seq2 protocol [START_REF] Picelli | Smart-seq2 for sensitive full-length transcriptome profiling in single cells[END_REF] with minor modifications. Briefly, freshly harvested single cells were sorted into 96well plates containing the lysis buffer (0.2% Triton X-100, 1U/µl RNase inhibitor, Thermo Fisher Scientific). RT was performed using SuperScript II (Thermo Fisher Scientific) in the presence of 1 μM oligo-dT30VN (IDT), 1 μM Template-switching oligonucleotides (QIAGEN), and 1 M betaine. cDNA was amplified using the KAPA Hifi Hotstart ReadyMix (Roche) and IS PCR primer (IDT), with 23 cycles of amplification. Following purification with Agencourt Ampure XP beads (Beckmann Coulter), product size distribution and quantity were assessed on a Bioanalyzer using a High Sensitivity DNA Kit (Agilent Technologies). A total of 140 pg of the amplified cDNA was fragmented using Nextera XT (Illumina) and amplified with Nextera XT indexes (Illumina). Products of each well of the 96-well plate were pooled and purified twice with Agencourt Ampure XP beads (Beckmann Coulter). Final libraries were quantified and checked for fragment size distribution using a Bioanalyzer High Sensitivity DNA Kit (Agilent Technologies). Pooled sequencing of Nextera libraries was carried out using a HiSeq4000 (Illumina) to an average sequencing depth of 0.5 million reads per cell.

Sequencing was carried out as paired-end reads of 75 bp length (PE75) with library indexes corresponding to cell barcodes.

Data processing. For the Smart-seq2 single-cell samples, quality check of the sequenced reads was performed with the FastQC v0.11.8 software to ensure the quality standards. Next, reads were aligned with STAR v2.5.4b on the human genome reference GRCh38 with Gencode 32 annotations. Gene expression was estimated with RSEM v1.3.0

Single cell analysis

Fastq files were aligned on the human genome (hg38) using STAR v2.7.0f (default parameters) [START_REF] Dobin | STAR: ultrafast universal RNA-seq aligner[END_REF] . Raw counts for each gene were also computed by STAR, using the gencode V29 annotations.

Counts data for each cell of the same plate were grouped together.

Quality control of the data was done using the R packages scater [START_REF] Mccarthy | Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics[END_REF] and scran [START_REF] Lun | A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor[END_REF] . To remove low-quality data, we filtered out cells with < 3000 genes or > 7000 genes. We also removed genes expressed in < 5% of cells. No more filtering was needed. Data from all cells was grouped in a single matrix and normalized using the logNormCounts method. Using the 500 most variable genes, we computed PCA and UMAP dimension reductions. We then clustered the data with the buildKNNGraph method (K=15).

Signature scores for each cell were extracted using the ssGSEA method from the GSVA package [START_REF] Hänzelmann | GSVA: gene set variation analysis for microarray and RNA-Seq data[END_REF] except for the Jean-Paul Thierry EMT signature, for which we used the published method for cell lines [START_REF] Tan | Epithelial-mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients[END_REF] .

We used the FindMarkers method from Seurat 73 (using the Model-based Analysis of Singlecell Transcriptomics (MAST) method) to identify gene markers for each data cluster. We then selected the top marker genes from each cluster (p-value < 0.001 and fold change > 1 or < -1)

and checked for enriched pathways in these genes using the clusterProfiler package [START_REF] Yu | ClusterProfiler: An R package for comparing biological themes among gene clusters[END_REF] and the Hallmark, C2 and C5 genesets from MSigSB [START_REF] Liberzon | Molecular signatures database (MSigDB) 3.0[END_REF] .

We used the Slingshot method [START_REF] Street | Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics[END_REF] for trajectory analysis on the previously established data clusters to which PCA was applied for dimension reduction.

2x10 6 HEK293T

 6 cells transfected with GeneJuice® Transfection Reagent (MerckMillipore) with 26 µg of total lentiviral expression vectors (10.2 µg of pCMVdeltaR8.91, 2.6 µg phCMVG-VSVG and 13.2µg of plasmid of interest). The pCMVdelteR8.91 and phCMVG-VSVG were gifts from D. Nègre (International Center for Infectiology Research, INSERM U1111-CNRS UMR5308-ENS de Lyon-UCB Lyon1, EVIR Team, Lyon, France). 48 hours post-transfection, the supernatant was collected, filtered and supplemented with 5 µg/ml of polybrene (MerckMillipore) combined with cells for 6 hours. Cell selection was done 72 hours post-infection through FACS-sorting for GFP plasmids (pCDH-CMV-EF1-copGFP/MIR200c, pCDH-CMV-MCS-EF1-copGFP-empty and FUGW-d2GFP-200) or DSRED plasmid (pLLRSV Red-empty) or by antibiotics (puromycin 0.5 mg/mL or neomycin 100 mg/mL; Invivogen) depending on plasmid construct. To produce retrovirus particles, 2x10 6 Phoenix cells were transfected with GeneJuice® Transfection

.

  The lentiviral plasmids: pCDH-CMV-EF1-copGFP/MIR200c and pCDH-CMV-MCS-EF1-copGFP-empty were kind gifts from Dr. Thomas Brabletz, DsRED plasmid, pLLRSV Red-empty was a kind gift from Dr Christophe Ginestier, and miR-200 sensor plasmid, FUGW-d2GFP-200 was purchased from addgene (plasmid #79602)[START_REF] Toneff | The Z-cad dual fluorescent sensor detects dynamic changes between the epithelial and mesenchymal cellular states[END_REF] . Retroviral plasmids used were purchased from addgene: pLNCX2 neo-RAS-ER (#67844) and pLNCX2 neo-empty 5757,58 .CRISPR-Cas9 knockdown. ZEB1-depletion model in HME (HME-RASER-CRISPR ZEB1)was generated using CRISPR-cas9 gene editing technology. Scrambled sgRNA/Cas9 All-in-One Lentivector (Applied Biological Materials; Cat# K010) and ZEB1 sgRNA/Cas9 All-in-One Lentivector (Human) (Target 1: 5'-CACCTGAAGAGGACCAG-3') (Applied Biological Materials; Cat# K2671006) lentiviral particles were used to infect HME cells. Scrambled sgRNA/Cas9 and ZEB1 sgRNA/Cas9 cells were selected with puromycin (Invivogen) at 1 µg/ml 48 hours after infection. After cloning by limited dilution, single cells were grown for approximatively 3 weeks and colonies were screened for knockouts by quantitative PCR, genomic DNA sequencing and western blotting. Genomic DNA sequencing was performed using the sanger method with the following primers for amplification and sequencing 5'-TGAACTGAACGTCAGAGTGGT-3' (forward) and 5'TCACGTGCAGTGGCATTACT-3' (reverse). Three different clones were finally validated.

every 3 to 4 1 (

 41 days.RNA extraction, reverse transcription, and PCR. Adherent cells were washed in PBS (Eurobio scientific) and TRIZOL reagent (Merck Millipore) was added. After sorting, cells were directly centrifuged and washed before TRIZOL was added. Total RNA was isolated using the TRIZOL Reagent according to the manufacturer's instructions. Total RNA concentration and purity were determined from absorbance at 260 nm and 280 nm using a ND-1000 NanoDrop spectrophotometer. Reverse transcription (RT) was performed with 300 ng to 1 µg of RNA using cDNA RT kit (Thermo Fisher Scientific). Quantitative PCR (qPCR) analysis was performed with 3 µl of a 1:10 dilution of the resulting cDNA. DNA amplification was monitored by real-time PCR with a CFX96 instrument (Bio-Rad) and analyzed with Bio-Rad CFX manager software. The relative quantification of gene expression was performed using the comparative CT method, with normalization of the target gene to HPRT1 housekeeping gene. List of primer sequences used for Q-PCR analysis: Human ZEB1 AGG GCA CAC CAG AAG CCA G and GAG GTA AAG CGT TTA TAG CCT CTA TCA, human ZEB2 AAG CCA GGG ACA GAT CAG C and GCC ACA CTC TGT GCA TTT GA, human SNAI1 GCT GCA GGA CTC TAA TCC AGA and ATC TCC GGA GGT GGG ATG, human SNAI2 TGG TTG CTT CAA GGA CAC AT and GTT GCA GTG AGG GCA AGA A, human TWIST1 GGC TCA GCT ACG CCT TCT C and CCT TCT CTG GAA TaqMan qRT-PCR assay was used for detection of mature miRNAs. Reagents, primers and specific probes for miR-200c and miR-141 were obtained from Applied Biosystems. RT reactions and real-time qPCR were performed according to the manufacturer's protocols from 40 ng of RNA per sample. RNU48 was used as a loading control. Immunoblot analysis. Cells were washed in PBS with CaCl2 and lysed in a radio-immuno precipitation assay (RIPA) buffer (100 mM NaCl, 1% NP40, 0.1% SDS, 50 mM Tris pH 8) supplemented with protease inhibitor cocktail (tablets, Roche), phenyl-methane sulfonyl fluoride (PMSF, Merck Millipore) and a phosphatase inhibitor cocktail (Merck Millipore). Cells were scratched at 4°C and lysed cells were sonicated. Protein concentration was dosed with Bio-Rad Protein Assay Dye Reagent (BIORAD). Primary antibodies used include anti-ZEB1 (Merck Millipore # HPA027524), anti-ZEB2 (Merck Millipore #HPA003456), anti-SNAIL+SLUG (Abcam #ab85936), anti-phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) (Cell signalling #4370)), anti-p44/42 MAPK (Erk1/2) (Cell signalling #9102), anti-RAS (G12V mutant specific) (Cell signalling #14412), anti-GAPDH (Merck Millipore #ABS16) and anti-tubulin (Sigma Aldrich #T7816).Secondary antibodies used include horseradish peroxidase-conjugated goat anti-mouse (Santa Cruz #sc-2005) or mouse anti-rabbit polyclonal antibodies (Santa Cruz #sc-2357). Western blots were revealed using a western blot luminol reagent kit (BIORAD). Luminal signal was detected by ChemiDoc (BIORAD). Post-analysis was done with ImageLab software 6.0.BIORAD).Flow cytometry analysis and sorting. For CD44/CD24/CD104 or EpCAM staining, cells were stained with primary antibodies from Miltenyi Biotech: anti-CD44 labelled Fluorescein-5-isothiocyanate (FITC) (clone REA690, dilution 1:400), anti-CD24 labelled phycoerythrin (PE) (clone 32D12, dilution 1:50), anti-CD104 (Integrin β4)-APC (clone: REA236, dilution 1:40) or anti-CD326 (EpCAM)-APC (clone REA764, dilution 1/400). For analyzing GFP-or dsREDtagged cells, anti CD44-Brilliant Violet 421 (clone BJ18, dilution 1:50; Ozyme) and anti CD24-APC (clone ML5, dilution 1:100; Ozyme) were used. Fresh cells were washed once in PBS, stained protected from light at 4°C for at least 1 hour and then washed again with PBS 3 times. A FACS Canto II cytometer (Becton Dickinson) or BD LSR Fortessa (Becton Dickinson) was used for data acquisition alongside Diva software. The cytometer was calibrated daily using Cytometer, Set-up and Tracking (CST) beads (Becton Dickinson) according to the manufacturer's instructions. Sample acquisition was made at a medium flow rate and set to record the highest number and identical number of events possible for each condition. Post-analysis was done using Flowlogic software (Miltenyi Biotec). Antibody validation information is available on the manufacturers' websites. Cell sorting was performed using BIORAD S3 (Biorad) for GFP-and DSRED-tagged cells after cell infection. For all other sorting experiments, FACS ARIA III (BD Biosciences) was used. Statistical analysis. For all biological experiments, significance testing was performed using Prism Software version 7.0 (GraphPad). P values were calculated by one-way analysis of variance (ANOVA) and Tukey multiple comparison test as specified in the figure legends. All experiments were repeated at least 3 times, unless stated otherwise. Kinetics analysis data were plotted as median +/-range and showing all points. P-values <0.05 were considered to be significant. All statistical tests are two-tailed.

(

  FPKM) values from the GDC data portal (https://portal.gdc.cancer.gov/). FPKM data by gene were converted to Transcript Per Million (TPM) as follows: for each gene g ∈ G and each sample s ∈ S, 𝑇𝑃𝑀(𝑔, 𝑠) = ( RNAseq expression data from the CCLE breast cell lines were extracted as Reads Per Kilobase Million (RPKM) values from the CCLE data portal (https://portals.broadinstitute.org/ccle). RPKM data by gene were converted to TPM as follows: for each gene g ∈ G and each sample s ∈ S, 𝑇𝑃𝑀(𝑔, 𝑠) = ( 𝑅𝑃𝐾𝑀(𝑔,𝑠) ∑ 𝑅𝑃𝐾𝑀(𝑖,𝑠) 𝐺 𝑖=1

Fig

  Fig. 1| RAS/MAPK and EMT pathways are highly correlated and strongly activated in claudin-low breast cancer subtype. a,b, Distribution of ssGSEA or EMT scores for each molecular subtype of breast cancer in TCGA cohort, corresponding to (a) RAS or MAPK pathway (ssGSEA score) and (b) EMT pathway (ssGSEA or EMT score 66 ); c, Spearman correlations between KRAS signalling pathway (ssGSEA score) and EMT pathway (ssGSEA score or EMT score 66 ) in breast tumours from TCGA dataset. See Extended Fig.1.

Fig. 2|

  Fig. 2| RAS-activation in differentiated mammary cells induces a phenotypic switch in 2D associated with the expression of EMT-TFs and a disruption of organoid formation in 3D. a, Fold change expression across time of mRNA for EMT-TFs ZEB1, ZEB2 and SNAI2, miR200c and miR141 in HME-RASER cells or in HME-CTRL cells induced with 4-OHT (IND) or not (NI). Relative expression was determined by the ΔΔCt method, normalized to the expression of HPRT1 housekeeping gene for ZEB1, ZEB2 and SNAI2 mRNA and to RNU-48 for miR200c and miR141 and divided by the expression of the untreated sample at the same time point (Ratio IND/NI). Median± range (n=2 independent experiments in duplicate); b,

Fig

  Fig. 3| RAS-induced cellular plasticity and transformation capabilities are ZEB1dependent. a-c, HME-RASER-miR200c cells (RASER-miR200c) or HME-RASER-empty cells (RASER-empty) after 28 days of induction with 4-OHT (IND) or not (NI); d-f, HME-RASER-CRISPR ZEB1 clones (RASER-ZEB1#1, RASER-ZEB1#2 and RASER-ZEB1#3) or HME-RASER-CRISPR scramble (RASER-SCR) cells after 28 days of 4-OHT treatment (IND) or not (NI); a,d Flow cytometry analysis and quantification of CD24 -/low /CD44 + cells. Data are presented as median± range of four independent experiments (n=4); b,e Transformation potential analysis by soft agar colony formation assay. The number of colonies (as defined by > 20 cells) is indicated. Data are presented as median± range of eight independent experiments (n=8) (b) or six independent experiments (n=6) (e); Images of GFP-positive colonies (b) or phase-contrast colonies (e). (Scale bars, 200 µm); c,f, Quantification and phase-contrast images of mammospheres. (Scale bars, 100 µm (c) and 200µm (f)). Data are presented as median ± range of four independent experiments (n=4). p values are calculated by one-way ANOVA, Tukey multiple comparison test (****p<0.001). See Extended Data Fig. 4.

Fig. 4|

 4| Fig. 4| Identification of distinct senescence and EMT clusters in induced HME-RASER. a, Unsupervised UMAP of the transcriptome of all cells at all time points (D0, D3, D7, D14 and D20). Cells are colored by their attributed cluster; b, Main altered pathways by marker genes for each cluster. Gene ratio presented as k/n where k is the size of the overlap of our input with the specific gene set and n is the size of the overlap of our input with all members of the

Fig. 5|

 5| Fig. 5| Senescent cells drive ZEB1-dependent cellular plasticity. a, Representative phasecontrast images and quantification of senescence-associated β-galactosidase (SA-β-gal) activity in HME-RASER (RASER) cells analyzed at D7 of induction with 4-OHT (IND) or not (NI) and treatment with the senolytic drug dasatinib at 1.5µM or the equivalent concentration of DMSO starting from D0. Median±range (n=4); b, Phase-contrast images and ZEB1 mRNA expression in HME-RASER (RASER) cells at D21 of induction with 4-OHT (IND) or not (NI) and treatment

Fig. 6|

  Fig. 6| ZEB1-dependent cellular plasticity is driven by cytokines secreted by senescent cells. a-e, Co-culture assays; (a) Experimental outline and representative fluorescence images of co-culture assay; (b) Representative flow cytometry analysis of GFP expression in HME_d2GFP cells co-cultured with HME-RASER-dsRED (+HME-RASER-dsRED) cells after 14 days of induction with 4-OHT (IND) or not (NI) and quantification of GFP high population across time. Median±range (n=7); (c) ZEB1 expression in HME-d2GFP cells co-cultured with HME-RASER-dsRED cells after induction with 4-OHT and sorted at day 14 based on their GFP expression. Median±range (n=5 independent experiences in duplicate); (d) Representative flow cytometry analysis of CD24 and CD44 markers in HME_d2GFP cells co-cultured with HME-RASER-dsRED or HME-CTRL-dsRED cells, induced with 4-OHT (IND) or not (NI) for 21 days and analyzed at days 2, 7, 14, 22; (e) Transformation potential analysis as assessed by a soft agar colony formation assay of HME_d2GFP cells co-cultured with HME-RASER-dsRED (+HME-RASER-dsRED) or HME-CTRL-dsRED (+HME-CTRL-dsRED) cells, induced with 4-

Fig

  Fig.7| IL-6 and IL-1α are major cytokines responsible for ZEB1 cellular plasticity. a, Quantification of cytokine levels in the supernatant from HME-RASER cells after 4-OHT treatment compared to controls at days 4, 6, 8 10, 13, 17, 20 and 27. Data are presented as fold

  -/low /CD44 + in induced HME-RASER cells and induced HME-CTRL cells after 4-OHT treatment. Median± range (n=2 independent experiments in duplicate); d, Kinetics FACS analysis of cancer stem cell markers CD24 -/low /CD44 + /EpCAM + (as described by[START_REF] Al-Hajj | Prospective identification of tumorigenic breast cancer cells[END_REF] and CD44 + /CD104 + (as described by[START_REF] Kröger | Acquisition of a hybrid E/M state is essential for tumorigenicity of basal breast cancer cells[END_REF] in induced HME-RASER cells and HME-CTRL cells after 4-OHT treatment. Median± range (n=2); e, Transformation potential analysis by soft agar colony formation assay. Representative images of phase-contrast colonies (Scale bars, 200 µm). The number of colonies (as defined by > 20 cells) is indicated. Median± SD (n=3); f, Representative images of multiimmunofluorescence staining for the indicated markers in cross-sections of organoids generated from HME-RASER cells (RASER) that were induced with 4-OHT (IND) or not (NI) for 21 days (Scale bars, 100 µm). (n=3). See Extended Data Figs. 2 and 3.

Acknowledgements

The authors would like to thank Aruni P. Senaratne for critical reading of the manuscript. We are grateful to Cancer Research Centre of Lyon-Centre Léon Bérard core facilities, the Cell Imaging Platform and the Flow Cytometry Core Facility. We acknowledge the help of Nicolas Gadot and Clementine Leneve (Research Pathology Platform East) for multiplex immunofluorescence stainings, Holger Heyn, Sara Ruiz Gil and Gustavo Rodriguez-Esteban (CNAG-CRG, Barcelona Institute of Science and Technology, Spain) for single-cell analyses. HDB was supported by Fondation pour la Recherche Médicale (FRM) and LabEx DEVweCAN. This work was supported by funding from the Ligue Nationale contre le Cancer (EL2016.LNCC/AIP and EL2019.LNCC/AIP) and SIRIC LYriCAN (INCa-DGOS-Inserm_12563). This work was additionally supported by funding from Inserm within the framework of the International Associated Laboratory between the Cancer Research Centre of Lyon, France, and the Victorian Comprehensive Cancer Centre of Melbourne, Australia (LIA/LEA 2016, ASC17019CSA) and from Le Cancer du sein. Parlons-en! association.

The graphical abstract was produced using Servier Medical Art (https://smart.servier.com).

Data availability

The raw FASTQ files and the processed single cell sequencing data of this study can be obtained from Gene Expression Omnibus (GEO). METABRIC data are available in the European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI) archive (accession number: EGAS00000000083) and from Supplementary Information in [START_REF] Curtis | The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups[END_REF] and [START_REF] Pereira | The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes[END_REF] .

TCGA BRCA RNAseq expression data were extracted as Fragments Per Kilobase Million (FPKM) values from the GDC data portal (https://portal.gdc.cancer.gov/). RNAseq expression data from the CCLE breast cell lines were extracted as RPKM values from the CCLE data portal (https://portals.broadinstitute.org/ccle).

Code availability

All original code is publicly available as of the date of the publication.

(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Corresponding authors

Correspondence to Alain Puisieux or Anne-Pierre Morel.

Competing interests

The authors declare no competing interests.


Jeanne Becdelievre

Xin Guan

Iuliia Dudko

Philippe Regreny

Nicolas Chauvin

Gilles Patriarche

Michel Gendry

Alexandre Danescu

Jose Penuelas
email: jose.penuelas@ec-lyon.fr

José Penuelas

Growing self-assisted GaAs nanowires up to 80 µm long by Molecular Beam Epitaxy

Keywords: ultra-long nanowires, Molecular beam epitaxy, VLS growth

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Introduction:

Ultra-long semiconducting nanowires (NWs) can be of interest for applications which require large length or large active areas, such as sensors [Cui 2001, Thelander 2006] or water splitting [Kamimura 2017]. Various methods have been used to fabricate ultra-longs NWs, as for example electrospinning for ZnO [Wu 2006], CVD for ZnO [Li 2008], Si [Kayes 2007] or III-V [Kim 2008], HVPE for GaAs [Ramdani 2010] or GaN [Avit 2014]. Due to its low growth rate, Molecular Beam Epitaxy (MBE), is rarely used to grow ultra-long NWs, whereas it allows integration on Si substrates of various semiconductors with high purity and a good verticality.

GaAs, due to its direct bandgap and its high carrier mobility, is a promising material for solar energy harvesting with photovoltaics [Vettori 2019] or photoelectrodes [Hu 2013, Cui 2021],

the NW geometry leading to an efficient light trapping. More recently it was demonstrated GaAs NWs can be used as templates for the growth of hexagonal Germanium [Fadaly 2020, Dudko 2021]. However, the maximal reported length of GaAs NWs grown by MBE, at the best of our knowledge, is about 25 µm [Braakman 2014] and typical growth time rarely exceeds 2 hours [Zhou 2017]. Achieving ultra-long NWs only requires to maintain the Vapor-Liquid-Solid (VLS) mechanism during the growth, which means to prevent the crystallization of the catalyst droplets. However, this can be challenging in the case of the self-assisted NWs since when reaching the ultra-long regime, the growth mechanism becomes more complex due to phenomena, such as molecular flux shadowing, becoming predominant and/or due to significant variations of the temperature appearing at the NW tip.

The aim of this work is to study the properties of ultra-long nanowires grown by MBE. In particular, we focus on the NW growth kinetic and their structural and optical properties. By prolonging the MBE growth time we achieve a maximum length of 80 µm for self-assisted GaAs NWs grown on Si.

Nanowire growth

Self-assisted GaAs NWs have been grown on substrates by solid source MBE.

Before being introduced in ultrahigh vacuum, the epi-ready Si substrates were cleaned for 5 min with ultrasonic waves in acetone then in ethanol in order to remove surface contaminations. Si substrate temperature was increased to 500°C and 1 monolayer of Ga was deposited at a deposition rate of 1.4 Å/s on the substrate which was still covered by its 2 nm-thick native oxide layer. Then, the substrate temperature was increased up to the growth temperature at 610°C. During this step, Ga droplets react with the oxide layer to form volatile oxides with the formation of holes [Matteini 2015, Fouquat 2019]. It allows the epitaxial growth of the GaAs NWs on the Si substrate [Bailly 2022]. The Ga flux used for the NW growth was 2.1 Å/s in unit of equivalent 2D GaAs growth rate [Rudolph 2011] and the As4 beam equivalent pressure was set at 7.6x10 -6 mbar which corresponds to a As/Ga flux ratio of 4.8. Ga shutter and As valve were opened at the same time to initiate the NW growth and closed simultaneously in order to avoid the crystallization of the droplet and to confirm the VLS process.

Growths were carried out for various durations, from 5 min to 12 hours. A representative SEM image of the NWs grown for 12 hours is shown in figure 1(a). The GaAs NWs are almost all vertical (see the SEM images of the Supporting Information section). We can also notice in figure 1(a) that two groups of NWs are present. NWs from the first group still have a Ga droplet on the top, as illustrated on figure 1(b). The diameter of the Ga droplet is around 1 µm and the length of the NWs is about 80 µm. These NWs present a strong inverted tapering with a diameter increasing from 80 nm at the base to 700 nm just underneath the droplet. NWs from the second group, observable on figure 1(c), exhibit a facetted crystal on the top. For these NWs, the VLS process has not been kept during the whole growth. The droplet has crystallized during the process, stopping the axial growth. The origin of this crystallization phenomena could be the consequence of a strong V/III ratio supply in the droplet. The origin of the droplet crystallization is not clear and is not studied in this study. In the rest of this article, we are only interested by the first group of NWs which are representative of the VLS process.

Growth kinetic of ultralong NWs

The evolutions of the NW length and diameter as a function of the growth time can be seen on the figure 2(a). For growth times shorter than 2 hours, the relation between the NW length and the growth time is linear, as expected for the VLS process [Colombo 2008]. However, the slope of the curve changes at 2 hours. The decrease of the growth rate of long NWs is not expected by the Glas et al. model [Glas 2013]. To examine furthermore this phenomenon, we plot the axial growth rate as a function of the radius of the NWs beneath the droplet. Glas' model predicts an almost stationary axial growth rate for radii between 50 and 100 nm and even if larger radii haven't been studied, a similar behavior is expected. As we can see on figure 2(b), we have a different result. For radii larger than 100 nm, the axial growth rate drops. The classical scenario of a self-regulated evolution of the NW growth is based on the following arguments: in the Ga-rich regime, since the droplet radius increases and the V/III ratio is always larger than 1, the amount of As entering the droplet by direct impingement will stabilize the droplet size so as to obtain a stationary growth regime (here by stationary growth regime we understand constant axial growth rate at a fixed NW diameter). At the opposite, in a Ga-poor regime, the droplet radius decreases so that both Ga and As quantities entering the droplet by direct impingement decrease. But the droplet radius variation does not affect the amount of Ga feeding the droplet via the diffusion along the NW facets so that this contribution will also stabilize the droplet radius. However, small deviations from the stationary growth regime cannot be observed at short growth times. Experimental data in figure 2 However, the remaining experimental data do not fit this short-time behavior and the deviation needs an interpretation. Several explanations can be put forward: the temperature at the top of the NW (i), the droplet morphology (ii) and the incorporation of arsenic in the droplet (iii).

(i) For ultra-longs NWs the temperature isn't homogenous along the NW and a temperature drop of about 50 K can be expected (see details in Supporting 3). Consequently, in the following of this article we will consider that elementary As is desorbed from the GaAs surface during the VLS growth.

(ii)

The ultra-longs nanowires presented here have a very large droplet, with a diameter larger than 1 µm. A calculation has been carried out to check if arsenic diffusion length is sufficient to reach the liquid-solid interface where the growth occurs. To do this, we have calculated the length L=√𝐷𝜏 𝑐 covered by an arsenic atom in the droplet during τc = 0.1 s, the time needed to create one monolayer of GaAs. We have used the Stokes-Einstein equation to determine an approximated value of the diffusion coefficient D of arsenic in liquid gallium:

𝐷 = 𝑘 𝐵 𝑇 6𝜋 𝑟 𝐴𝑠 𝜇 𝐺𝑎 ~ 6.0 × 10 -5 𝑐𝑚² 𝑠 -1 (eq1)
The computed value is coherent with already obtained results in [Gorokhov 1984]. In this equation, kB is the Boltzmann coefficient, T the temperature, rAs the radius of the arsenic atom and µGa the gallium viscosity [Spells 1936]. At last, we found a length L ~ 25 µ𝑚 considerably larger than the droplet diameter. This result is in good agreement with previous results on gold-assisted growth of silicon NWs where it has been shown that whatever the diameter of the droplet, the growth rate is stationary [Kodambaka 2006].

(iii) Among the arguments advanced in the literature the retro-diffusion of As was mentioned in [Ramdani 2013]. In this model, arsenic atoms reemitted from other NWs and substrate incorporate the droplet to participate to the axial growth. Due to its large length, a strong screening effect occurs and arsenic atoms could not reach the droplet and only participate to the radial growth. It would explain the growth rate drop when the growth time increases. From a qualitative point of view the correlation between the As amount needed for the solidification and the time evolution of the droplet free surface area may be explained as follows: when the length of the NW scales as 𝑡 𝛼 and the radius of the NW scales as 𝑡 𝛽 the amount of As (or Ga) atoms in a small time-interval scales like 𝑡 𝛼+2𝛽-1 . Meanwhile at constant wetting angle the radius of the droplet scales like 𝑡 𝛽 so that the free-surface area of the droplet scales like 𝑡 2𝛽 . An excellent correlation is then obtained only when 𝛼 = 1, which is the case in the short-time behaviour. From a quantitative point of view we notice a significant variation of the amount of As (and Ga) atoms needed for the NW growth process: from 50.1 × 10 6 atoms/s at the beginning of the growth to 76.7 × 10 8 atoms/s after 10 hours. Compared to this, the deficit of As entering the droplet varies from 37.2 × 10 3 atoms/s at the beginning of the growth to 10.4 × 10 5 atoms/s after 10 hours. Thus, since the As deficit (or Ga Excess) is three order of magnitude lower than the amount of atoms entering the solidification process, only long-time growth experiments can obtain evidence of it.

Structure and optical properties

TEM characterizations have been lead on ultra-long GaAs NWs. The structure of the NWs is mostly Zinc Blende (ZB). The base of the NWs is purely ZB, twins appear after several microns and their density is increasing along the NWs, as it can be seen on figure 4 in order to obtain desired properties [Li 2021]. These ultra-long NWs also present crystallographic particularities. In the middle of the NWs, we found notches on the ridges of the NWs, as we can observe on figure 4(c). A WZ segment can be noticed at each notch, see figure 4(d). The WZ segment seems to be a point of departure of the notches formation. We think these defects are the consequence of the GaAs decomposition due to the high growth temperature and the lack of arsenic already highlighted via the discussion on the growth kinetic.

At the bottom of the NWs, we found holes with an increasing density near the base, see figure

4(e). In the area where the hole density is low, these defects are aligned perpendicularly to the growth axis. Recently, thanks to in situ TEM experiments it was found that during heating of GaAs NWs, the WZ phase evaporates before the ZB one [Gang 2021]. It can thus be noted that the holes are formed at the NW foots which are pure ZB phase. In our case, the lack of As could explain this phenomenon, since the congruent evaporation temperature of GaAs is directly controlled by the As molecular flux [Zhou 2012]. In order to confirm this hypothesis we performed VLS growth of ultralong NWs under As-rich condition and low density (see figure S6). The NWs grown under As-rich condition do not show any structural defects from SEM observation, and no tapering is observed, which means the ultralong NWs morphology and structure can be partially controlled by tuning the V/III ratio during the growth.

The photoluminescence (PL) measurements are performed in a closed cycle cryostat, using a 532 nm continuous wave laser as the excitation source and a liquid nitrogen cooled silicon CCD detector coupled to a monochromator for the detection. To investigate the dynamics, timeresolved measurements were performed at 14 K using a 200-fs pulsed laser emitting at 515 nm with a 54 MHz repetition rate. The PL of the NWs is analyzed by a spectrometer and a synchronized streak camera with a temporal resolution of 20 ps. PL measurements are performed on the as-grown sample. Therefore, the laser is completely absorbed in the upper part of these ultra-long NWs. PL performed at 12 K reveals a single peak whose energy blueshifts from 1.442 eV to 1.447 eV along with the excitation power increase (see figure 5 (a)). This peak is clearly below the exciton emission in ZB GaAs (1.516 eV) and WZ GaAs

Conclusion and perspectives:

In conclusion, we have managed to growth ultra-long GaAs nanowires by MBE. We have noticed a decrease of the growth rate after 2 hours of growth and attributed this phenomenon to a lack of arsenic in the droplet due to a screening effect and to radial growth. This lack of arsenic seems also at the origin of notches and holes in the nanowires. To confirm this hypothesis, in situ heating experiments as in ref [Fouquat 2021] could be performed. It should be noted that for application with ultra-long GaAs nanowires a particular attention has to be given to the degradation on the bottom of the nanowires due to the possible lack of Arsenic.

Figures

Temperature profile in ultra-long NWs

To calculate the temperature at the tip of a cylindrical NW, we use the model developed by

Frank Glas and Jean-Christophe Harmand in [Glas 2006]. This temperature is calculated numerically using the equation: where 𝑟 0 = 100 𝑛𝑚 is the NW radius, 𝜎 the Stefan-Boltzmann's constant and 𝜅 the thermal conductivity. This thermal conductivity is equal to 19 Wm -1 K -1 at 883 K for bulk GaAs [Maycock 1967]. However, it is known that 𝜅 can have a smaller value in semiconducting NWs. In Ref [Martin 2010], it is theoretically predicted that, for a 100 nm radius GaAs NW, 𝜅 is decreased by a factor of two in the case of a NW with ideally smooth surfaces and could be reduced by a factor of 10 in the case of a NW with rough surfaces. Fig S2 shows the temperature at the tip of a 100 nm radius GaAs NW as a function of the NW length for three different thermal conductivities: 19 Wm -1 K -1 (bulk GaAs), 10 Wm -1 K -1 (NW with smooth surfaces), 2 Wm -1 K -1 (NW with rough surfaces). We can see on this graph that the temperature drop is at least of 45 K for an 80 µm long NW. At room temperature, a shoulder located at 1.42 eV, associated to the ZB GaAs emission, is observed, as a consequence of the thermal activation of the electrons from the WZ segment towards the ZB sections. As far as the PL integrated intensity is concerned, a decrease by more than 4 orders of magnitude is reported when the temperature is increased from 12 K to 300 K. The fact that the NWs are unpassivated could explain this result. A 5 orders of magnitude PL decrease has already been reported for unpassivated gold catalyzed ZB GaAs NWs [Breuer 2011]. This strong decrease is also probably related to the carrier separation. The thermal activation of non-radiative decay channels induces a competition with the radiative recombinations. Due to the weak radiative decay rate, the non-radiative processes can easily take over the radiative ones.

𝑙 = 𝛬

Growing ultralong nanowire under higher As flux

Tuning the As flux during the growth of self-assisted GaAs NWs is known to produce a modification of their morphology and crystal phase. By increasing the As molecular beam flux we obtained NWs without inverse tapering. It is expected the crystalline structure can be modified by careful adjustment of the V/III ratio. 89, 5815 (2001)

  Substrates were then outgassed in ultrahigh vacuum for 20 min at 200°C. GaAs NWs were grown in a Riber MBE reactor dedicated to III-V semiconductors equipped with a Reflection High Energy Electron Diffraction (RHEED) facility. The MBE reactor is equipped with a Ga cell with a flux incidence angle equal to 27.9°, and an As4 valved cracker cell with a flux incidence angle equal to 41° [Vettori 2019b]. The substrate temperature was controlled by a calibrated thermocouple and checked by a pyrometer. During growth the sample holder was continuously rotated to enhance the growth homogeneity. Self-assisted GaAs NWs were grown by MBE using the VLS method with Ga droplets [Foncuberta 2008, Jabeen 2008, Ramdani 2013, Priante 2013, Matteini 2015, Dubrovskii 2015, Guan 2016a, Guan 2016b]. First, the

  provide a straightforward way to compute the amount of Ga and As atoms which participate to the solidification process in the [𝑡 𝑖 , 𝑡 𝑖+1 ] time interval. In the following, to obtain good accuracy, we use linear interpolation of the experimental data with a fixed time-step ∆𝑡 = 432 s. From the experimental data we compute the solid volume grown in the [𝑘∆𝑡, (𝑘 + 1)∆𝑡] time interval by using the interpolated values for the NW radius and NW length. Obviously, the amount of solid volume grown in the [𝑘∆𝑡, (𝑘 + 1)∆𝑡] time interval is ∆𝑉 𝑘 this can be further converted so as to obtain the (equal) amount of Ga and As atoms that participate to the solidification process in the [𝑘∆𝑡, (𝑘 + 1)∆𝑡] time interval. But the tappering observed during the growth (see figureS1(d)) shows that the droplet contact angle is fixed at its maximum value in which case, from the NW radius, we can compute the droplet radius at 𝑡 𝑘 = 𝑘∆𝑡 further denoted 𝑅 𝑘 . Using the droplet radius and the NW radius we compute the (time-dependent) area of the free-surface of the droplet 𝑆 𝑘 𝑑𝑟𝑜𝑝 intercepted by the flux and we can check the correlation between the free-surface of the droplet and the equal amount of Ga and As atoms used for solidification in the [𝑘∆𝑡, (𝑘 + 1)∆𝑡] time interval. Figure3shows an excellent correlation between these two quantities obtained for a constant value of the As (and Ga) flux of 1.7 × 10 7 atoms/ (nm².s) in the [0, 115 min] time range. We can conclude the quantity of As which participate to the growth is directly proportional to the droplet surface. By consequence, incorporated As is only originated from the droplet and not from the diffusion along the facets.

  (a). Near the top, Wurtzite (WZ) inclusions appear, as shown on figure4(b) and a large WZ segment is present underneath the droplet[Dursap 2020]. The presence of two crystal phase in self-assisted GaAs NWs is well known[Panciera 2020] and can be in a certain extent controlled[Dursap 2021]

( 1 .

 1 524 eV) as reported in[Dursap 2021]. Moreover, the PL dynamics reveal a decay time longer than 10 ns (see figure5 (b)). If the inhibition of the radiative lifetime is possible in high refractive index materials[Claudon 2013], the diameter of the investigated NWs discards this hypothesis. The long decay time, the emission below the band gap and the PL blueshift are signatures of carrier separations induced by the WZ-ZB phase mixing, in agreement with the TEM results, which creates a type II band alignment in GaAs NWs[Heiss 2011, Vainorius 2014, Vainorius 2015]. The observation of a PL emission centered between 1.44 eV and 1.45 eV is associated to relatively thick WZ insertions: in Ref[Vainorius 2015], these energies were obtained for 2 to 3 nm long WZ GaAs QDs in ZB GaAs NWs. These lengths agree with the length of the WZ insertions measured by TEM where the notches are observed. The PL emission was also investigated as a function of the temperature (see figureS4), revealing a typical character of unpassivated GaAs NWs. Then, micro-photoluminescence experiments were performed to investigate the optical properties at the single NW level. GaAs NWs have been transferred on a TEM grid and inserted into a low-vibration closed cycle cryostat. For theses measurement, a cw 671 nm laser with a 5 µm spot size and a power of 500 µW was used for the excitation and a liquid nitrogen cooled silicon CCD detector coupled to a monochromator for the detection. Fig 5 (c) shows the µ-PL spectrum of a single GaAs NW excited in the center where several peaks are observed. The low energy peak at 1.431 eV is associated to the thick WZ GaAs insertions as observed in the PL experiments. Other peaks are observed at higher energy up to 1.491 eV. These peaks are associated to thinner WZ insertions. For instance, Martin Heiss et al report a µ-PL emission in the 1.49-1.5 eV range for 2-3 MLthick WZ GaAs insertions [Heiss 2011], whereas Bolinnsson et al observed a peak at 1.48 eV related to rotational twins [Bolinsson 2014]. No emission associated to the ZB GaAs is observed which can be explained by a high density of WZ insertions and twins: in Ref [Bolinsson 2014], pure ZB GaAs segments longer than 35 nm were required to observe the free exciton emission from ZB GaAs at cryogenic temperature.

Fig

  Fig 1: SEM side view of ultra-long GaAs NWs grown during 720 min (a). Two groups of NWs are clearly evidenced. In the first group the NWs exhibit a droplet on their top, which confirms the VLS growth mode (b) while in the second group the NWs are shorter and exhibit a GaAs crystallite on their top (c).

  Fig 1: SEM side view of ultra-long GaAs NWs grown during 720 min (a). Two groups of NWs are clearly evidenced. In the first group the NWs exhibit a droplet on their top, which confirms the VLS growth mode (b) while in the second group the NWs are shorter and exhibit a GaAs crystallite on their top (c).

Fig 2 :

 2 Fig 2: (a) Length and diameter of GaAs NWs as a function of the growth time. (b) Axial growth rate as function of the NW radius beneath the droplet.

Fig 3 :

 3 Fig 3: The evolution of the computed (equal) amount of As (or Ga) for each time interval [kΔt, (k + 1)Δt] for k = 1 . . . 16 and Δt = 432 s (in red) and the amount of As entering the droplet by direct impingement (in blue) computed as the product between the fixed-value of As-flux equal to 1.7 × 10 7 atoms/(nm².s) and the time dependent surface of the droplet exposed to the As flux.

Fig 4 :

 4 Fig 4: TEM images of: (a) twins in the ZB phase and (b) WZ inclusions. Insets: corresponding diffraction patterns. (c) TEM image of an ultra-long GaAs NW where notches can be observed and (d) the corresponding high resolution image where a WZ segment can be observed. (e) TEM image of the bottom an ultra-long GaAs NW where a high density of holes can be observed.

Fig S2 :Fig

 S2 Fig S2: Temperature at the tip of the NW as a function of its length, for different thermal conductivities.

Fig

  Fig S4: (a) High resolution TEM image of a WZ segment (highlighted in yellow) surounded by

Figure S6 :

 S6 Figure S6: SEM image of ultralong NWs grown during 6 hours with higher As flux.

  Information section). Yet, a temperature drop should induce an increase of the growth rate[Glas 2013], so it cannot explain the observed specific growth kinetic of ultra-long NWs. However, it should be noted that if we consider a mechanism of

As diffusion on the GaAs facets as suggested by Scarpellini et al. [Scarpellini 2017], then the temperature drop would induce a decrease of the As diffusion and consequently a decrease of the axial growth rate. However this additional source of elementary As is expected to occur at temperatures of about 400°C, which is significantly different from our calculation (see figure

  𝜏 𝑙 = 𝑇 𝑙 𝑇 𝑠 ⁄ with 𝑇 𝑙 the temperature at the tip of a NW of length 𝑙 and 𝑇 𝑠 = 883 𝐾 the temperature of the substrate. The absorption 𝛼 is assumed to be equal to the emissivity. At 610°C, we have 𝜖 ≈ 0.3 [Timans 1992]. The characteristic length 𝛬 is equal to:

				𝜅𝑟 0 8𝜖𝜎𝑇 𝑠 𝛬 = √ 3
	∫ [ 1 𝜏 𝑙	1 10	(𝜏 5 -𝜏 𝑙	5 ) -	𝛼 4	(𝜏 -𝜏 𝑙 ) + ( 8𝛬 𝑟 0	) 2	𝜏 𝑙	8 ]	-1 2 ⁄	𝑑𝜏

where

Acknowledgements:

Chinese Scholarship Council (CSC) is acknowledged for its financial support. The authors thank the NanoLyon platform for access to equipment, J. B. Goure for technical assistance.