The swelling was concluded to contribute to the control of phthalate plasticisers, the use of which in wire harness has recently been limited by the Restriction of Hazardous Substances (RoHS) directive. Cables swollen with n-butyl acetate or acetone were subjected to dry ball milling for ~ 60 min to completely separate PVC and Cu and achieve the quantitative recovery of these components from 20-cm-long cables. Thus, this work unveils the high potential of recycling the otherwise non-recyclable long and non-uniform waste wire harness cables and is expected to impact the related  industries, contributing to the establishment of a more sustainable society.Poly(vinyl chloride) (PVC) swelling coupled with ball milling was employed for the simultaneous recovery of high-purity Cu and PVC from waste wire harness under ambient conditions. The experimentally determined performances of 15 organic solvents for PVC swelling and phthalate plasticiser extraction were compared with those predicted considering Hansen solubility parameters. As a result,  Cu exhibits good electrical conductivity, and the corresponding applications have a 58% share of the global Cu demand , with 30% of this demand fulfilled by recycling2. PVC is well suited for coating Cu wire strands and is commonly made more flexible through the incorporation of plasticisers such as diisononyl phthalate (DINP) and di-2-ethylhexyl phthalate at loadings of 18–40 wt%3. Wire harness is the major component of the cable waste stream, being hard to process because of the low thickness and non-uniformity of the cable assembly, which usually consists of Cu strands coated with a single layer of PVC4.Electric cables are indispensable for electricity/information transmission and contain poly(vinyl chloride) (PVC) and Cu as major constituents accounting for almost 16 and 42%, respectively, of the total material consumption in cable production5). On average, a vehicle contains ~ 30 kg of Cu, with wire harness accounting for 50% of this value6. On the other hand, wire harness accounts for only 2% of the 50 million tonnes of e-waste generated annually7. As Cu has a higher economic value than PVC, the recycling of wire harness in some places of India8, China9, and Africa10 is performed by open burning to recover Cu. As PVC contains ~ 57% chlorine by weight and thus emits undesired HCl11 (and possibly dioxins12) upon burning, this recycling method poses a threat to the environment. Moreover, the pigments and stabilisers used in cable PVC coating may contain hazardous Pb and Cd compounds, and the landfilling of burnt PVC residues may therefore result in soil and water contamination8.The wire harness assembly, used to conduct electricity to the components of vehicles and electrical/electronic appliances, is abundantly present in waste electrical/electronic equipment and end-of-life vehicles (ELVs), the production of which is steadily increasing  resulted in a > 80% Cu recovery (as CuCl) at 900 °C. Despite being effective for cable residues, this method is not environmentally friendly because of its high thermal energy consumption. The ability of PVC to be dissolved with organic solvents has been employed in the recycling of PVC-coated polyethylene terephthalate fibre16. Moreover, Solvay (VINILOOP) recycled waste PVC cables17 by dissolution of the PVC coating to recover Cu, with subsequent PVC recovery achieved by precipitation. The advantages of this technique are the avoidance of pre-treatments such sorting/cutting and the recovery of high-grade Cu, while the drawbacks are the multistep processes for plasticiser recovery and solvent regeneration.In view of the above, much effort has been directed at the development of a safe and competitive method of PVC and Cu recycling. Koyanaka et al19. In this case, 1-cm-long cables were subjected to quantitative plasticiser extraction and then ball milled to achieve full recovery of ~ 100% pure Cu, although the recycling of longer or non-uniform cables was problematic. To retain existing PVC properties, Xu et al.21 developed a method based on swelling and mechanical agitation. Specifically, swelling created a gap between Cu and the PVC coating and thus facilitated their separation, obviating the need for PVC dissolution. More importantly, the above authors realised the recovery of plasticiser-free PVC using organic solvents. However, only cables less than 5 cm long could be recycled by this method.Thin cable recycling has also been attempted by extraction-induced PVC embrittlement followed by crushing22 by blending23, and the retrieved plasticisers can be re-used in the PVC industry.The advantages and disadvantages of the abovementioned techniques highlight the need for a process allowing one to recycle long, thin, and non-uniform cables and achieve high recoveries of pure Cu. Moreover, the controlled (or complete) extraction of plasticisers must be taken into account, as phthalates are categorised as hazardous substances according to the Restriction of Hazardous Substances (RoHS) directive. Fully plasticiser-free PVC can be incorporated into flooring and drain/sewer pipesHerein, we show that non-uniform, long, and thin cables can be recycled in an environmentally friendly way by adequate swelling of their PVC coating followed by ball milling to afford Cu and solid PVC Fig. . The PVC1H NMR analysis revealed that samples contained DINP as a plasticiser at a content of 21.4 wt% , moderate , adequate , partial dissolution (4-methyl-2-pentanone (MiBK), 4 ≤ Rswel), and complete dissolution  groups. THF and MiBK, which completely and partially dissolved PVC coatings, respectively, were thus considered unsuitable for swelling. Although the order of Rswel did not completely match that of Ra [S-PVC], solvents suitable for adequate PVC swelling were concluded to have Ra [S-PVC] = 6–9. A detailed discussion regarding the effects of HSP on PVC swelling and the corresponding mechanism are provided in Supplementary Swelling allows the PVC coating to be easily separated by crushing in a ball mill reactor. Herein, 15 solvents covering a broad range of solvent-PVC HSP interaction distances (d by Eq.  were selRa [S-DINP]). The best plasticiser extraction yield (Yext) of ~ 60% was observed for shaking-free 80-min extraction with acetone and n-butyl acetate  were comparable to those of the latter (97%) at 80 min. Thus, almost complete DINP extraction could be achieved using supportive shaking motion, whereas this motion did not influence PVC swelling.Figure ate Fig. a. Althoun-butyl acetate, and ethyl acetate were selected as inexpensive and safe 24 solvents for further experiments.Thus, solvents adequately swelling PVC without its dissolution were concluded to be appropriate for subsequent dry ball milling, while solvents with high DINP extraction abilities were found to be beneficial for the control of RoHS-regulated phthalate plasticisers. Thus, taking the cost factor into account, acetone, 25. Herein, the dissolution of PVC in THF was controlled by chain diffusion, and no swelling interface was observed before dissolution. At sufficiently high solvent penetration and swelling power, the swollen layer starts to be disentangled, as in the case of MiBK. Furthermore, the PVC coating contained encapsulated DINP, which could be released by swelling or chemical interactions. However, swelling is known to expand the PVC matrix and thus facilitate extraction. Hence, boundary layer resistance appears, particularly when the solvent affinity to PVC is low. Solvent molecules interact with incorporated DINP molecules at the outer boundary layer and initiate extraction to create voids (free surface), which allows for the further diffusion of solvent molecules and their interaction with deeper-lying DINP molecules. This process results in weight loss (shrinkage), as the solvent finally leaves the occupied voids. Figure n-butyl acetate, n-propyl acetate, and 1,4-dioxane featured a slow swelling extent that was maximised at the end of the swelling time, leaving adjacent PVC chains unaffected. However, EA achieved a very limited swelling extent. Notably, only shrinkage was observed in diethyl ether and petroleum ether, i.e., these solvents engaged in favourable chemical interactions with DINP but not with PVC. To validate shrinkage behaviour of PVC by weight loss into diethyl ether, petroleum ether, and ethanol, herein Rswel tests of fully deplasticized were conducted, as results Rswel exhibited unity for all three respective solvents after 60-min submersion, indicate that there was no weight loss occurred during swelling tests, while as Rswel was tested < 1 for plasticised cables. Moreover, the Rswel values of specimens (see Table Polymer dissolution is controlled by either the disentanglement of chains or their diffusion at the polymer (solid)-solvent interfaceRa [S-PVC], was positively correlated with the extent of solvation. Polar forces such as those due to δd and δh (as opposed to forces due to δd) are highly directional and greatly influence solvation. For example, ethanol exhibited δd = 15.8, which was close to that of THF (16.8); however, THF dissolved PVC (δd = 18.8), while ethanol completely incompetent solvent in solvation of PVC, makes sense that δp and δh substantially influence PVC solvation. Considering δp and δh to describe the order of solvation, we note that solvents  with either low or very high δp and δh [(2.9 ≤ δp ≥ 0) and (5.1 ≤ δh ≥ 0)] induced no significant chemical changes of PVC, which has δp = 9.2 and δh = 6.3. Still, shrinkage (Rswel < 1) was observed for diethyl ether/petroleum ether because of plasticiser extraction from the PVC matrix. Moreover, while PVC-diethyl ether/petroleum secondary forces are exceptionally weaker for PVC swelling, shrinkage may impart due to fact that induced secondary forces of PVC–PVC attempt to contract chains by occupied free space . Unlike the above ethers, benzene achieved a certain degree of PVC swelling, which was ascribed to the interaction of the benzene π-electron cloud with PVC molecules. However, this hypothesis is not backed up by evidence. Another viewpoint on polymer solubility26 that is difference in free volume between the polymer and solvent liquids, i.e., dissimilarity mainly due to differences of sizes of solvent molecules and polymers molecules , may influence polymer solvation, aside of induced contact energy dissimilarities. Likely, Gilbert27 also highlighted the contribution of molecular size and chemical structure to polymer solvation. The δp and δh deviate in small for THF , and EA , wherein THF dissolve PVC, while EA Rswel exhibited < 3, is very far from PVC dissolution. Although, induced forces are important for discussion but hard to explain individual influence of each on PVC solvation. In the same way, n-butyl acetate (δp = 3.7) achieved a larger swelling extent than EA (δp = 5.3), although the δp of the latter is much closer to that of PVC (9.2). Remarkably, 1,4-dioxane (δp = 1.8) showed an Rswel equivalent to that of n-butyl acetate, which was ascribed to the fact that the δh of the former solvent (9) significantly exceeds that of the latter (6.3). Thus, one decisively cannot predict solvation by considering only δp. The worthy to note that high electronegativity of chlorine makes the neighbouring carbon atom of PVC more prone to nucleophilic attack by electron-donating groups16. The polarisation due to chlorine may take into consideration but cannot be justified distinct behaviour of PVC solvation into respective solvents. Therefore, we employed the sum of δp and δh forces 0.5) as a prediction parameter. For two solvents with similar δtp,h, such as MiBK and n-butyl acetate, one can definitely say that δp has a larger contribution than δh. However, abovementioned explanations could not directing such significant order for δp and δp to define the degree of PVC solvation. Here, we can make a general statement that the different proportionating value of δp and δh may show substantial solvation. The solvents which have δtp,h in the range of 7.3–12.5, is about closer or similar to the δtp,h (11.2) of PVC, showed significant solvation of PVC. Thus, water did not achieve PVC solvation or extraction because of the unfavourable secondary force–induced interactions. At this point, it is worth re-emphasising that polar forces  are highly directional, while those due to δd are omnidirectional. Conclusively, PVC matrix swelling occur when involved secondary polar forces in PVC-solvent would greatly compatible, whereas chains may stretch . This phenomena described as PVC/DINP ≈ solvent for polar solvents. Likewise, the shrinkage condition stated as DINP ≈ solvent ≪ PVC for non-polar solvents. Figure Notably, the solvent-PVC HSP interaction distance, Yrecy) was determined by 1H NMR, with detailed analytical procedures summarised in Supplementary Yrecy values of 99, 98, and 91% were obtained for acetone, n-butyl acetate, and ethyl acetate, respectively,  regeneration efficiency of distillation was determined as ~ 90%  samples were prepared by 80-min swelling with shaking, fed into the ball mill reactor containing 20-mm-diameter balls, and crushed at 45 rpm for a maximum time of 80 min. Figures Ysep of 5-, 10-, and 20-cm swollen cables, respectively, showing that in the case of n-butyl acetate and acetone, complete separation of 5-cm cables was achieved after 40 min, while in the case of ethyl acetate, complete separation required 60 min. The milling time required for complete separation increased with increasing cable length, e.g., in cases of n-butyl acetate and acetone, complete separation of 20-cm cables was achieved after 55 and 65 min, respectively. The high volatility of acetone resulted in quick de-swelling and, hence, in a longer ball milling time. On the other hand, for ethyl acetate, Ysep values of 63 and 21% were obtained for 10- and 20-cm cables after 80 min, i.e., complete separation could not be realised. As expected, no separation was observed for untreated cables. Thus, we concluded that Rswel strongly affects the efficiency of ball milling–induced Cu-PVC separation , polar interaction (δp), and hydrogen bonding (δh) forces. The cohesion energy for PVC-PVC or PVC-DINP separation depends on the participating polar/nonpolar groups and contributing forces. The HSP interaction distances between solvents and PVC (Ra [S-PVC]) and between solvents and DINP (Ra [S-DINP]) were calculated and defined as28The HSP concept considers the action of three general secondary forces between molecules (nonelectrolytes), namely dispersion interaction (Rswel) asV0 and Vs [g/cm3] are the cable volumes before and after the swelling test, respectively.The collected waste cables were cut into 7-cm-long pieces and submerged into organic solvents (100 mL) in sealed 110-mL glass bottles at room temperature for 20, 40, 60, and 80 min. For comparison, submersion tests were also conducted using a water bath shaker  with horizontal shaking motion (150 rpm). For each group of tests, ten cables with different textures  was determined aswf [g] and wf0 [g] are weights of the flask after distillation and the empty flask, respectively, while WDINP in PVC [g] is the amount of DINP in the 12.8-g waste cable sample  in 110-mL sealed glass bottles for 20 or 80 min at ambient temperature. Moreover, cable samples of identical weight were tested with shaking using a water bath shaker for 20, 40, 60, and 80 min at ambient temperature. After the extraction test, the spent solvent was collected into a 250-mL round-bottom flask and evaporated using a rotary evaporator  at a flask rotation speed of 30 rpm. The water bath temperature was set to 30–60 °C according to solvent volatility. The solvent condenser was cooled to − 5 to − 10 °C by a chiller  in all experiments. The distillation pressure was controlled using a pressure-controllable vacuum pump  and was set to 560, 350, 250, 180, 170, 180, or 175 hPa for acetone, THF, ethyl acetate, Yrecy) were determined by 1H NMR  using fumaric acid as an internal standard21, with details provided in Supplementary The yields of plasticiser recycled from extraction solvents (Ysep) were determined asm0 and ms [g] are the total sample weight after the milling test and the total weight of separated Cu and PVC after the milling test, respectively.After swelling and simultaneous plasticiser recovery, the swollen cables were immediately transferred to the ball mill reactor for PVC separation, as schematically illustrated in Fig. Supplementary information


Skin consists of the epidermis and dermis, which are connected by a specialized basement membrane—the epidermal basement membrane. Both the epidermal basement membrane and the underlying interstitial extracellular matrix (ECM) created by dermal fibroblasts contain distinct network-forming macromolecules. These matrices play various roles in order to maintain skin homeostasis and integrity. Within this complex interplay of cells and matrices, cell surface receptors play essential roles not only for inside-out and outside-in signaling, but also for establishing mechanical and biochemical properties of skin. Already minor modulations of this multifactorial cross-talk can lead to severe and systemic diseases. In this review, major epidermal and dermal cell surface receptors will be addressed with respect to their interactions with matrix components as well as their roles in fibrotic, inflammatory or tumorigenic skin diseases. The epidermis includes the innermost ne cells ,2. Additne cells . The derne cells ,2. Even ne cells , the majne cells , which pne cells . The ECMne cells . This inne cells . Moreovene cells ,7,8. Bione cells .Both the epidermal basement membrane and the interstitial ECM interact with the cells they tangent or embed in a bidirectional manner in order to create a functioning homeostatic and integer tissue. In this context, a crucial role is assigned to cell surface receptors, since they coordinate cell-signaling events for ECM synthesis, degradation or remodeling. At the same time, they respond to external physical or chemical stimuli and communicate these into cellular responses, such as proliferation, differentiation or migration.On the basis of these bidirectional cell–matrix interactions in skin, this review will provide a general overview of cell surface receptors in healthy, injured or diseased skin, with a focus on malignancies that involve unremitting inflammation, fibrosis and cancer.Integrins are the main adhesion proteins that bridge the cellular cytoskeleton with the extracellular matrix (ECM) and thereby serve as bidirectional signal transducers regulating cell proliferation, homeostasis, differentiation, adhesion, migration and apoptosis ,10,11,12At the plasma membrane, integrins undergo conformational changes to transform from an inactive form with low ligand affinity, sometimes over an intermediate form, to a high affinity form to a fully activated ligand-bound integrin . ImportaIn the classical model of integrin signaling, adaptor and signaling proteins intracellularly cluster around transmembrane integrins to generate focal adhesions, dynamic multi-protein structures that connect the ECM with the actin cytoskeleton ,33,34. TThe main integrins expressed in skin are: α2β1 , α3β1 4 α5β1 4040, α6β4 Integrin α6β4 is an integrin rather specialized to epithelial cells. In skin, high abundances of integrin α6β4 subunits are found in basal keratinocytes at their basal plasma membrane adjacent to the basement membrane ,42,43,44The integrin β4 differs from other subunits, due to its atypically large cytoplasmic domain containing four type III fibronectin-like repeats  and fiveITGA6 or ITGB4 gene, encoding for the integrin α6β4 subunits result in the skin blistering disease junctional epidermolysis bullosa (JEB) associated with pyloric atresia (JEB-PA) [ITGB4 mutations in part through formation of integrin α6β1 [Mutations in the (JEB-PA) . Integri(JEB-PA) ,54,55, irin α6β1 , which iIntegrin α6β4 also associates with the receptor tyrosine kinases EGFR family receptors ,57 throuDespite being essential for firm, stable cell adhesion evidence supports integrin α6β4 to promote tumor invasion and progression ,66,67,68The integrin subunit β1 pairs with the α1, α2, α3, α4, α5, α6, α7, α8, α9, α10, α11 and αV subunits ,75,76. BThe major constitutive integrin β1 integrins in skin are in the epidermis α2β1 and α3β1 . After wIntegrin α2β1 is in skin found along the lateral and apical surface of basal keratinocytes . It is cITGA3 encoding the integrin α3 subunit cause congenital nephrotic syndrome, interstitial lung disease, and skin fragility [Integrin α3β1 localizes at basolateral sites of basal keratinocytes , where iragility , which iragility . Both huragility ,98,99.As integrin α6β4, integrin α3β1 regulates keratinocyte migration. Keratinocytes isolated from integrin α3-deficient mice migrate faster and with increased directional persistence , they shIn the epidermis minor integrin, integrin α9β1 promotes re-epithelialization .stratum corneum. Additionally, hair follicle development was absent , integrin β1 is required for cell adhesion, spreading and dermal invasion, but, in contrast to normal keratinocytes, not for proliferation . Both ofIntegrin αV belongs to the non-I-domain group of α subunits  and heteIn healthy adult skin, integrin αV is present in the epidermis and dermis, where it reaches its maximal expression levels in the plasma membrane of proliferative basal keratinocytes . It is nTo generalize, αV integrins can be viewed as regenerative integrins, and are involved in multiple physiological and pathophysiological regenerative processes. In this context, their ability to activate latent TGFβ1 and 3 is an important trait. This activation occurs through the RGD sequence within the latency associated peptide LAP of TGFβ1 and TGFβ3 ,130,131.The integrin-mediated release of active TGFβ is facilitated by increased tissue stiffness , which lIn terms of its role in cell adhesion, Duperret et al.  found inαV integrins are players in wound healing and their roles appear contextual as both overexpression and loss of the same integrin can cause delay of healing ,142,143.Proteoglycans are macromolecules containing a core protein with one or more covalently bound glycosaminoglycan (GAG) chains . Either Syndecans are a family of transmembrane proteoglycans and in mammals they comprise four members   that areSyndecan ectodomains can undergo protease-mediated shedding. Syndecan-1 and syndecan-4 are cleaved by the matrix metalloproteinases MMP-2 ,173, MMPstratum spinosium and granulosum, and only weakly in basal keratinocytes; it is absent in the stratum corneum of intact skin [In skin, syndecan-1 and -4 are expressed with high abundances in the epidermis ,186. Synact skin ,188,189.act skin ,191,192 act skin . Human dact skin . Furtheract skin  but is eact skin . Syndecaact skin .stratum spinosum and stratum basale, and a disturbed organization of lower epidermal layers, while the suprabasal cells keep their cytoplasmic extensions reaching to the basement membrane [Loss of syndecan-1 expression has also been linked to decreased intercellular adhesion in acantholytic and spongiotic processes, which may foster blister formation in acantholytic or spongiotic dermatosis and also in pemphigus vulgaris or foliaceus  and thermembrane . Mechanimembrane ,199. Themembrane  found symembrane .Syndecans have been reported to have both pro- and anti-inflammatory effects, most likely depending on the underlying model, the tissue of focus or the stage of inflammation, as well as characteristics of their ectodomains . In the In wound healing, syndecans may regulate inflammation and cell proliferation. After injury, syndecan-1 becomes highly abundant in keratinocytes at wound margins ,188,201.Of specific importance for skin are interactions between syndecan-1 and -4 and laminin-332 ,205,206.Syndecan-1-mediated cellular adhesion to non-processed laminin-332 retaining LG4-5 has been shown to induce the formation of fascin-containing protrusions via the Rho GTPases Rac1 and cell division control protein Cdc42 . This inApart from laminin-332, collagen I, III and V  and protStudies indicate syndecan-4 to be involved in mechanotransduction and mechanosignaling ,227,228.Syndecan-1 in skin seems to protect against cancer initiation and progression. Its loss is associated with transformation of epithelia into anchorage-independent mesenchyme-like cells  and alsoThe transmembrane glycoprotein family CD44 belongs to the group of cell adhesion molecules  and is mIn humans, a single gene on the short arm of chromosome 11 with 19 exons encodes for various isoforms of CD44 ,239,261.2-terminal fragment [The extracellular domain of CD44 can be proteolytically cleaved by matrix metalloproteinases (MMPs) ,267, a dfragment  and therfragment . Ectodomfragment . On the fragment  is generfragment ,258, whifragment . CD44ICDfragment ,268. Intstratum spinosum and stratum basale [In skin, the expression patterns of CD44 are independent from gender, age or ethnicity of donors, as well as from the anatomical origin of the sample . Howeverm basale ,272. An m basale ,274, botm basale  and is am basale .Although, complete CD44 deficiency in mice does not significantly alter the speed of macroscopic healing of punch biopsy wounds, such mice display alterations of the regeneration of the dermal collagen matrix . During stratum corneum [Δker mice). They observed delayed wound healing, a compromised proliferation and differentiation of keratinocytes as well as a decreased keratinocyte adhesion to and migration on HA coated surfaces [Δker mice revealed a reduction in epidermal stiffness, whereas dermal stiffness remained unaffected when compared to wild-type mice [CD44 knockout mice have been reported to have thinned epidermis with altered differentiation, diminished apical localization of lamellar bodies as well as a delayed recovery of the skin barrier function upon acute perturbation of the  corneum ,277, whi corneum  employedsurfaces . Moreoveype mice . The autype mice .Following the influence of CD44 on dermal healing and ECM deposition, CD44 would also be expected to have effects on fibrosis. Indeed, there is a large volume of studies describing the role of CD44 and its ligand HA in TGFβ-mediated pro-fibrotic signaling; however, findings are contradictory indicating contextuality of CD44 in regulation of fibrogenic processes . While sGrowth factor receptors are transmembrane receptors employing protein kinase activity to activate intracellular signaling cascades and thereby modulate, inter alia, cell proliferation, differentiation, metabolism or migration. Prominent players in skin development, homeostasis as well as inflammatory and fibrotic skin malignancies are the transforming growth factor β receptors (TGFβR) and the epidermal growth factor receptor (EGFR). Therefore, and due to their interaction with various ECM components, they are highlighted in this review.In mammals, there are three transforming growth factor β (TGFβ) isoforms  ; howeverHere, with a skin-centric focus, we concentrate on the receptors themselves rather than their ligands. We will refer to them as type I (TGFβRI) or type II receptors (TGFβRII).The TGFβRs consist N-terminally of small extracellular cysteine-rich domains for ligand binding followed by the transmembrane region and the cytoplasmic kinase domain . TGFβRI Apart from utilizing the canonical Smad signaling pathway, TGFβ is able to regulate signaling and gene transcription via other pathways, such as Ras-ERK-MAPK ,299,300,strata basale, spinosum and granulosum of the epidermis, but can also be found in the dermis, though to a lesser extent [In normal human skin, TGFβRI and TGFβRII are expressed in the r extent ,319,320.r extent ,322. TGFr extent ,324,325.A broad body of literature on the effects of TGFβ on fibrosis and wound healing exists ,333,334 Martins et al.  found TGCammareri et al.  reportedThe epidermal growth factor (EGF) receptor (EGFR) is a transmembrane protein that belongs to the EGFR family of receptor tyrosine kinases along with that also includes ErbB2, ErbB3, and ErbB4 . In skinEGFR consists of an extracellular ligand-binding domain, a transmembrane helix followed intracellularly by a kinase domain and tyrosine-rich C-terminal phosphorylation sites ,351. TheCell–ECM interactions are crucial to maintain cellular metabolic activity by modulating signaling pathways and thereby regulating gene expression or growth factor availability. Detachment of epithelial cells from an ECM causes, inter alia, downregulation of EGFR and integrin β1, both on protein and mRNA level . HoweverApart from the classical ligands, many ECM proteins influence EGFR signaling and interact with the receptor directly or indirectly. These are decorin, which binds with high affinity to the EGFR , causingThe differentiation and proliferation of epithelial cells is dependent on EGFR signaling. Various studies emphasize the role of EGFR in epidermal development, proliferation and differentiation ,376,377,EGFR relies on the interplay with other receptors for its functions in skin. This is exemplified by interactions with epidermal integrins. It has been shown that a fraction of EGFR directly associates with the hemidesmosomal integrin α6β4 in keratinocytes ,362 Figb. ActivaThe mechanical properties of tissues are emerging as essential regulators of EGFR activity. Kenny et al.  investigFrom a pathological perspective, it is known that even moderate stiffening of the ECM sensitizes epithelial cells to EGF, allowing them to proliferate independently of their contact to neighboring cells—a mechanism which is a hallmark of cancer cells . AccordiIntensive studies on the receptor-mediated interplay of cells with their surrounding ECM have been carried out during the last few decades, and they have revealed the presence of complex and highly intertwined communication networks that go far beyond simply anchoring cells and matrices together. In fact, these complex systems impact diverse cellular processes, and vice versa, modifications in single cellular receptors may alter the integrity or composition of the entire ECM, promoting systemic diseases. Examples highlighted in this review are mutations of single integrin subunits that result in junctional epidermolysis bullosa, mutations in EGFR that cause skin fragility, or mutations in TGFβRs that impact cutaneous SCCs development. These effects are, in part, also mediated by the tight interplay of cell-surface receptors in skin, such as the direct or indirect interaction of EGFR with syndecans and integrins, which impact each other in a bidirectional manner.The research work reviewed in this article highlights that malignancies, even though they symptomatically or clinically appear similar, may actually hold distinct underlying mechanisms and should therefore be approached in different ways.However, an exciting and promising feature of the reviewed diseases is that—despite disease-specific triggers—shared characteristics can be observed not only symptomatically, but also on a biochemical level. These common hallmarks, in turn, may be targeted for therapeutic purposes. A more comprehensive understanding of the bidirectional cell–matrix crosstalks would foster a move from descriptive studies towards the identification and validation of specific therapeutic targets that are common in several conditions. This may pave the way for the development of single therapeutic agents applicable to a broad range of conditions.


We aimed to examine psychosocial function, suicidality and school dropout in a clinical psychiatric population over a 3-year period from adolescence to young adulthood and explore associations with negative life events.1), 717 (43.5% of eligible) participated, aged 13–18 years (2009–2011), and 3 years later (T2), 570 answered a questionnaire (school functioning and negative life events), and 549 completed Kiddie SADS as telephone interview assessing DSM-IV diagnoses, psychosocial functioning and suicidality.This study is part of the Health Survey in Department of Children and Youth, St. Olavs hospital, Norway. In the first study visit (Tp < 0.001), as was suicidal behavior , p < 0.001). Girls had lower psychosocial functioning than boys , p < 0.001), and more school dropout , p = 0.006). For those with a psychiatric disorder, 24.8% of girls had suicidal ideation and 30.0% suicidal behavior, which was larger than for boys , p < 0.001, and RD = 18.3%, CI (10.2 to 25.8), p < 0.001, respectively). Exposure to negative life events was frequent for both genders, but more girls had experienced sexually uncomfortable or abusive situations, the last 3 years , p < 0.001), and ever , p < 0.001). Suicidal behavior was associated with having been threatened, physically harassed or violently hurt , p < 0.001), and for girls been put into sexually uncomfortable or abusive situations , p < 0.001) and seen others violently hurt , p = 0.011).Suicidal ideation was more frequent among girls (17.9%) than among boys (5.4%) , The high frequency of suicidality and school dropout confirms the severity of adolescent psychiatric disorders, especially among girls. Specific life events were associated risk factors and should be target points for prevention and intervention.The online version contains supplementary material available at 10.1186/s12888-021-03100-w. Adolescence is the period for transitioning into young adulthood and is usually a time of life characterized by good physical health . HoweverIt is common to have experienced negative or stressful life events or adversities from childhood to young adulthood , 19. ManPsychiatric disorders and comorbidities in early years influence academic functioning, and may subsequently lead to increased risk of dropping out of school  and receThe objective of the present study was to examine suicidality and functioning 3 years after referral to Child and Adolescent Mental Health Services. We aimed to assess psychosocial function, suicidal ideation, suicidal behavior, and school dropout, in the total sample and specified by psychiatric disorders, and furthermore to investigate associations with negative life events. We set out to specify analyses for girls and boys and explore gender differences. Hypotheses were that present suicidal symptoms and school dropout were associated with co-occurring exposure to negative life events, and furthermore, that frequencies differed between girls and boys, with girls having higher rates of suicidal symptoms and boys more school dropout.1) (2009–2011), all patients aged 13–18 years who visited the Department of Children and Youth at least once over a 2-year study period, received oral and written invitations at their first attendance. The exclusion criteria were difficulties in answering the survey due to low cognitive function, visual impairments, insufficient language skills, or an unstable psychiatric state. Emergency patients were invited to take part once they entered a stable phase. The participants and their parents received standard application of services. They gave written informed consent to extract diagnostic data from clinical charts and respond to an electronic survey. At 3-year study follow-up (T2) (2012–2014), age 16–21 years, data were collected from the T1 enrolled sample and their parents, by an electronic survey and a diagnostic telephone interview performed by trained professionals.The Health Survey in Department of Children and Youth, Division of Mental Health Care, St. Olavs hospital, Trondheim University Hospital, Norway (St. Olav CAP Survey), is a prospective longitudinal cohort study of a defined clinical population assessed at two time points. Design and procedures are thoroughly described in former publications , 46. At 1 study period, 2032 adolescent patients had at least one attendance in the Department of Children and Youth [1, n = 717 participated (393 (54.8%) girls). At T2, all T1 participants who previously consented to further inquiry were invited (eligible n = 685), of whom 570 (83% of eligible) completed the follow-up questionnaire (324 (56.8%) girls), and 549 (80%) completed the diagnostic interview (308 (56.1%) girls).In the Tnd Youth , 46. Fig1, anonymous information about the total clinical population was collected from annual reports from the Department of Children and Youth, 2009–2011, as previously published [n = 2032) minus those excluded (n = 289) were defined as reference population (n = 1743). The main reason for referral, age and gender were similar between participants  and non-participants  (data not shown). Participants were 0.27 years older: Mean (SD) 15.7 (1.7) vs. 15.4 (2.0), and there were more girls among the participants: 393 (54.8%) vs. 509 (49.6%). Among those with participation at T1, there were 570 participants and 147 non-participants at T2. In depth attrition analyses are reported in a former publication [To explore the representativeness of the study population at Tublished , 46. AllPsychiatric Diagnoses at T2 were set using the semi-structured Schedule for Affective Disorders and Schizophrenia for School-Age Children (K-SADS) [1 diagnoses. Inter-rater reliability in terms of negative agreement and positive agreement as recommended by van de Vet et al. [, was assessed using second ratings for 28 of the taped telephone interviews. Positive agreement varied from 0.615 to 1.000, and negative agreement varied from 0.884 to 1.000 [(K-SADS)  accordin(K-SADS) . Psychom(K-SADS) , and the(K-SADS) , 50. Adot et al. , was assto 1.000 . The undto 1.000 .2; Any psychiatric disorder, Anxiety disorders , Mood disorders , ADHD (DSM-code 314) and Other . Due to few participants in some diagnostic groups, for example autism and eating disorders, and especially when examining suicidality and school dropout, we chose to merge children with these diagnoses into “other psychiatric disorders” for the purpose of this manuscript.In the present study, disorders were grouped into the following categories, based on DSM-IV diagnoses at TThe Children’s Global Assessment Scale (CGAS) [e (CGAS)  was usedSuicidal ideation or behavior were measured at T2 by asking the following questions during K-SADS interview:Suicidal ideation; “Sometimes children who get upset or feel bad think about dying or even killing themselves. Have you ever had such thoughts? How would you do it? Did you have a plan?” Assessed and scored as; 0; No information, 1; Not at all, 2; Infrequent or vague thoughts of suicide , or 3; Recurrent thoughts of suicide. As measure of Suicidal ideation, we used “infrequent or vague thoughts” (2) or “recurrent thoughts of suicide” (3), presently at T2.Suicidal acts or attempts; “Have you actually tried to kill yourself? When? What did you do? Any other things? Did you really want to die? How close did you come to doing it? Was anybody in the room? In the apartment? Did you tell them in advance? How were you found? Did you really want to die? Did you ask for any help after you did it?” Assessed and scored as; 0; No information, 1; No attempt, 2; Preparations with no actual intent to die  or planned attempt but did not follow through, 3; Self injurious behavior with any suicidal intent. There was one more assessment; “Ever attempted suicide”, scored as yes or no. In the present study, Suicidal behavior included “preparations or planned attempt” (2) or “self injurious behavior with any suicidal intent” (3), presently at T2, or yes to the question: “Ever attempted suicide”.School dropout was self-reported at T2 based on answer “yes” to the following question: “Have you canceled your education (dropped out)?”Negative life events were registered by self-report at both T1 and T2. At T1, the following questions were asked: “Have any of the following things happened to you?”; “That someone in your family has been seriously ill”, “Death of a loved one”, “A catastrophe ”, “A serious accident (ex: a very serious car accident)”, “Been violently hurt (beaten or injured)”, “Seen others violently hurt”, “Been put in sexually uncomfortable/abusive situations by someone about your age”, “Been put in sexually uncomfortable/abusive situations by an adult”, “Been threatened or physically harassed by other students at school for a long time”, “Received painful or frightening treatment at the hospital while being treated for an illness or injury”. These items were also used in the Young-HUNT3 study (https://www.ntnu.edu/hunt/data/que).2, the same questions were asked, and with a supplementary question: “Been seriously ill or injured”.At T1; “No”, “Yes, last year” and “Yes, in my life”, and at T2; “No”, “Yes, last year” and “Yes, last three years”. In the present study, negative life events defined as “last 3 years” were events measured at T2 only, and negative life events defined as “ever” were measured at T1 or T2.The answering opportunities were at TSocioeconomic Status (SES) was measured at T1 by the highest level of mothers’ education, divided into eight categories: 1) less than 9-year primary school; 2) completed 9-year primary school; 3) one or two years in high school; 4) completed high school; 5) completed high school and one-year education/training after high school; 6) academy/university for up to and including 4 years; 7) academy/university for 5 years or more; 8) academy/university including PhD.2 as dependent variables and negative life events reported at T1 and T2 as covariates, one at a time, to study their associations. The coefficients in binary linear regression represent risk differences. These regression analyses were carried out unadjusted and adjusted for SES as a possible confounder where relevant. Some estimates including suicidal behavior could not be computed when adjusting for SES due to non-convergence of the calculations. We report 95% confidence intervals (CI) where relevant, and two-sided p-values < 0.05 were considered statistically significant. Binary linear regression and the Newcombe CI were performed in Stata 16, and the other in SPSS 25.We compared proportions using the Newcombe hybrid score confidence intervals, as recommended by Fagerland, Lydersen and Laake , and the2, psychosocial functioning CGAS score was mean 71.3 (standard deviation 14.5) (Table p < 0.001). The frequency of suicidal ideation was 12.4%, girls 17.9% and boys 5.4% , p < 0.001) (Table p < 0.001). School dropout was more frequent for girls than boys , p = 0.006). Among those with a psychiatric disorder, suicidal ideation was higher among girls (24.8%), and suicidal behavior even higher (30.0%), RD for gender differences 18.0%, CI (10.8 to 24.7), p < 0.001, and 18.3%, CI (10.2 to 25.8), p < 0.001, respectively (Table p = 0.043).At Tp = 0.002) , p < 0.001, respectively). Ever been exposed to a serious accident or catastrophe, were more frequent among girls , p = 0.001). There were highly significant differences between girls and boys in having been put into sexually uncomfortable or abusive situations, both during the last 3 years and ever , p < 0.001, and 44.4% vs. 7.9%, RD = 36.5%, CI (29.9 to 42.7), p < 0.001, respectively).Having serious illness of someone in family or death of a loved one, were the most common negative life events in this study (57.7% last 3 years and 85.7% ever), with higher frequencies among girls than boys only for the last 3 years , p = 0.012) and having been put into sexually uncomfortable or abusive situations , p = 0.018). Gender-specific analyses adjusted for SES, showed associations with having serious illness of someone in the family or death of a loved one, and being threatened, physically harassed or violently hurt for girls, but no associations were present for boys.Binary linear regression with suicidal ideation as dependent variable and negative life events as covariates showed associations for several life events Table . After ap = 0.008), exposure to a serious accident or catastrophe , p = 0.015), and been threatened, physically harassed or violently hurt , p < 0.001) (Table p = 0.011). Some estimates could not be adjusted for SES due to non-convergence of the calculations. Thus, the association with having been put into sexually uncomfortable or abusive situations , p < 0.001) could not be adjusted for SES, neither could the corresponding association that was present only for girls , p < 0.001). Having been threatened, physically harassed or violently hurt was related to suicidal behavior for both girls , p = 0.001) and unadjusted for boys , p = 0.004). There was an association between SES and suicidal behavior , p = 0.005). Specified by diagnostic groups, associations with suicidal behavior were highly significant for Mood disorders and Anxiety disorders (p < 0.001) and the group Other disorders (p = 0.007), adjusted for SES (data not shown).With suicidal behavior as dependent variable, adjusted associations were present for been seriously ill or injured , p = 0.007), and only among girls , p = 0.041) (Table n = 6) (data not shown). An association found between suicidal behavior and school dropout was attenuated after adjustment for SES in the total sample , p = 0.209).There were associations between school dropout and having seen others been violently hurt or been put in sexually uncomfortable/abusive situations, but after adjusting for SES, the associations only persisted for having seen others been violently hurt , 1) Table . ResultsThis study is one of few surveys following a general clinical psychiatric population of adolescents who received standard clinical care, studying symptoms and function over time. Reassessed after 3 years, suicidal ideation and suicidal behavior were frequent, especially among girls, and across all subgroups of psychiatric disorders. Girls had lower psychosocial functioning than boys, and more school dropout. Associations were found between negative life events and suicidal ideation and behavior. Most marked were the associations between suicidal behavior and having been exposed to interpersonal violence for both girls and boys. For girls only, sexually uncomfortable or abusive situations were also related to suicidal ideation and suicidal behavior, as was having seen others been violently hurt. Furthermore, having serious illness of someone in family or death of a loved one were also associated with suicidal ideation for the total sample, and SES was linked to suicidal behavior for both girls and boys. School dropout was associated with having seen others be violently hurt among girls with ADHD.In our sample assessed 3 years after referral for psychiatric disorders, the prevalence of suicidal ideation and behavior were similar to earlier research on clinical samples , 17. ThePsychosocial function as measured by CGAS with values in the sub-normal range, indicated better functioning than expected in a clinical sample with frequent comorbidity. The inter-rater reliability was tested and shown to be good. CGAS was lower among girls than among boys, corresponding with earlier findings by Gårdvik et al. , showingThe frequencies of experiencing negative life events are in accordance with earlier research reporting associations between psychiatric disorders and earlier negative life events or childhood adversities –56. The We examined possible associations between suicidal ideation at follow-up and negative life events. There was a significant association between suicidal ideation and having been threatened, physically harassed or violently hurt in the total sample, but in gender-specific analyses, the association was present only for girls. Suicidal ideation was also associated with exposure to sexually uncomfortable or abusive situations for the total sample, corresponding with earlier research described in the meta-analysis of 50 years of research by Franklin et al. . FurtherEarlier studies have demonstrated associations between suicidal behavior in adolescents and experiences of negative life events, as described in the systematic review by Serafini et al. . In our In our clinical sample, an association was found between suicidal behavior and SES, for both girls and boys. The risk of having suicidal behavior decreased for the total sample with 2.3% per unit increase in level of mothers’ education. This indicates that SES does have an effect on the presence of suicidal behavior at follow-up, and that higher maternal education may be a protective factor for development of these symptoms. A large national register-based study showed strong associations between SES and suicidal risk .p-values due to lower number of participants with SES information, or that the attrition was not random, i.e. that those with SES information were not representative for the entire sample.School dropout is related to many different risk factors. A meta-analytic review by Gubbels et al.  describe1 to T2. Furthermore, suicidal ideation and behavior were assessed in-depth by clinicians during the diagnostic interview, and not based on self-report measures which involves the limitations of less accuracy in establishing psychopathology. Some limitations need to be considered. The attrition rate was high in the initial recruitment, and even though the T1 sample did not differ in age, gender or reason for referral compared to non-participants, we cannot exclude that this high attrition rate may have affected the results. Since there were more girls than boys among participants compared to non-participants in this study, we may have lost some of the boys with psychiatric disorders, suicidality symptoms and impaired function. Life events and school dropout were measured by self-report only. School dropout was reported by one question, and additional information would have strengthened the measure, either by using several questions or information from other sources, supplemented by asking about the subjective reasons for the respective school dropout. Family characteristics including unemployment and low socioeconomic status influence mental health in off-spring [One strength of the present study is the inclusion of a large clinical sample receiving standard clinical care, assessed after 3 years with a high response rate from Tf-spring , and mayThe results of this study bring an important message to clinical practice. Even though clinicians know about increasing symptoms of suicidal ideation and behavior during adolescence, the self-reported high rates of suicidal attempts in this patient group should be an extra reminder, also the high rates of school dropout, especially among girls. The burden of exposure to negative life events must also be acknowledged. Comprehensive assessment of mental health problems should of course include important risk factors, and asking adolescent patients about suicidal ideation and behavior, experiences of negative life events and school functioning seems to be important, especially for female psychiatric patients, in order to reveal any such risks and prevent further traumatic events.In this clinical sample reassessed after 3 years, one out of four adolescent girls with a persisting psychiatric disorder had suicidal ideations, and one out of three had a previous history of suicidal behavior. Girls had lower psychosocial functioning and higher rates of school dropout and experiences of negative life events than boys. Negative life events, especially exposure to interpersonal violence, were associated with suicidal ideation, suicidal behavior or school dropout. The high frequency of suicidal symptoms, school dropout and experiences of negative life events, indicates a high burden of challenges in functioning. The results reinforce the need to include these symptoms and associated factors in an extensive follow-up of psychiatric disorders in this age group.Additional file 1: Table S1 Design matrix for the re-scoring of telephone interviews. Table S2 Suicidal ideation T2 and Negative life events. Table S3 Suicidal behavior T2 and Negative life events. Table S4 School dropout T2 and Negative life events


Pseudomonas aeruginosa (a tit-for-tat species) with Vibrio cholerae (random-firing), revealing that P. aeruginosa does indeed fire multiple times per incoming attack. Our work suggests bacterial competition has led to a particular form of reciprocation, where the principle is that of strong retaliation, or ‘tits-for-tat’.Tit-for-tat is a familiar principle from animal behavior: individuals respond in kind to being helped or harmed by others. Remarkably some bacteria appear to display tit-for-tat behavior, but how this evolved is not understood. Here we combine evolutionary game theory with agent-based modelling of bacterial tit-for-tat, whereby cells stab rivals with poisoned needles (the type VI secretion system) after being stabbed themselves. Our modelling shows tit-for-tat retaliation is a surprisingly poor evolutionary strategy, because tit-for-tat cells lack the first-strike advantage of preemptive attackers. However, if cells retaliate strongly and fire back multiple times, we find that reciprocation is highly effective. We test our predictions by competing  Game theory has contributed much to the understanding of social evolution. In an elegant combination of experimental tests and modelling, this study suggests that when bacteria face intense competition, repeated retaliation outcompetes a single tit-for-tat response to attack. Structurally and functionally homologous to a phage’s tail8, the T6SS consists of a membrane-bound baseplate complex, an effector-tipped needle, and a surrounding sheath whose contraction drives the needle through the membranes of target cells10. Used by many notorious plant and animal pathogens, the T6SS is a potent anti-competitor weapon: the T6SS can determine whether a strain can invade, or defend, its niche in both environmental and host-associated microbial communities16.The type VI secretion system (T6SS) is a contact-dependent bacterial weapon that is found in numerous bacterial species19 and against both prokaryotic and eukaryotic targets22. The specific pattern of firing by cells also varies: whereas placement of T6SS assembly appears to be random in some species, such as Vibrio cholerae, Serratia marcescens, and Acinetobacter baylyi25, other bacteria are known to fire from specific locations on their cell membranes. Perhaps, the most striking example of this spatiotemporal control is the retaliatory firing strategy observed in Pseudomonas aeruginosa, whose T6SS apparatus (encoded at the HSI-1 locus) is specifically activated in cells that are themselves attacked by T6SS needles27.There is remarkable variation in the regulation and use of T6SS weaponry across species. Bacteria activate and deploy the T6SS across a range of environmental contextsP. aeruginosa is an active topic of study. So far, it has been shown that various stimuli can trigger counterattacks in P. aeruginosa strain PAO1, including incoming T6SS attacks from multiple bacterial species28, conjugative T4SS pili29, and membrane-disrupting antibiotics like polymyxin B29. There is also evidence that retaliation can be toxin-specific—not all T6SS effectors trigger counterattacks, and in V. cholerae, only the lipase effector TseL triggers retaliation30. Across stimuli, P. aeruginosa appears to be responding to membrane perturbation, and a putative model is that this response is mediated post-transcriptionally via the TagQRST pathway. This signaling cascade leads to the localized phosphorylation of cytoplasmic Fha1 proteins, and subsequent T6SS activation33.The regulatory pathway underpinning T6SS retaliation in 34, its evolution has not—leaving open the question of why such a complex strategy has evolved in bacteria, and only in some species. At a broader level, while the evolution of reciprocation has a long history of study in evolutionary biology38, past efforts have focused largely on the evolution of reciprocal cooperation, rather than competition. Understanding the evolution of T6SS regulation and retaliation is therefore important, both for understanding bacterial warfare and as a distinct case in evolutionary biology.Although the molecular regulation of retaliatory T6SS firing has received attentionP. aeruginosa, we show that it does indeed fire multiple times in response to an incoming attack. Our work suggests that T6SS reciprocation is most beneficial during combat when performed in an aggressive manner.To address this, we used an agent-based modeling framework to simulate competition between different T6SS strategists. By combining modeling with game theory, we explore the evolution of T6SS regulation, including tit-for-tat (TFT) firing, across a wide range of conditions. This reveals that TFT has significant limitations as a strategy for T6SS warfare. We found that it rarely wins in direct competition because it fails to fire against unarmed strains and always fires second against armed strains. However, we also found that a strong retaliator, which fires multiple times in response to an attack, is a powerful competitor against randomly firing T6SS attackers. Finally, by studying the retaliatory firing patterns of 42. The heart of this model is a realistic representation of physically interacting bacteria growing in dense communities. Agent-based models of this sort have proved to be a powerful means to explore cell–cell interactions in bacterial communities, generating a wide range of predictions that have been verified by empirical work (reviewed in ref. 43).To study the interactions and evolution of different T6SS firing strategies, we began with an established agent-based modeling framework (CellModeller)44, designed specifically for the study of T6SS competition, in which cells can intoxicate neighbors by firing T6SS needles. Here, we extend this model, such that different modes of T6SS firing . Further details of our model are provided in the “Methods” section.We recently reported a new version of this modelkgrow), and simulations end once a patch becomes depleted of resources  and T6SS-susceptible Unarmed cells (U). We simulated community growth within 2-D patch environments, beginning with a randomly scattered, 1:1 mixture of R and U cells. Each patch simulation begins with a finite, uniform resource quota that is consumed as cells grow  under a broad set of conditions. As well as being favored by high cell density Fig. , which p47. As detailed in the “Methods” section, this approach considers the fate of an initially rare, novel strategist placed in a metapopulation (large set of patches) dominated by another, resident strategist. If the relative fitness of the novel strategist is greater than that of the resident, we assume that its frequency in the metapopulation will increase until it supplants the resident as the common strategy. We then also check whether the resident can itself re-invade a population of the novel strategist from rarity, and when it cannot, we assume that the novel strategy will permanently replace the resident.Our models confirm the intuition that the T6SS can help a bacterial strain to increase its frequency within a patch. But are Random T6SS attackers also expected to invade an Unarmed population when one also considers the competition between different patches to colonize new sites ? This question is important because while aggression may allow a strain to defeat a competitor, if this comes at a large personal cost, an aggressive strain may still end up producing very few dispersing cells. If other patches contain only passive strategies that make many dispersing cells, therefore, an aggressive strategy could win locally but lose globally by failing to colonize new patches. To address this question, we embedded our model in a game-theoretic framework that uses the principles of adaptive dynamicsIinv) as a function of its attack rate, kfire,R, for when there is purely local competition  and when there is both local and global competition . For local competition, Random attackers compete only within patches with the resident Unarmed strain. For global competition, they must also compete with Unarmed cells in neighboring patches where Random attackers are absent. In both scenarios, we find that R can successfully invade U for all non-zero firing rates, assuming a high initial cell density  competition. We find that either competitor can invade the other by firing faster than it  as the value of kfire,R, denoted What firing rate kfire,R1 = 0, Fig. kfire,R2 = δ, where δ > 0 represents some small increment in firing rate. Since the local invasion index kfire,R1 = δ becomes the resident strategy. The same outcome occurs with kfire,R2 = 2δ, 3δ … such that successive invasions by incrementally more aggressive mutants increase the firing rate in the resident population  will be displaced by mutants with lower firing rates  TFT does not fire its T6SS continuously, but counterattacks once per incoming attack ; (ii) TFT does not fire from randomly chosen sites on its cell membrane, but instead from the points where incoming attacks struck . To provide a fair basis for strategy comparison, we assume TFT to be identical to R in all other respects .Our results indicate that Random T6SS firing can often be a successful strategy, both enabling invasion of Unarmed populations and achieving higher cell frequencies against other Random T6SS attackers than Unarmed strategists attain. From this baseline, we can evaluate the evolutionary costs and benefits of the more complex T6SS firing strategy of TFT. Based on published empirical work on kfire,R that makes it equal or better than TFT —is highly successful against a Random T6SS attacker Fig. , outcompkfire,R > 0 , such that a single T6SS hit is sufficient to kill any non-clonemate cell  strains, just like TFT. This raises the possibility of rock-paper-scissors dynamics, also suggested in a recent studyWe also tested the robustness of 2TFT’s supremacy across a range of additional biological scenarios, including low diversity in T6SS toxins in the population . We identified two key advantages offered by retaliatory T6SS firing, and used our models to compare their relative contributions to 2TFT’s fitness in competition with R Fig. . First, ime Fig. . We confwhen one is being attacked prevents costly use of the T6SS when it is not needed. Examination of cell growth rates during R vs. 2TFT competitions confirmed that only 2TFT cells that are in contact with competitors pay for T6SS firing—compared with R cells, which pay for constant T6SS firing whether or not competitors are actually in range  intrinsic resistance to T6SS attack such that a cell can survive more than one hit  cells, in response to random attacks by V. cholerae (strain 2740-80) bacteria, as in the original T6SS retaliation study27 and subsequent work56. In our experiments, both cell types express functional T6SS apparatus, the sheaths of which (TssB subunits in the case of P. aeruginosa and VipA subunits in the case of V. cholerae) carry fluorescent tags (see “Methods”). These tags allow individual T6SS firing events to be tracked using time-lapse fluorescence microscopy, as described in previous studies57. When the two are grown together on agarose pads, V. cholerae antagonizes P. aeruginosa and causes it to launch counterattacks  weapon costs are not excessive and (ii) initial mixing provides enough inter-strain contact. By contrast, retaliatory firing is successful only against other T6SS users, and then only if the retaliator is robust to incoming attack, gains an aiming advantage, and deals more damage than it sustains . Ultimately, these additional constraints stem from the first-strike advantage possessed by random attackers: having already been struck by at least one T6SS needle, a retaliator always enters combat at a disadvantage, requiring that retaliation be disproportionate to be generally successful.P. aeruginosa is, to our knowledge, the only example of a T6SS retaliator found so far—whereas many species appear to use random T6SS firing25. It is also clear that P. aeruginosa is very well suited for T6SS retaliation. First, P. aeruginosa can resist oncoming T6SS attacks from other species like V. cholerae27. Second, P. aeruginosa’s ability to aim T6SS firing—through spatially resolved, TagQRST-mediated attack sensing—is likely to be a key contributor to its success as a retalitor  of growth-limiting resources, which cells consume until the patch becomes depleted, thereby ending the simulation. Cell phenotypes, model variables, and model parameters are summarized in Supplementary Tables As in previous studiesCell growth and division: Each cell’s volume Vi increases exponentially through elongation, from initial volume V0, according to the equation dVi/dt = kigrow,Vi, where kigrow, is a (phenotype-dependent) cell growth rate with maximum kmax. For simplicity, we assume that all living cells deplete patch resources E at a rate proportional to their current volume, and independent of the resources remaining in the patch : dE/dt  = −kmax,∑iVi. Cells divide lengthwise into two identical daughter cells once they reach volume 2V0 + ηdivision, with ηdivision a uniform random noise term. Each daughter’s axis vector ηorientations, to represent spatial imperfections in the division process. Following the cell growth phase, the cell configuration is returned to a quasi-stationary mechanical equilibrium using an energy minimization algorithm, described previously39. Briefly, cells whose surfaces overlap are identified using a standard contact finding algorithm59. Then, sets of impulse vectors p satisfying the equation A is a matrix summarizing cell contact geometry, M is a matrix of drag coefficients, d is a vector of contact distances, and α is a matrix regularization weighting coefficient. The application of these impulses returns cells to mechanical equilibrium while also minimizing cell displacement.T6SS firing and costs: T6SS+ cells can fire toxin-laden needles of length Lneedle outwards from points on their surface. Every timestep dt, a focal T6SS+ cell i may fire Nifirings, ≥ 0 times. The number and spatial orientations of firings depend on the phenotype of the focal cell  strategists fire back twice for every hit they sustain. To reflect the material and energetic costs of T6SS carriage and use, T6SS+ cells reduce their growth rate to cupfront represents the cost of T6SS gene carriage, while the latter term reflects the pro rata running costs of T6SS firing.T6SS hit detection: To determine whether a given firing event is successful, we run a two-step hit detection algorithm to determine (i) whether that needle intersected any other cell in the population, and if so, (ii) where on the target cell the needle struck  involves computing the shortest distance dmin between the needle and cell line segments; R is the cell radius of the victim, and Lpeneration a small tolerance factor. Test (ii) involves checking whether a needle vector passes through the cylindrical midsection of the cell, or through spheres of radius R placed at its poles; whichever intercept lies closest to the needle’s origin is logged as the entry point . Cells respond to T6SS intoxication with a step-like dose response: once a cell’s cumulative translocation count reaches threshold Nhits, that cell begins to lyse. Nhits therefore parameterizes both the potency of a given T6SS effector and the capacity of a victim cell to withstand it. Lysing cells die—and are immediately removed from the simulation—after a delay of 1/klysis, where klysis is the victim cell lysis rate. Lysing cells do not grow or consume patch resources.40. Where possible, parameters governing T6SS firing and response were estimated directly from previous experimental observations of T6SS competition44. We used a lower bound of Nhits = 1 based on a previous study44. However, based on the observation that P. aeruginosa seems to withstand multiple T6SS hits before being killed27, we also considered larger values . Throughout the study, we used the parameter values c = 0.001, cupfront = 0.05; we chose these values because they made the optimal firing rate in R vs. U competitions roughly consistent with the firing rates observed in random-firing bacteria like V. cholerae and A. baylyi (~50–100 firings cell−1 h−1)44. Since it was not possible to glean cost parameter values directly from experiments, we instead performed broad parameter sweeps to test the effects of different costs.The parameters of our model  are summarized in Supplementary Table 60, we use the logic of game theory and adaptive dynamics47 to determine whether a focal strategy  could evolve from a given bacterial metapopulation, subject to different scales of competition. This method uses short-term competition outcomes to infer the evolutionary fate of a rare, novel strategy in a metapopulation where a different “resident” strategy predominates. If the novel strategist can reproduce faster than the resident strategist even when rare, its frequency in the metapopulation will increase, until eventually it supplants the resident. For example, to test whether an R strategist can invade a population of U strategists, we compare their effective fitnesses where one is rare, and the other common. For R to invade U, we requireWrel(X∣Y) is the relative fitness of X against Y. The first inequality specifies that R can invade U from rarity; the second checks that R is resistant to re-invasion by U once R becomes common. The definition of relative fitness Wrel depends upon the spatial scale of competition within the metapopulation. If competition is localized, then R competes primarily with nearby residents. Here, invasion is predicted simply from the ratio of strategists’ fitnesses within a spatial patch: R invades U provided thatωX defined as As in previous studieskfire shown in Fig. kfire,R1,kfire,R2, and so invasion outcome is summarized by the 2-D color map,We refer to these two sets of inequalities as local and global invasion constraints, respectively. To create the 1-D invasion plots shown in Fig. les Fig.  is5\docuωX, to be a weighted sum of its fitness against a competitor with (Y′) or without (Y) mutual immunity, ps = 0.To test the effects of allowing for mutual immunity between T6SS+ strains, we re-ran competition simulations as before, except with T6SS attacks having no effect on T6SS+ target cells. We then expanded our definition of a strategist’s fitness, kfire,R and of the weighting parameter ps. The updated invasion indices are as follows: for R vs. U, we haveWe then used this updated definition to recompute global invasion indices, now as a function both of firing rate We interpreted invasion indices as previously: for each strategist pairing, the inequalities c), representing a cheater strain emerging from a T6SS+ parent . Rc possesses the same T6SS genes as its parent , but never invests in any T6SS activity  the other two strategists in each case. Thus, the invasion behavior within each grouping is summarized by the values of six invasion indices.To examine the effects of cheater emergence in our pairwise competition system, we created a new strategist (RωR is a weighted sum of (i) fitness when growing alone , and (ii) fitness growing alongside a competitor Y. Note that being paired with self implies mutual immunity to T6SS attacks, since by assumption all clonemates share identical T6SS effector and immunity genes. Thus, we haveI is a weight parameter representing the probability of perfect strategist segregation, and where To incorporate the effects of increased within-patch relatedness, we created a second weighting scheme in which an R-strategist’s effective fitness I values of 0.01, 0.5, and 0.99, representing increasing levels of within-patch relatedness. These results are plotted in Supplementary Figs. for Y = U, TFT, or 2TFT. We used these indices to construct 1-D invasion plots , and visualized using the Paraview software (version 5.4.0)Pseudomonas aeruginosa PAO1 tssB-mNeonGreen, V. cholerae 2740-80 vipA-mCherry2, and V. cholerae 2740-80 vipA-mCherry2 Δhcp1 Δhcp2 were inoculated from Luria broth (LB) agar plates and grown aerobically at 37 °C in LB to an OD600 of 1 (~3 h). One milliliter of each day culture was then pelleted at 11,000 × g for 1.5 min and resuspended in LB to reach OD600 of 10. Pseudomonas aeruginosa PAO1 tssB-mNeonGreen was mixed with V. cholerae 2740-80 vipA-mCherry2 or V. cholerae 2740-80 vipA-mCherry2 Δhcp1 Δhcp2 in a 1:5 ratio (10–50 µl). Both mixtures (1.5 µl) were spotted on a pad of 1% agarose in 1/3 LB and 2/3 phosphate-buffered saline). The pad was covered with a glass coverslip and incubated for 30 min at 30 °C before imaging.63; a Nikon Ti-E inverted microscope with Perfect Focus System and a Plan Apo 1003 Oil Ph3 DM (NA 1.4) objective lens, a SPECTRA X light engine (Lumencore), and ET-GFP (Chroma #49002) and ET-mCherry (Chroma #49008) filter sets to excite and filter fluorescence. Exposure time was set to 150 ms and LED powers to 20%. Images were recorded with a sCMOS camera pco.edge 4.2  and VisiView software . Imaging was carried out at 30 °C and 95% humidity controlled by an Okolab T-unit (Okolab) and images were collected every 2 s for 5 min. The imaging experiments were performed in two biological replicates.For live-cell fluorescence microscopy, the same equipment was used as described previously64. Contrasts were set equally for a set of compared images. Intensity of GFP and mCherry channels was corrected with the simple ratio bleach correction function. Numbers of P. aeruginosa PAO1 tssB-mNeonGreen cells in contact with V. cholerae 2740-80 vipA-mCherry2 or V. cholerae 2740-80 vipA-mCherry2 Δhcp1 Δhcp2 cells were counted based on the phase contrast and GFP channel. The number of T6SS structures per cell in P. aeruginosa PAO1 tssB-mNeonGreen was counted in the maximum intensity projection image of the GFP channel. Only T6SS structures of cells in contact with V. cholerae were counted. To quantify the number of repeated T6SS assemblies in kymograms, the reslice function was used. Only repeated T6SS assemblies directed towards V. cholerae cells were analyzed. Kymograms of V. cholerae were used to calculate the time without new T6SS after contraction (2 s pixel−1). Only T6SS assemblies directed towards P. aeruginosa cells were included in the analysis. All quantifications were performed manually. GraphPad Prism7 was used to display the histogram of repeated T6SS assemblies. The number of cells analyzed, averages with standard deviations, and medians are given in the figure legend.Image analysis and manipulation was carried out with Fijit test, assuming data normality. No adjustments were made for multiple comparisons. To estimate effect sizes, we used Cohen’s d measure, μi and SDi are, respectively, data means and standard deviations for each sample i. All statistical calculations were performed in Matlab ® (version R2017a 9.2.0.556344).Unless indicated otherwise, the number of simulation replicates is five for each parameter combination shown. Two biological replicates were used in all experiments. For comparative statistics Fig. , we usedFurther information on research design is available in the Supplementary InformationReporting SummaryDescription of Additional Supplementary FilesSupplementary Movie 1Supplementary Movie 2Supplementary Movie 3Supplementary Movie 4Supplementary Movie 5Supplementary Movie 6Supplementary Movie 7Supplementary Movie 8


Ionizing radiation (IR) is a common mode of cancer therapy, where DNA damage is the major reason of cell death. Here, we use an assay based on fluorescence imaging of single damaged DNA molecules isolated from radiated lymphocytes, to quantify IR induced DNA damage. The assay uses a cocktail of DNA-repair enzymes that recognizes and excises DNA lesions and then a polymerase and a ligase incorporate fluorescent nucleotides at the damage sites, resulting in a fluorescent “spot” at each site. The individual fluorescent spots can then be counted along single stretched DNA molecules and the global level of DNA damage can be quantified. Our results demonstrate that inclusion of the human apurinic/apyrimidinic endonuclease 1 (APE1) in the enzyme cocktail increases the sensitivity of the assay for detection of IR induced damage significantly. This optimized assay also allowed detection of a cooperative increase in DNA damage when IR was combined with mild hyperthermia, which is sometimes used as an adjuvant in IR therapy. Finally, we discuss how the method may be used to identify patients that are sensitive to IR and other types of DNA damaging agents. Most cancer patients are treated with radiotherapy at some stage ,2. RadioRadiotherapy uses ionizing radiation (IR) generated by different sources, such as X- rays, γ- radiation, electron, proton and neutron beams . IR induHyperthermia is making its way into the clinic as an adjuvant to conventional cancer therapy  and is sMethods used to asses DNA damage include comet assay , DNA breBst DNA Polymerase, Endo VIII, UDG, T4 PDG and Taq DNA Ligase, to incorporate fluorescent nucleotides, in an in vitro repair reaction, at the single-strand lesions [Recently, an assay for quantifying single stranded DNA damage, based on single DNA molecule imaging, has been demonstrated ,35. Zirk lesions . The lab lesions . Using t lesions .coli and is a key player of the BER pathway involved in the repair of reactive oxygen species induced DNA damage [in vitro repair of 3′-phosphoglycolate lesions generated by γ-radiation [in vitro in the presence of Mg2+ [In vitro rejoining of nicked mismatched DNA is dependent on APE1 [One of the pathways to repair DNA damage induced by IR is base excision repair (BER) . Repair A damage . Exo IIIadiation . The 3′- of Mg2+ . In vitr on APE1 . APE1 an on APE1  and APE1 on APE1 ,45 have Here, the method developed by Zirkin et al. was adapted for detecting single-strand lesions induced by IR and hyperthermia . The stuBlood samples from apparently healthy individuals with normal differential blood count were collected from the Hematology unit at the Clinical Chemistry Department at Sahlgrenska University Hospital. PBMCs were isolated from blood samples through density centrifugation using Lymphoprep  according to the manufacturers' instructions. The study has been approved by the Regional Ethical Review Board (Dnr: 246-07 and Dnr: 308-08).6 cells/ml before treatment and 200 μl was used per ionizing radiation treatment. The cells were irradiated on ice in a 35 mm × 10 mm petridish without the lid, in an RS 2000 X-ray Irradiator (Rad Source Technologies) at the doses indicated in each figure. For repair kinetic studies, the samples were incubated in a water bath at 37 °C for the time intervals indicated in each figure after ionizing radiation treatment of 2 Gy.PBMCs were resuspended in RPMI-1640 (Sigma-Aldrich) at 20 × 10Immediately after ionizing radiation treatment, 100 μl of ice-cold cell lysis buffer  was added to stop any enzymatic processes. For hyperthermia treatments, the cells were incubated on the thermal block at temperatures ranging from 37 °C to 42 °C for the indicated periods of time. After hyperthermia treatment, the cells were either lysed or incubated at 37 °C for 30 min before lysis. For the combination of hyperthermia and IR, the samples were pre-treated at 42 °C for 30 min and then exposed to a 2 Gy dose of IR on ice. After hyperthermia-IR treatment the cells were either lysed or incubated at 37 °C for a further 5 or 30 min as indicated in the figure.DNA was extracted using the GenElute-Mammalian Genomic DNA Miniprep Kit (Sigma-Aldrich), according to the company protocol with some modifications. The samples were not vortexed and wide bore tips were used to pipette the samples in order to maintain the integrity of genomic DNA. The proteinase K treatment was done at 55 °C for 20 min. 10 mM Tris-Cl, pH 8.5 was used for elution.4, 50 μg/ml bovine serum albumin) for 2 h at 37 °C in a final volume of 100 μl. The enzymes were inactivated at 65 °C for 5 min, followed by 4 h incubation at 16 °C with dNTPs  and 0.1 μM Aminoallyl-dUTP-ATTO-647 N (Jena Bioscience), in nick translation buffer (0.05 M Tris HCl (pH 7.5), 0.1 mM dithiothreitol, 0.01 M MgSO4, 50 μg/ml bovine serum albumin), supplemented with DNA polymerase 1 (5 U), 1 mM ATP, and T4 DNA ligase (10 U). For control experiments, the repair enzyme cocktail was not added to the reaction buffer. The enzymes were purchased from New England Biolabs (USA). The reaction was terminated with 2.5 μl of 0.25 M EDTA. The samples were stored at 4 °C prior to analysis.Samples containing 500 ng of DNA were incubated with APE1 (5 U), FpG (1.6 U), Endo III (2 U), Endo IV (2 U) and Endo VIII (2 U) in the reaction buffer (0.05 M Tris HCl (pH 7.8), 0.1 mM dithiothreitol, 0.01 M MgSOThe functionalization of glass coverslips was adapted from Wei et al. . Briefly2 μl of the fluorescently labeled DNA was stained with 320 nM YOYO-1 (Invitrogen) and 2 μl of β-mercaptoethanol  in a total volume of 100 μl of 0.5 × TBE. Then, 3.8 μl of each sample was extended on a 22 × 22 mm coverslip by placing the solution at the interface of an activated coverslip and a clean microscopy slide (VWR Frosted). The extended DNA molecules were imaged with a fluorescence microscope (Zeiss Observer.Z1) using an Andor iXON Ultra EMCCD camera. The images were captured using 475/40 and 640/30 band-pass excitation filters and 530/50 and 690/50 bandpass emission filters, for YOYO-1 and Aminoallyl-dUTP-ATTO-647 N, respectively. EM gain setting of 100 and exposure times of 10 ms and 500 ms were used for YOYO-1 and Aminoallyl-dUTP-ATTO-647 N, respectively.The microscopy images were analyzed using a custom-made software . The totThe formula for calculating residual DNA damage:t-tests were performed using excel, p-values were determined and p < 0.05 was considered to be statistically significant. ** represents p < 0.01 and * represents p < 0.05 as measured by two tailed t-tests assuming equal variance.The data was collected from three healthy volunteers on three different days and the corresponding standard deviation is reported. Two tailed- The assay for detecting single-strand lesions formed by IR and hyperthermia is schematically outlined in Using our single-molecule DNA-damage assay we first quantified the damage for PBMCs treated with 2 Gy of IR using only DNA polymerase 1 and T4 DNA ligase and a ~32% increase in DNA damage was detected compared to control samples A. This i2O2 induced damage [The assay has previously been used to measure DNA repair kinetics for UV and Hd damage . To measHyperthermia is an emerging treatment used in combination with IR and/or chemotherapy to sensitize cells to the treatment ,50. We fIn this study we report the adaptation of a previously reported assay  for direPatients with deficiencies in DNA repair genes are known to be sensitive to DNA damaging agents, including IR . Furthervia both homologous recombination [We also combined IR and hyperthermia, a joint therapy that is making its way into the clinical setting . Severalbination  and non-bination . Additiobination ,62. Mostbination . When cobination .Using this assay to measure DNA damage has several advantages relative to previously reported methods. Firstly, the assay can be modified to measure different types of DNA damage and the enzymes can be chosen so that specific types of damage can be quantified, as has been shown in a recent paper by Torchinsky et al. . The comIf the assay is shown to also be able to detect defective DNA repair in patients, it has the potential to be used clinically for personalized radiation- and chemotherapy. To reach this goal, the assay needs to be validated using individuals with known DNA damage sensitivities, as well as resistant and sensitive cell lines. Further, the repair profiles of cancer patients' PBMCs using the assay needs to be correlated to therapy response. In this study, blood is used as a source of lymphocytes, which is relatively easily obtained and allows investigation of differences in the normal tissue response among individuals. This also enables studying patients undergoing chemotherapy to evaluate whether their cell response correlates with the effectiveness of the therapy. However, it should be taken into consideration that the DNA damage response in normal cells does not reflect that of tumour tissue, or other tissues from the individual.To conclude, the present work demonstrates the development of an enzymatic assay that determines the total number of single-strand lesions generated on DNA, tailored to sense IR and hyperthermia induced lesions. The enzyme APE1 was included in the enzyme cocktail to increase the number of detected lesions, in particular at high IR doses, where clustered DNA damage is common. The marked increase seen in the number of single-strand lesions due to hyperthermia and IR can be explained by both inhibition of DNA repair and formation of more single-strand lesions. The results from this study correlate with prior studies in the field of hyperthermia and IR-based therapy, indicating that the assay is robust. We propose that the assay could be used to identify patients with deficiencies in repair of IR-induced damage, which would be a way of personalizing IR treatment for patients, that in turn would minimize side effects for sensitive patients.Vandana Singh: Conceptualization, Methodology, Validation, Formal Analysis, Investigation, Writing - Original draft preparation. Funding Acquisition. Pegah Johansson: Conceptualization, Resources, Writing - Reviewing and Editing, Supervision. Dmitry Torchinsky: Software, Formal Analysis. Yii-Lih Lin: Software. Robin Öz: Methodology. Yuval Ebenstein: Conceptualization, Methodology, Software, Formal Analysis. Ola Hammarsten: Conceptualization, Resources, Writing - Reviewing and Editing, Supervision, Funding Acquisition. Fredrik Westerlund: Conceptualization, Methodology, Writing - Reviewing and Editing, Supervision, Project Administration, Funding Acquisition.


Currently most methods take manual strategies to annotate cell types after clustering the single-cell RNA sequencing (scRNA-seq) data. Such methods are labor-intensive and heavily rely on user expertise, which may lead to inconsistent results. We present SCSA, an automatic tool to annotate cell types from scRNA-seq data, based on a score annotation model combining differentially expressed genes (DEGs) and confidence levels of cell markers from both known and user-defined information. Evaluation on real scRNA-seq datasets from different sources with other methods shows that SCSA is able to assign the cells into the correct types at a fully automated mode with a desirable precision. Recent development of scRNA-seq methods has enabled unbiased, high-resolution transcriptomic analysis of individual cells in a heterogeneous cell population  and t-distributed stochastic neighbor embedding (t-SNE) have been developed to partition the cells based on the similarity of their gene expression patterns . For each cell cluster, a marker gene identification vector is generated for j genes with LFC values, which is defined as Ej = {e1, e2, ⋯ej} = (e)j×1, here, e represents the absolute value of LFC multiplied by mean of all.The input of SCSA is a DEGs clusters matrix, in a format that is supported by the clustering output of CellRanger or Seurat. Based on the matrix, SCSA identifies the marker genes of each cell cluster through a filtration with log2-based fold-change (LFC) value and In order to improve the accuracy of cell cluster annotation for scRNA-seq data, SCSA uses cell markers from two public databases: CellMarker and CancerSEA. SCSA integrated 11,464 manually curated cell markers of 459 cell types in 158 human tissues/sub-tissues and 7,855 cell markers of 385 cell types in 80 mouse tissues/sub-tissues from CellMarker database. Also SCSA integrates 1,244 markers from CancerSEA database, which consists of 14 functional states from 25 human cancer types. Furthermore, SCSA can accept user-defined marker gene database as additional information for cell cluster annotation. The user-defined marker gene database must have two columns, with the name of cell types in the first column and marker gene for each cell type in the second column. In that case, SCSA will combine both known databases and the custom database to predict the annotations for cell clusters.M1 = (aij)c1×g1) with c1(c1 ≤ i) cells and g1(g1 ≤ j) genes as “marker evidence.” Here, for each cell i and each gene j in the matrix M1, a refers to the sum number of references in the CellMarker database. To eliminate the huge differences of marker evidence between the well-known gene and less-known genes, we transform the value to log2-based and plus a constant (0.05). Also, to represent the whole gene set for a certain cell, we create a cell type style vector which takes multiplication of standard deviation of the marker evidence and marker numbers . So, for the known marker database, the raw score vector of a cell type is Sc1 = M1 × Eg1 * L1 = {s1, s2, ⋯sc1 }.For those genes which existed in both the DEGs and known cell marker databases, SCSA constructs a cell-gene sparse matrix p×q, p × q ∈ {(c1 × g1), (c2 × g2), ⋯ , (ck × gk)}. With the definition of multiple gene expression vectors defined as Ek = (e)q×1, q ∈ {g1, g2, ⋯ , gk}, (q ≤ j), k raw score vectors will be generated, which is defined as Sk =MkEkLk = (aij)p×q × (e)q×1 * (l)q×1 = (s)p×1, p ∈ {c1, c2, ⋯ck}. Suppose Sk to be a function to the score vectors, which is defined as Fl. To eliminate the difference of those vectors and compare them with each other standardly, SCSA performs z-score normalization for them. In detail,For marker databases from multiple sources including known and user-defined ones, with Notably, the score vectors derived from different kinds of databases may have different lengths. To give a uniform score to a certain cell type, SCSA transforms them to the same length:W and the last uniform score vector.Finally, an annotation model will be constructed by merging the database weight coefficient matrix P-values, using DEGs of the selected cluster as foreground values and DEGs in other clusters as background values, respectively. After that, P-value was adjusted by the Benjamini-Hochberg (BH) method . The dataset contained gene expression data of 916 human samples (660 primary cell samples and 256 cell lines) and 821 mouse samples  from FANTOM5 and 972 human samples and 1,188 mouse samples in SingleR's reference dataset. Annotation of these cells into certain categories were also downloaded from the same repository. For human cells in FANTOM5+SingleR dataset, terms of cell types associated with more than 10 samples were collected as the FANTOM5_hs1 dataset. And cell types associated with more than 40 samples were used FANTOM5_hs2 datasets. Similarly, FANTOM5_mm1 contained cell types with more than 10 samples and FANTOM5_mm2 included cell types with more than 40 samples in the FANTOM5+SingleR mouse dataset.FANTOM5+SingleR dataset was collected from GitHub repository of scMatch (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72056) and used to optimize the parameters for SCSA. Normal cells in the GSE81861 dataset was downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE81861). E-MTAB-6149 was downloaded from ArrayExpress repository (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6149).Four real datasets . The 3k and 6k data were blood samples from one donor generated using the v1. chemistry and preprocessed with CellRanger1.1.0. They were labeled “3k PBMCs from a healthy donor” and “6k PBMCs from a healthy donor,” respectively. The 4k and 8k datasets, under the label “4k PBMCs from a healthy donor” and “8k PBMCs from a healthy donor,” were samples collected from one donor, generated using the v2. Chemistry and preprocessed using CellRanger2.1.0.The peripheral blood mononuclear cells (PBMCs) datasets, including four scRNA-seq data of peripheral blood mononuclear cells  were downloaded from the 10X Genomics official website  and predicted the cell types according to the clusters.For the four PBMCs datasets, data preprocessing, normalization, and unsupervised clustering were already performed by CellRanger workflow from its website. Since 10X Genomics official website illustrated a workflow example using 3k PBMCs from a healthy donor containing five cell clusters and gave a final annotation results contained monocytes, T cells, NK cells, megakaryocytes, and B cells, respectively, we choose results with five clusters to do the further evaluation. SCSA identified the DEGs of each cluster through the LFC (LFC ≥1.5) value and To evaluate the stability of SCSA in annotating the cell type of a cluster, a heat map was generated using hierarchical clustering method for all cell types of top five scores in a cell cluster. To further demonstrate the accuracy of SCSA, we calculated the percentage of the five clusters cell types to measure the abundance of the same cell type based on the prediction of SCSA in the four PBMCs datasets, respectively.https://cole-trapnell-lab.github.io/garnett/classifiers) and used for classification. Because CellAssign and Garnett only annotate cell types for each of single cells instead of cell cluster, to make the evaluation at the same level, all cells of a certain cluster were predicted and the cell type with the maximum occurrence was chosen as the final predicted result. Three tags were used to compare the results. “Positive”meant correct prediction to the cell type. “Negative” meant incorrect prediction cell type. “Missed” meant no clear cell type prediction. Accuracy of cluster was calculated as sum of clusters with “Positive” tag divided by sum of all clusters.Three annotation tools, scMatch  datasets from 10X genomics website. We collected all possible cell types of a cell cluster according to the top five scores under the score annotation model of SCSA. The correlation of all cell types and scores were calculated and compared. As shown in Figure 3B, the percentages of cell numbers for five cell types annotated by SCSA remained stable across these datasets. T cells occupied half of the PBMCs, and monocytes cell represented another 25%, B cells and NK cells had similar levels, while megakaryocytes cell has the lowest number among all the five cell types. Meanwhile, as shown in Figure 3C, SCSA can predicted the five cell types consistent with the reference information of four PBMCs.To further demonstrate the robustness of SCSA over the five annotated cell types , we compared their abundance in each cluster using the four PBMCs datasets. As shown in In addition, to test the GO module in SCSA, which is designed to give some clues on cluster cell function in addition to its label, we collected gene expression profiles of primary cells and cell lines from FANTOM5+SingleR dataset, which contains both human and mouse cell types . For celWe compared the performance of SCSA with three other tools  of B cells were correctly assigned for true B cell cluster, whereas 2,861 (51.1%) cells were mistakenly assigned as myofibroblast cells and 1,755 (31.3%) cells were not able to assign a clear cell type. In general, most predicted cell types in the cluster (>50%) from these tools were not consistent with true cell type. For example, the T cells of E-MTAB-6149 dataset was not annotated by CellAssign, the fibroblasts of GSE81861 dataset was missed by scMatch, and the natural killer cells of GSE72056 dataset were not identified by CellAssign and scMatch , S4–S6. Currently, for scRNA-seq data, cell type annotation of cell clusters after unsupervised clustering is mainly conducted manually. The limitation of the manual procedure makes it impossible to generate high-quality, reproducible, and standardized annotation results for the growing number of scRNA-seq datasets.To address this challenge, we presented a novel tool, SCSA, for automatic annotating the cell types from single-cell RNA sequencing data, which can be applied directly on the output generated from CellRanger or Seurat. By introducing the newly developed annotation model merging DEGs and cell markers reference information to replace the manual steps, and based on the extensive evaluation of performance on seven real known datasets, as well as comparisons with alternative methods . We believe that SCSA is an important addition to the toolbox used for single-cell studies and will greatly improve our efficiency and capacity to explore the functional potential of novel cell types.Publicly available datasets were analyzed in this study. This data can be found here: GSE72056, GSE81861, E-MTAB-6149, FANTOM5, SingleR‘s reference datasets and PBMCs.GP proposed the method and conceived of the study. YC optimized algorithm and designed the program. YC and GP analyzed the data. YC, GP, and XW wrote the manuscript. All authors discussed the results, commented on the manuscript, read, and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.