PRECISE-based pathway signatures, can delineate pan-cancer commonalities and differences in proteomic network biology within and across tumors, demonstrates robust tumor stratification that is both biologically and clinically informative and superior prognostic power compared to existing approaches. Towards establishing the translational relevance of the functional proteome in research and clinical settings, we provide an online, publicly available, comprehensive database and visualization repository of our findings (https://mjha.shinyapps.io/PRECISE/).Personalized (patient-specific) approaches have recently emerged with a precision medicine paradigm that acknowledges the fact that molecular pathway structures and activity might be considerably different within and across tumors. The functional cancer genome and proteome provide rich sources of information to identify patient-specific variations in signaling pathways and activities within and across tumors; however, current analytic methods lack the ability to exploit the diverse and multi-layered architecture of these complex biological networks. We assessed pan-cancer pathway activities for >7700 patients across 32 tumor types from The Cancer Proteome Atlas by developing a personalized cancer-specific integrated network estimation (PRECISE) model. PRECISE is a general Bayesian framework for integrating existing interaction databases, data-driven  These include perturbations of signaling pathways that generate modifications of molecular activities affecting the downstream molecules along specific pathways. The coordinated triggers that regulate the cumulative change in the signaling pathways may result in different phenotypic changes, along with signaling alterations that can be delineated by a deeper understanding of the pathway networks and topologies4. These network structures vary widely within and between different tumors and are important factors in characterizing molecular tumor types and developing therapeutic strategies, especially in cancer6.Cancer is a complex disease initiated and characterized by myriad molecular and genomic events, which often occur within a modular and layered architecture of functional or cell signaling pathwaysglobal (across patients) and local (within patients). Global approaches typically generate population-level summaries (across patients) that rely on different distance metrics, such as functional and topological information of the networks, to measure associations between the target genes and known gene/protein sets of interest that represent various cellular processes. For example, methods based on gene set enrichment analysis9 use functional information that assesses the statistical overrepresentation of genes in a pre-selected list of interest from a reference list of known gene sets without consideration of their topological information in the graph structure. Analogously, several computational methods have been developed that incorporate the known topologic pathways to improve the prioritization of the gene set associations11. Without defining pathways, de novo pathway enrichment analysis14 has been proposed to identify functional modules/sub-networks enriched in biological entities active in a given experimental dataset by using large-scale interaction network.Most systematic endeavors for investigating pathways can be broadly classified into two categories based on the hypothesis, approach and outputs: 15. This type of approach acknowledges the fact that pathway structures and activity may differ considerably within and between patients as well as across tumor sites. For example, patient-specific pathway or meta-gene (MG) analysis has been proposed to infer the patient-specific activity of a gene/pathway/MG by incorporating known (pathway) interactions among genes and multiple types of molecular data18 as well as the functional impact of a mutation within gene/protein networks21. However, these methods only incorporate curated  interaction database and do not consider de novo data-driven causal networks. Moreover, these methods provide patient-specific scores of individual genes/MGs, as opposed to more interpretable cumulative pathway-level summaries that better reflect the complex pathophysiology of tumors.The local category of patient-specific approaches has recently emerged within a precision medicine paradigm. It is the treatment and prevention of disease by accounting for individual variability to match the right drugs or intervention to the right patientsa priori existing knowledge databases, data-driven de novo causal structures and diverse molecular profiling data at proteomic, transcriptomic (mRNA expression), epigenomic (DNA methylation) and microRNA expression levels. The novelty of our methodology lies in combining causal structure learning, Bayesian graphical models and variable selection at both population (cancer-specific) and patient-pathway levels. PRECISE outputs cancer-specific integrated networks, infers patient-specific networks (PRECISE networks), and elicits interpretable pathway-level signatures (PRECISE scores) by summarizing the patient-specific network as a single score that can be used for tumor stratification as well as constructing pathway-based prognostic models in cohorts of patients.In this study, we develop a novel and general Bayesian framework that conflates both global and local approaches: personalized cancer-specific integrated network estimation (PRECISE). PRECISE is a multi-scale approach that integrates multiple sources of information: 22. Predictions of protein levels from RNA levels are weak (with correlations <=0.5), which can be explained by many factors, for example (i) suppression of translation by miRNAs, (ii) rate of degradation of mRNAs may be different from their protein products, (iii) phosphoproteins are dependent on signaling activity and may not correlate well with their transcripts alone, (iv) in the case of protein complexes, the presence or absence of other proteins in the complex may stabilize the complex or destabilize it resulting in altered protein expression22. Furthermore, because the signaling properties of proteins can be modified by post-translational events like phosphorylation, acetylation and ubiquitination, it is difficult to predict their functional impact at the genomic or transcriptomic level23.We delineate the interplay of proteins in signaling pathways in various cancer types and infer patient-specific proteomic pathway activities. While pathways have often been assessed at the DNA and RNA levels, proteomic signaling networks are emerging for two critical reasons. First, the amount and function of proteins are downstream summation of changes that occur at the genomic and transcriptomic levels23. Thus, assessing the cancer-specific topology and structure of proteomic signaling networks and relating them to (prognostic) clinical outcomes are important tasks toward understanding the biological mechanisms behind cancer development and progression and, more importantly, identifying potential therapeutic targets5.Second, the signaling properties of proteins can be further modified by post-translational events e.g. through phosphorylation and other processes, which cannot be predicted at the genomic or transcriptomic levelhttp://tcpaportal.org) of 7,714 samples from 32 tumor types from The Cancer Genome Atlas (TCGA) where-in we analyze multiple pathways of critical importance to the behavior of tumor cells and response to therapy. From the comprehensive exploration of proteomics-based patient-specific pathway signatures across 32 cancer types, we delineate pan-cancer commonalities and differences in key proteomic signaling pathway activities and prognostic power of the patient-specific scores. We demonstrate the superior performance of PRECISE algorithm as compared to other established and current approaches as well as potential clinical utility of our scores across tumor types. We believe that our study is the largest and most comprehensive network-based modeling and exploration of pan-cancer proteomic pathway activities for tumor stratification and prediction.We applied PRECISE to a pan-cancer reverse-phase protein array (RPPA) proteomic data set  as described in the previous TCGA RPPA study for 11 tumor types22. The gene/protein membership for each pathway is listed in Supplementary Table pan-cancer network signaling and rewiring.We applied PRECISE independently on 32 cancer types from TCGA, which are listed in Table connectivity score (CS), which is the constructed as the ratio of the observed number of edges in the network to the total number of possible edges and its permutation p-value  in multiple lineages including colon/rectum cancers (CORE), BRCA, and thyroid cancer (THCA) while only CORE samples provided its significance with We investigated the extent of cross-signaling (among proteins) for each pathway across lineages. For defining a metric of connectivity in the corresponding integrated cancer-specific network, we use edge consistency (EC), which for a given edge is defined as the number of tumor types that hold the particular edge. We compared the EC to the known protein-protein interaction (PPI) score to corroborate with existing evidence as well as find new ones. Figure A pan-cancer analysis was undertaken to determine the extent to which edges (both regulatory and correlative) in each of the pathways are shared across tumor types. To this end, we used the To identify subtypes for the pan-cancer patients and pathway determinants of those subtypes, we developed a tumor stratification algorithm for all of the tumor samples in the 32 lineages based on the PRECISE scores. The PRECISE scores were computed by deriving summary measures from the PRECISE networks (patient-specific), indicating that the entire pathway is suppressed, neutral or activated. For the clustering analysis, we combined the two positive values for the suppressed and activated scores by summation- the combined score for a pathway and patient- thus represents a global perturbation level including activated or suppressed directions of the pathway for the patient. After matching all four platforms, RPPA, mRNA expression, microRNA expression, and DNA methylation, we had 6,844 patients across the 32 lineages. For all patients and all pathways, we constructed a 6,844 × 12 suppressed and activated PRECISE score matrix, with each row corresponding to a patient and each column corresponding to a pathway. This matrix was the input for our clustering analysis, which identified 23 optimal clusters of patients Fig. . We concenrichment probability (EP), which is defined as the posterior probability of enrichment in patients within a particular clinical subtype  in a given cluster, with EP close to 1 if the proportion of patients from the subtype in the cluster is significantly higher than the proportion of patients from outside the subtype in the cluster by random chance (EP close to 0). The detailed information on computing EP is in Section S1.3. The DNA damage response and RAS/MAPK pathways were highly activated or suppressed for all BRCA samples, and most samples showed activation or suppression for the apoptosis, PI3K/AKT, and TSC/mTOR pathways  and thyroid carcinoma (THCA) samples were both located primarily in clusters C5 and C8 Fig. . KIRC me30 and, in our study, the mutations were found in KIRC and THCA samples in C8 (60%), compared to no mutations in KIRC and THCA samples in C5 , we computed the enrichment probability (EP), which is defined as the posterior probability that the proportion of samples with a mutation in a cluster is higher than the proportion of samples with a wild-type modification in the cluster Fig. . The mos C5 Fig. . This inPRECISE status—which indicates that the pathway is suppressed, neutral, or activated for the patient, can be decided by the maximal probability. To investigate the prognostic power of the PRECISE status, we performed Kaplan-Meier survival analysis with a log-rank test to determine the difference in survival probabilities among the three groups according to the PRECISE statuses. The direction of the association with PRECISE statuses varied in different tumors, indicating that the effects of the pathways were dependent on the intrinsic characteristics of the tumor, either in terms of overall prognosis or intrinsic proteomic pathway expression patterns  and Receptor tyrosine kinases (RTK) pathways – which have established and critical roles in cancer development and metastasis across multiple tumor type. We correlated PRECISE EMT activated/suppressed score with epithelial tumor types such as Ovarian (OV), Lung Adenocarcinoma (LUAD), and Stomach Adenocarcinoma (STAD), and mesenchymal tumor types such as brain tumors, Glioblastoma Multifome (GBM) and Low Grade Gliomas (LGG). The mesenchymal tumor types such as brain tumors, GBM and LGG tend to have higher PRECISE EMT scores than the epithelial tumors such as OV, LUAD and STAD  and thus enable a fair comparison. Using prior knowledge from protein causal structure learning and existing database helps to improve its prognostic power for most pathways. The difference was the largest in the breast reactive pathway that were defined based on the breast cancer patients: 14 folds increase in p-values when we use no prior  native pathway score, which is defined as the sum of all positive regulatory components minus that of the negative regulatory components in a particular pathway as used and reported by recent literatures33, (3) PARADIGM score, which measures the patient-specific genetic activities incorporating curated pathway interactions among genes using expression16, and (4) Random protein signatures obtained by the method following34 (see Section S1.1). The naïve method gives all proteins equal weight (thus ignoring network information) in estimating the pathway-level score; whereas the native method takes directionality (+/−) into account to yield overall pathway-level scores. PARADIGM uses pathways from the National Cancer Institute Pathway Interaction Database (NCI-PID) and assigns an integrated pathway level (IPL) reflecting the activity of each node by computing patient-specific log-likelihood ratio. The major difference between these methods and our PRECISE algorithm is that we estimate and construct de novo cancer-specific networks (model building) to calibrate the patient-level pathway signatures by exploiting multiple sources of information obtained from cancer-specific multi-platform data, causal structure learning and existing interaction database. It is well established that sub-networks within signaling pathways and their products undergo changes in response to different conditions35. Such network rewiring in cancers are manifested at the level of signaling networks and is not currently well understood5. For FDR < 0.1, PRECISE, naïve and native scores were prognostic for 31, 21, and 4 numbers of tests across all the 12 pathways and lineage combinations  ons Fig. . There wons Fig. . For KIRons Fig. .32 across four tumor types. UCEC, OV, SARC, and UCS – to assess shared/differentiated pathway-based features. There was clear evidence that our PRECISE method preserves tumor-specific signals  better than native and PARADIGM scores and showed more evident pattern of activities across different tumor types . We use the proportions of random signatures that work better than those obtained from PRECISE as another measure of significance; we refer those proportions as concordance score between the predicted value using the PRECISE method and the observed value for each protein in a given sample (Section S1.4). This score takes values between 0 and 1, with a high value indicating that the predicted value has concordance with the observed value. Almost all lineages had concordance scores greater than 0.9; however, uveal melanoma (UVM) had large variations below 0.9, which was likely due to the small sample size of 12   explain tumor-specific pathway networks and connectivity; (2) develop patient-specific pathway networks and scores that are reflective of the molecular underpinnings in various tumor types; (3) are well-calibrated to identify tissue-specific signals, prognostic and clinical utility across multiple tumor types and; (4) general enough to be applied to any modern high-throughput technology that generated complex multivariate data.We develop a general Bayesian framework, called PRECISE, for inferring cancer-specific network structures and topologies of signaling pathways and inferring patient-specific pathway activities. We report the discovery that, through model-based integration of multiple sources of information such as existing protein-protein interactions databases, data-driven a priori knowledge leads to sparser and more biologically interpretable edges. We found a high degree of variation in pathway activity across cancer types, with some signaling pathways showing higher connectivity in certain tumor types than others , which suggests that the data-driven cancer-specific network aligns well with the a priori functional characteristics of this pathway. The RTK pathway and the TSC/mTOR pathway showed high cross-signaling across most major tumor types. The pan-cancer analyses of edges to determine network relationships (both regulatory and correlative) revealed that several network/edge relationships were highly conserved across tumor types, some of which confirmed existing knowledge while others represented novel links with significantly high probability which might warrant further experimental and functional validation  and correlative  edges between the proteins in the pathways. Through Bayesian sparse regression modeling, our approach is able to produce sparse tissue-specific and pathway-specific networks and the incorporation of ion Fig. , S3.Using Bayesian posterior predictive modeling, we de-convolve the patient-specific networks from the global tissue-specific ones and derive network-based scores reflective of the underlying patient-specific pathway-level activities. Using these PRECISE scores in an unsupervised clustering algorithm to assess cross-tumor heterogeneity across all tumor types, we find a high fidelity between the optimal clusters and the tumor lineages, e.g., 23 clusters, 14 of which were enriched with single type of tissue at the site of tumor origin with markedly different pathway activation status, which can help delineate pathway-based therapeutic targets. We found three clusters that represent the 3 subtypes of breast cancer, luminal, HER2 positive and basal, and both of the three clusters and pathway scores for breast reactive and core reactive pathways were significantly associated with patients’ survival times. Patients who have KIRC and THCA tumors were mostly classified into two PRECISE clusters and the PBRM1 gene that is a novel drug target for KIRC was found in patients located in one of the clusters, which indicates that PRECISE effectively classified subtypes across cancers.de novo causal networks, and computed the pathway scores across the multiple pathways for breast cancer patients that showed the clearest patterns of prognostic proteomic signatures and thus enable a fair comparison. We find that using prior knowledge from protein causal structure learning and existing database helps to improve its prognostic power for most pathways. The difference was the largest in the breast reactive pathway that were defined based on the breast cancer patients: 14 folds increase in p-values when we use no prior knowledge.We also investigated different aspects of PRECISE by performing external and internal validations and showing potential clinical utility. For objective external validation of the PRECISE algorithm, we compared PRECISE scores with that of published as well as scores obtained by random (unstructured) perturbations of the data. We found clear evidence that our PRECISE method preserves tumor-specific and clinical subtype-specific signals better than other existing methods and has much higher prognostic power in detecting pathway-specific prognostic signals between and across tumor types as compared to existing methods and random signatures. For the internal validation, we used a diluted version of our PRECISE method without using prior calibration from existing databases and 22. Therefore, direct estimates of RTK pathway activity using proteomics data would potentially be more predictive of tumor response. Importantly, the PRECISE pathway scores, while developed in the absence of clinical data, were robustly able to predict outcomes across patient lineages.For demonstrating potential clinical utility, we investigated the role of epithelial-mesenchymal transition (EMT), which has established critical roles in cancer development and metastasis across multiple tumor type. The mesenchymal tumor types, low-grade gliomas and glioblastoma multiforme tend to have higher PRECISE EMT scores as compared to epithelial-type tumors such as ovarian, lung and stomach adenocarcinomas, which shows PRECISE preserves and accentuates the actual biological mechanism of these pathways in relevant tumor types. Furthermore, we also confirmed that HER2+ breast cancer samples  have high PRECISE RTK scores compared to other breast samples and can potentially be used as a complementary proteomics surrogate for determining HER2- status. Our rationale for using RPPA-based proteomics data rather than mRNA’s, which have been shown to be poor surrogates for estimating protein expression33. This will not only shed light on some of the fundamental cancer mechanisms but also aid drug development and screening. Moreover, integrative analysis of patient and cell-line data, and identification of commonalities and differences in network biology could reveal insights into the specific roles of tumor microenvironment in cancer development and progression. From a methodological perspective, we note that our model assumes a linear relationship between the proteins for simplicity and interpretability. It is possible to extend this approach to allow for more flexible and non-linear dependence structures36.There are several refinements and generalizations possible for the PRECISE methodology. In this article, we only focused on patient tumor samples. An interesting next step would be to investigate the functional proteome of the cancer cell lines that has recently been collectedavailability of data and material section below).To the best of our knowledge, this is the first comprehensive exploration of pathway-specific network signatures for stratification and prediction using the cancer functional proteome. A more robust tumor stratification and pathway selection will emerge as more comprehensive set of functional pathway information from more mature proteomic data are generated across the entire proteome. Our PRECISE framework is a step towards the major goal of precision medicine — to find a disease treatment and its optimal targets while taking into account individual variability. Another contribution of this method is toward establishing the translational relevance of the functional proteome in research and clinical settings. We have also developed an R package for PRECISE and developed an online data and visualization repository, that compiles a comprehensive database of networks, scores, and pathway signatures that are easily accessible to the scientific and research community  were downloaded and preprocessed using ‘DownloadRNASeqData’ and ‘ProcessRNASeqData’ functions for gene-level expression. For microRNA expression data, miRNASeq data (generated by illuminaga or illuminahiseq) were downloaded using the ‘DownloadmiRNASeqData’ function. Using the ‘ProcessmiRNASeqData’ function, we processed the miRNASeq data, using the Hg19 reference genomes to map the reads. Then we annotated the microRNAs to genes using the ‘microRNA’ package in R version 3.2.0. DNA methylation data (generated by Illumina Infinium HumanMethylation 27 K or 450 K) were downloaded and preprocessed using ‘DownloadMethylationData’, ‘DownloadMethylation27Data’ and ‘DownloadMethylation450Data’.We used a pan-cancer reverse-phase protein array (RPPA) proteomic data set of 7,714 samples from 32 tumor types from The Cancer Genome Atlas (TCGA) . As an illustrative example, Supplementary Fig. We developed a general Bayesian framework to estimate cancer-specific and patient-specific networks and elicit patient-level pathway scores by integrating data arising from multiple platforms and incorporating existing network information. With Pan-cancer TCGA proteomic, genomic and epigenomic data, PRECISE uses a multi-scale approach Fig. . The impBriefly, we use a Bayesian regression model, in which each protein is regressed on covariates that include the protein regulators learned from the data-driven causal network and the PPIs (as prior information), and features from upstream data. The selected regulators for all proteins constitute cancer-specific integrated network. Given the cancer-type-specific network, we deconvolve the patient-specific PRECISE scores, using the status of each protein (node) of the networks as suppressed, neutral, or activated. We use these PRECISE scores for patient stratification and clinical outcome prediction. PRECISE is a general framework to achieve patient-level and pathway-level signatures by sequential estimations of cancer-specific  and patient-specific  networks. The novelty of our methodology lies in conflating causal structure learning, Bayesian graphical models and variable selection at both population (cancer-specific) and patient-pathway levels. The integrative modeling strategy involves smooth calibration of the prior by applying a causal structure learning method on resampled data, defining a patient-specific network using cancer-specific network with patient-specific labels on the nodes (proteins) with the estimated activation statuses of proteins, and subsequently estimating patient-specific pathway and network scores.i to protein j, where protein i is a regulator of protein j or protein j is a target of protein i. We decide the weights prior inclusion probabilities for the protein regulators based on data-driven causal structure and known PPI scores (discussed in the next section). Because both i for n patients and includes Ki covariate vectors  and microRNA expression. For protein i, the 13We aimed to estimate integrated cancer-specific networks using Bayesian regression methods on each of the proteins with other molecular profiling data. For regulatory relationship among proteins, let g reflects the prior on g implies deviation from 38 to infer the posterior network. Specifically, for each regression of protein I, its integrated cancer-specific regulators that have edges directed toward the protein i are defined by the proteins or other covariates that have a posterior inclusion probability  greater than 0.5. Therefore, among proteins, the network includes both directed regulatory edges - when regulator proteins of a protein are not targets of the protein and correlative edges- where-in both proteins in a link are regulators and targets.The hyper-parameter i for a patient j (A PRECISE network is the integrated cancer-specific network with patient-specific labels on the nodes (proteins). Specifically, the activation statuses of the nodes are evaluated by estimating the posterior predictive density of each of the proteins for each patient. To determine the activation status of a protein p genes, the pathway activity scores for a patient j is given byPRECISE pathway status- which indicates that the pathway is suppressed, neutral, or activated for the patient, can be decided by maximum of the three PRECISE pathway scores, j.To compute pathway activity scores for each patient, we derive summary measures from the PRECISE networks obtained from the Step 2, indicating that the entire pathway is suppressed, neutral and activated. Under the PRECISE networks, the number of nodes that have edges from protein To decide the prior inclusion probabilities for protein regulators  of Step 1.2. Draw a random sub-sample , which provides known and predicted PPIs. Each interaction is associated with a combined confidence score that integrates the various types of evidence such as genomic context, high-throughput experiments, co-expression, and previous knowledge. STRING v10 data were downloaded. As the weighted prior network, we collected all PPIs in the database, where each PPI has a score ranging from 0 to 1. A higher score means higher confidence. Note that the PPI network from STRING database only serves as a prior since the PPI scores in the STRING DB, ranged from 0 to 1 are used in combination with the de novo cancer-specific PPI scores, also ranged from 0 to 1 from the causal structural learning described above in each type of cancers.In combination with the data-driven causal structure, we collected the PPIs from the STRING database (16) on the known network, we use the soft-thresholding approach by calibrating the priors of regressions based on the average interaction scores from data-driven de novo cancer-specific causal structure and existing global PPI information. Finally, our methodology is general enough to be combined with any PPI database that is relevant to scientific context.Our PRECISE estimation method for cancer-specific networks conflates Bayesian variable selection and causal structure learning. Thus, if the data does not support a particular a priori network structure, it will exhibit low posterior probabilities in the final estimands and will thus be naturally filtered out. Instead of performing hard-thresholding (e.g. PARADIGMp genes and a patient j, we combined the activated and suppressed PRECISE scores, 43.The PRECISE method provides processed and highly interpretable pathway-level functional summaries that compress the regulatory relations from DNA methylation, mRNA/microRNA expression, and protein regulators. Using the resulting PRECISE score matrix as an input data, we obtain robust pan-cancer stratification. For a pathway with Supplementary Information


Ferroic materials are well known to exhibit heterogeneity in the form of domain walls. Understanding the properties of these boundaries is crucial for controlling functionality with external stimuli and for realizing their potential for ultra-low power memory and logic devices as well as novel computing architectures. In this work, we employ synchrotron-based near-field infrared nano-spectroscopy to reveal the vibrational properties of ferroelastic (90 Ferroic domain walls are nano-objects that are considered functional elements in future devices. Here, the authors study phonons across ferroelastic domain walls by synchrotron-based near-field infrared nano-spectroscopy and relate these changes to the order parameter which helps to understand domain wall dynamics. Ferroics also have naturally occurring heterogeneities in the form of domains and domain walls18. This is especially true for Ca20 where the polarization arises from a trilinear coupling mechanism23 and abundant charged domain walls have been observed24. Atomic force and piezoforce imaging reveal the different orientations of directional order parameters and domain wall character, providing a physical playground for graph theory. Like other counterpart materials17, ferroelectric domain walls in Ca24. Theoretical modeling and electron diffraction experiments recently revealed that polarization rotates across the ferroelectric walls in a manner that is more Néel than Ising-like25. Ferroelastic walls in Ca20. Because the boundaries connect domains with different spontaneous strain states, mechanical compatibility conditions determine their orientation27. These ferroelastic walls are also 9020.Many unique properties in functional materials arise from spatially heterogeneous electronic and magnetic states. Examples include the formation of charge and spin stripes in some cuprates and nickelates and phase separation across the Mott transition of VO28. Second-harmonic generation spectroscopies also provide insight into local symmetries and chemical structure and, at the same time, are sensitive imaging tools8. X-ray photon correlation spectroscopy has uncovered ferroelectric domain wall dynamics in PbZr29. Despite a number of significant discoveries, almost nothing is known about domain wall phonons. Infrared spectroscopy, a powerful tool for probing phonons and their symmetries, has been unable to address the nano-scale heterogeneities of domain walls due to its poor spatial resolution. The long wavelengths of infrared light necessarily mean that, even for a diffraction-limited beam, traditional infrared techniques average over any micro- and nano-structured character of a target material, thereby reducing the sensitivity to these heterogeneities30. The development of near-field spectroscopic techniques has made real space imaging of nano-scale heterogeneity feasible, although most efforts are confined to the middle infrared due to the availability of suitable laser and broadband sources32. The extension into the far infrared using an accelerator-based source33 enables broadband measurement of the low-frequency response including phonons. Synchrotron-based near-field infrared spectroscopy34 thus has the potential to unlock the properties of defect states such as domain walls in complex oxides and chalcogenides. Near-field infrared spectroscopy is, of course, quite different than tip-enhanced Raman scattering in that ungerade (odd symmetry) phonon modes, which are crucial to the development of ferroelectricity and other functionalities, can be probed and analyzed.There is considerable interest in the dynamics of domain walls. Broadband scanning impedance microscopy of hexagonal ErMnO36. Loss mechanisms involving phonons are also key to controlling decoherence in domain wall-based computing architectures37.In order to uncover the behavior of fundamental excitations like phonons at domain walls and to explore the structural distortions that they represent, we performed far infrared synchrotron-based near-field nano-spectroscopy of the local phonon response at domain boundaries in Ca39. This structure  that transforms like the 24. For the chosen setting of the orthorhombic axes relative to the tetragonal axes, the twin domains are labeled by different settings of the space group symbol: 27. The wall symmetry is obtained by combining the symmetry elements of the two domains and those transforming one domain into the other. To go from twin A to twin B, all three structural order parameters rotate  and the other arm is a moving mirror. Synchrotron infrared light is focused and scattered off an AFM tip in close proximity to the sample surface. The scattered light is combined with the reference beam from the moving mirror on a silicon or KRS-5 beamsplitter and detected by a liquid helium-cooled Ge:Cu detector. Scanning the mirror of the reference arm creates an interferogram, which is Fourier-transformed to obtain both amplitude and phase information, which is related to the real and imaginary parts of the optical dielectric function of the material. The incident light is 49. To discriminate the near-field signal from the far-field background signal, the AFM is operated in non-contact (tapping) mode at a typical frequency of 250–300 kHz, and the corresponding detector signal is demodulated at twice the tapping frequency. Our measurements were performed with typical free oscillation tip amplitudes in the 80–100-nm range with a setpoint between 70% and 78% of the free-tapping amplitude, such that the engaged tapping amplitude values were in the 55–75-nm range. Routine approach curves with free oscillation tip amplitudes in the 80–100-nm range taken on gold indicate a near-field tip enhancement within Near-field infrared spectroscopy was performed using the set-ups at beamlines 5.4 and 2.4 at the Advanced Light Source, Lawrence Berkeley National LaboratoryOur near-field infrared measurements were carried out at room temperature over a frequency range between 330 and 800 cmWe also carried out traditional, far-field spectroscopy using a suite of Fourier transform and grating spectrometers covering the spectral range from 15 to 65,000 cm50 and the PBEsol functional, as implemented in VASP51. We used the  VASP pseudopotentials with electronic configurations 352. We performed group theoretic analysis with the aid of ISOTROPY53 and the Bilbao Crystallographic Server57 and visualized crystal structures using VESTA58.DFT calculations were performed using projector augmented wave pseudopotentialsSupplementary Information


Proximity-based labeling has emerged as a powerful complementary approach to classic affinity purification of multiprotein complexes in the mapping of protein–protein interactions. Ongoing optimization of enzyme tags and delivery methods has improved both temporal and spatial resolution, and the technique has been successfully employed in numerous small-scale (single complex mapping) and large-scale (network mapping) initiatives. When paired with quantitative proteomic approaches, the ability of these assays to provide snapshots of stable and transient interactions over time greatly facilitates the mapping of dynamic interactomes. Furthermore, recent innovations have extended biotin-based proximity labeling techniques such as BioID and APEX beyond classic protein-centric assays (tag a protein to label neighboring proteins) to include RNA-centric (tag an RNA species to label RNA-binding proteins) and DNA-centric (tag a gene locus to label associated protein complexes) assays. Key advantages include its ability to capture weak/transient interactions that can be lost in standard AP approaches, its applicability to both soluble and insoluble proteins, and the strength of the association of biotin with streptavidin, which allows efficient high-stringency protein extraction and capture methods that help minimize background contaminants. As demonstrated in more than 100 publications to date, BioID can be applied to a wide range of cellular proteins, from transcription factors and signaling molecules to ubiquitin ligases and cytoskeletal components. Although the majority of these studies used cultured cells, the technique has been extended to other model systems, including yeast2, protozoa3, plant protoplasts4, amoebae5, embryonic stem cells6, and xenograft tumors7. The technique has also been combined with lentiviral infection to map synapse-associated protein complexes in intact mouse brain8.In the six years following its introduction as a novel method to label neighboring proteins in cells with biotin9 and later extended to proximity labeling of protein complexes in live cells10. A key advantage of APEX over classic BioID is the significantly faster rate of labeling (minutes versus hours). When paired with quantitative proteomic approaches, this higher temporal resolution can facilitate the identification of dynamic changes in protein–protein associations over time or in response to cellular perturbation.A functionally related method, APEX, was originally developed to facilitate high-resolution imaging of cellular structures by electron microscopy (EM)This review will discuss the further evolution of the original BioID and APEX labeling reagents to improve their efficiency and applicability. It will also highlight their integration into organelle-, RNA-, and DNA-centric workflows to aid in the assembly of protein interaction network maps, characterization of ribonucleoproteins (RNPs), and identification of regulatory complexes associated with specific gene loci.Escherichia coli-derived biotin ligase (BirA*) with a catalytic site mutation (R118G) that destabilizes retention of the activated biotin molecule (biotinoyl-5′-AMP)1. This dissociates from the ligase and can react with free primary amines of exposed lysine residues in neighboring proteins, resulting in covalent attachment of biotin , which in turn recruits electron-dense osmium to provide contrast for EM18. This offers the ability to directly couple high-resolution interactome mapping to high-resolution imaging.APEX is a 27 kDa monomeric ascorbate peroxidase that catalyzes the oxidation of biotin-phenol to the short-lived (<1 ms) biotin-phenoxyl radical in the presence of H2O2 . Reactio2O2 15. Cell19 and SPPLAT (selective proteomic proximity labeling using tyramide)20. More recently, HRP was extended to intracellular antibody-based proximity labeling in fixed tissues and cells in a technique called “Biotinylation by Antibody Recognition”21. In this approach, samples are fixed, permeabilized, and stained with the desired primary antibody and species-specific HRP-conjugated secondary antibody, after which they are incubated with biotin-phenol and the biotinylation reaction induced by brief exposure to H2O2. This approach avoids fusion and overexpression artefacts but requires a monospecific antibody that is not sensitive to fixation artefacts.Horseradish peroxidase (HRP) can similarly be employed for both EM and proximity labeling but is a larger tag (44 kDa) and not as active in the cytosol and other reducing environments of the cell. It has been used primarily to study cell surface or secretory pathways via antibody- or ligand-based targeting of the enzyme, using techniques such as EMARS 24. This means that they can be divided into N- and C-terminal fragments that will re-form a functional enzyme when brought into close proximity. Thus,in vivo protein-fragment complementation assays (PCAs) can be carried out by fusing two proteins of interest to N- or C-terminal BirA* or APEX fragments and expressing them in cells. Association of the target proteins in the presence of biotin (or biotin-phenol and H2O2) drives enzyme re-formation and subsequent biotinylation of proximal proteins  protein Ago2 in complex with two different known binding partners23. These two initial studies show that PCA is supported by splitting the tag at either amino acid (aa) 140/14122 or 256/25723, although comparative testing suggests that the aa256/257 split supports a higher reconstituted ligase activity23. For the soybean-derived second-generation enzyme APEX215, PCA was supported when the tag was split at aa201/20224. As with otherin vivo PCAs, it is important to optimize expression levels of the two fusion proteins to maximize sensitivity and minimize artefacts, and multiple tag conformations may need to be tested to detect enzyme re-formation .As an extension of the applicability of biotin ligase- and peroxidase-based proximity labeling approaches, both BirA* and APEX2 have recently been shown to be amenable to protein-fragment complementationroteins . AlthougThe advantages of biotin-based proximity labeling approaches over classic antibody-based AP/MS are tempered by caveats that need to be taken into account when designing experiments and interpreting results. These include the potential for artefacts owing to the size or placement (or both) of the tag or the level of overexpression , and non-specific background biotinylation by free enzyme generated through cleavage or degradation of the fusion protein. Proteins that bind biotin directly, such as the mitochondrial propionyl-CoA carboxylase subunits PCCA and PCCB, also contribute to background noise. Although the strong streptavidin–biotin interaction enables higher-stringency protein extraction and capture workflows that help to minimize non-specific binding of proteins to the affinity matrix, the potential for false positives is not entirely removed.It should be noted that the high affinity of the streptavidin–biotin interaction, though facilitating the capture of biotinylated proteins, also complicates their elution from the affinity matrix. If proteins that are less biotinylated are eluted more efficiently than highly biotinylated proteins, this will affect both protein identification and quantitation. One option is on-bead digestion, although biotinylated peptides will be left on the affinity matrix. Another is to use an anti-biotin antibody to immunoprecipitate biotinylated proteins or peptides following BioID labeling.in vivo. There are also concerns that the H2O2 treatment, though brief, could affect the cellular oxidative status and induce a stress response.Peroxide-based labeling approaches can also be complicated by the higher hydrophobicity of biotin-phenol compared with biotin, which potentially affects substrate bioavailability in specific cellular locations. Alternate substrates such as biotin-DEAE have been proposed but still need to be tested25. This seems surprisingly small given the half-life of the biotin adenylate ester  and the relatively long incubation times required for efficient labeling11. Although the labeling radius of APEX has not been measured in a similar fashion, the short half-life of biotin-phenoxyl and EM analysis of diffusion suggest that it is in the range of about 20 nm9. Ongoing attempts to define the labeling radius of both BioID and APEX by network mapping of well-studied protein complexes would help to assess the benefits of one method over the other, depending on, the time and spatial resolution required. Complementary approaches that have been developed to offer higher-specificity labeling include NEDDylation and PUP-IT. NEDDylation is based on the fusion of an engineered version of the NEDD8 E2-conjugating enzyme Ubc12 (NEDDylator) to a bait protein or small molecule, with conjugation of the ubiquitin-like NEDD8 protein tag to prey proteins only occurring by direct attack of their lysine ε-amines upon the thioester in the active site of the Bait-NEDDylator26. PUP-IT is another prokaryote-derived method, developed to identify membrane protein interactions, in which cells co-express a small protein tag (Pup) with a C-terminal Gly-Gly-Glu domain and the Pup ligase (PafA) fused to a bait protein27. In a reaction similar to ubiquitination, PafA catalyzes phosphorylation of the C-terminal Glu in Pup and conjugates it to an exposed lysine-residue side chain on the target protein. Importantly, activated Pup (which cannot diffuse across membranes) is kept bound to the enzyme and thus operates within a more restricted labeling radius. A caveat to PUP-IT is that the enzyme is not as active as APEX and thus a longer labeling time is required.The effective biotinylation radius of the enzyme remains a significant concern for both methods, given that diffusion of the active moieties can lead to biotinylation of proteins whether or not they make direct contact with the bait protein. BioID analysis of different constituents of the structurally well-studied nuclear pore complex estimated a limited labeling radius of about 10 nm for BirA*Aquifex aeolicus that naturally lacks this domain28. The ligase was humanized, adapted for proximity labeling by mutation of a conserved residue in the catalytic domain (R40G) and dubbed “BioID2” 29. Removal of the N-terminal DNA-binding domain in this case did not adversely affect activity, and the 28 kDa tag has been dubbed “BASU”  but rather provides datasets that represent the sum of interactions for the target protein over several hours. During this time, the cells may divide and endogenous interactors may be degraded (at different rates), translated, and biotinylated again. To reduce the labeling time required for BioID, significant effort has been directed toward engineering a smaller and more-efficient promiscuous biotin ligase. Having noted that deletion of the N-terminal DNA-binding domain of BirA* adversely affected its ligase activity, the Roux lab used a bioinformatic approach to identify a smaller biotin ligase (27 kDa) fromBioID2” 28. In i “BASU” 29.E. coli-derived BirA with yeast surface display screening to generate two new tags: TurboID and MiniTurbo  and for carrying out parallel AP/MS and BioID experiments using a single multifunctional tag and the same affinity resin . The deposition of lists of proteins commonly identified in negative control experiments into the searchable online CRAPome contaminant repository (http://crapome.org)33 has also provided a valuable screening tool to help prioritize hits in BioID datasets for follow-up analysis.Researchers are also developing and sharing more-efficient methods for delivering proximity labeling tags into cells and tissues  to highlight distinct and overlapping components.RNPs carry out a large number of essential and functionally diverse roles throughout the cell, and various strategies have been developed over the years to isolate and characterize both their protein and their RNA components. “Protein-centric” approaches capture known RNA-binding proteins  in order to identify associated proteins or RNA or both. In a recent study, BioID was extended to large-scale RNP protein interactome mapping by analyzing the biotinylation profiles of 119 proteins known to be associated with different aspects of mRNA biology34. Peroxidase was targeted to different regions of the cell—for example, APEX to the nucleus, cytoplasm, mitochondrial matrix, and cytoplasmic face of the endoplasmic reticulum (ER) and HRP to the ER lumen—in the presence of biotin-phenol, followed by chemical crosslinking of protein-RNA, enrichment of biotinylated proteins, and identification of associated RNA using next-generation sequencing (RNA-Seq). A significant advantage of this technique is that it does not require the development of customized purification schemes for each compartment.The identification of RNA species associated with captured protein requires an additional crosslinking step prior to harvesting to preserve the interaction. This technique is known as RNA immunoprecipitation (RIP). The Ting lab recently developed an “APEX-RIP” approach to map transcriptomes for specific subcellular compartments35), whereas others target specific RNAs for pulldown by using short biotinylated complementary oligodeoxyribonucleotides  or long capture probes tiled across entire target RNA for higher specificity .RNPs can also be analyzed by using “RNA-centric” approaches, which are based on the capture of a target RNA for the identification of associated proteins by MS. Some use poly(dT) affinity resin to capture all polyadenylated mRNAs for global interactome mapping , followed by ultraviolet (UV)-induced crosslinking and click chemistry-mediated biotinylation of EU for streptavidin-based capture of labeled RNA. By circumventing the limitations of poly(dT)-based capture (which misses non-polyadenylated mRNAs and non-coding RNAs), this technique enables transcriptome-wide analysis of all RNA species. CARIC (click chemistry-assisted RNA interactome capture) is a similar approach that also incorporates the photosensitive uridine analog 4-thiouridine (4SU), which promotes more-efficient crosslinking using longer-wavelength UV light , based on the incorporation of MS2 stem loops into nascent RNA and the expression of BirA* fused to the MS2 coat protein (MCP) for proximal protein labeling, was recently deposited on bioRxiv40. Given the success of BioID in mapping multiprotein complexes, it is likely that these types of RNA-targeted proximity labeling approaches will become popular methods for characterizing RNPs.The BioID-based RaPID (RNA–protein interaction detection) technique is a hybrid RNA-centric method that targets a biotin ligase to a specific RNA sequence to promote biotinylation of associated proteinsse BASU . Recruit41. A limitation of both strategies is that they provide information about DNA interactions only for the protein chosen for capture (or methyltransferase-based labeling), although bioinformatic analysis of the overlap of datasets collected for multiple DNA-binding proteins can help to identify patterns and potential complexes.Another area of research that would benefit greatly from increased sensitivity and specificity is the mapping of transcriptional regulatory complexes on chromatin. Gene expression is controlled by the assembly of unique combinations of transcriptional regulatory proteins on specific regions of DNA, which can result in either activation or repression of the target gene. Chromatin immunoprecipitation (ChIP) is a highly popular technique that has been used to identify the region(s) of the genome with which a particular transcription factor associates. Chemical crosslinking  is followed by shearing of genomic DNA into smaller fragments and immunoprecipitation of the protein of interest (endogenous or tagged). The crosslinks are reversed to release the associated DNA fragments, which can then be identified by next-generation sequencing (ChIP-Seq). An alternate method for mapping protein–DNA associations is DamID, which is based on expression of the protein of interest fused to a DNA adenine methyltransferase and subsequent identification of adenine-methylated DNA fragments43) or directly on the affinity matrix beads . Caveats can include high levels of background contaminants, masking of low-abundance hits by the large number of antibody peptides, and identification of non-chromatin-associated complexes and DNA “hitchhikers” . ChIP-SICAP (ChIP combined with selective isolation of chromatin-associated proteins) was recently developed to help distinguish chromatin-associated from non-chromatin-associated interactors for a target protein45. It starts out as a standard ChIP experiment (capture of target protein and associated crosslinked complexes) but then adds a second purification step to specifically enrich chromatin-associated complexes for MS analysis. This is based on terminal deoxynucleotide transferase (TdT)-mediated end labeling of DNA with biotinylated nucleotides and capture on streptavidin beads , target DNA can be directly captured for the identification of associated proteins using MS. This is called “Reverse ChIP”, and strategies such as PICh (proteomics of isolated chromatin segments)46 and HYCCAPP (hybridization capture of chromatin-associated proteins for proteomics)47 are based on sequence-specific hybridization and capture of nucleic acid probes. The advent of the CRISPR/Cas9  system provided another option for targeted studies of the landscape surrounding specific regions of DNA.In order to identify, in a non-biased fashion, transcriptional regulatory complexes that associate with a specific gene locusin vivo gene editing, CRISPR/Cas9 is based on the targeting of the Cas9 endonuclease to a specific genomic sequence by co-expression of a single-guide RNA (sgRNA). Mutagenesis of the Cas9 enzyme to render it catalytically dead (dCas9) allows it be targeted to a specific gene locus without inducing double-strand breaks. Fusion of regulatory proteins or fluorophores to dCas9 takes advantage of this and has enabled techniques such as sequence-specific gene regulation, epigenetic editing and visualization of gene loci in live cells. 50, and CLASP (Cas9 locus-associated proteome)51. All are based on targeting of dCas9 to a specific gene locus via co-expression with an sgRNA, followed by chemical crosslinking, DNA shearing, and AP/MS of the tagged dCas9. The related CAPTURE (CRISPR affinity purificationin situ of regulatory elements) method introduces a biotin acceptor peptide into dCas9 and co-expresses it with both a target-specific sgRNA and wild-type BirA52. The ligase specifically biotinylates the peptide fused to dCas9 for high-affinity streptavidin-mediated capture and MS analysis.Originally developed to facilitate53. Co-expression of this fusion protein with a specific sgRNA in the presence of biotin leads to the biotinylation of proteins in close proximity to the targeted gene locus. The system was tested by probing repetitive telomeric, major satellite, and minor satellite DNA regions.Although the crosslinking step in these AP/MS-based approaches helps to stabilize chromatin-associated protein complexes for purification, the increased sensitivity and more efficient extraction and AP strategies offered by proximity labeling approaches inspired the combination of BioID and APEX with CRISPR/dCas9-based strategies . CasID e54. The GLoPro (genomic locus proteomics) technique, which uses a dCas9-APEX2 fusion protein called CASPEX . The authors note the caveat that identification only confirms association with a locus, not locus specificity ; however, future bioinformatic analysis of large collections of single loci datasets will help to identify patterns and define specificities.In a similar approach, dCas9 has been fused to APEX2. C-BERST  was tested by targeting dCas9-APEX2 to telomeric and centromeric regions, finding good agreement with results from previous BioID and PICh experiments (plus unique hits) CASPEX , was app57 and should be equally applicable in mammalian cells. This would also facilitate the tagging of target proteins for direct analysis in mouse models. An alternative approach (particularly if there are concerns that the tag is interfering with protein function) would be to genetically fuse a biotin acceptor tag to an endogenous protein and virally infect a wild-type BirA expression plasmid to promote its specific biotinylation.In the future, it will be interesting to extend the dCas9-based proximity labeling strategies to the dynamic analysis of gene loci during development or in response to perturbations that affect their transcriptional regulation. Given that the functional CRISPR/Cas9 gene editing system is now routinely used to fuse affinity tags and fluorophores directly to endogenous proteins, it will likely be employed in a similar fashion to introduce biotin ligase or APEX (provided that the constitutive presence/position of the enzyme tag does not affect the protein’s function). This has already been demonstrated in protozoain vivo expression of nanobodies . Because they can be genetically encoded, nanobodies have already been expressed in live cells fused to fluorophores to enable live imaging of endogenous proteins. Their small size compared with that of conventional antibodies (about 15 kDa versus about 150 kDa) places the fluorophore closer to the target protein, which offers a much higher spatial resolution58. A recent study fused secondary nanobodies (anti-mouse IgG and anti-rabbit IgG) to APEX2 to assess their applicability forin vitro enzyme-linked immunosorbent assays (via oxidation of the Amplex Red Ultra substrate)59. The fusions expressed well in bacteria and the recombinant enzyme was active, suggesting that this approach may be applicable to cell-based systems. Although the generation and validation of primary nanobodies can be expensive and laborious, recent advances in yeast surface display of synthetic libraries60 and an expanding range of commercial services offer alternatives to buying a llama.Another area of research that would pair well with proximity labeling is


Additionally, comprehensive optical and sensing characterization of the laser-printed bimetallic surface structures allows substantiating the convenient spectroscopy-free RGB color analysis as a valuable tool for predictive assessment of the plasmonic properties of the various irregularly and quasi-periodically nanotextured surfaces.Surface-enhanced spectroscopy (SES) techniques, including surface-enhanced photoluminescence (SEPL), Raman scattering (SERS) and infrared absorption (SEIRA), represent powerful biosensing modalities, allowing non-invasive label-free identification of various molecules and quantum emitters in the vicinity of nanotextured surfaces. Enhancement of multi-wavelength (vis-IR) excitation of analyte molecules of interest atop a single textured substrate could pave the way toward ultimate chemosensing performance and further widespread implementation of the SES-based approaches in various crucial areas, such as point-ofcare diagnostics. In this paper, an easy-to-implement ultrafast direct laser printing via partial spallation of thermally-thick silver films and subsequent large-scale magnetron deposition of nanometer-thick Au layers of variable thickness was implemented to produce bimetallic textured surfaces with the cascaded nanotopography. The produced bimetallic textures demonstrate the strong broadband plasmonic response over the entire visible spectral range. Such plasmonic performance was confirmed by convenient spectroscopy-free Red-Green-Blue (RGB) color analysis of the dark-field (DF) scattering images supported by numerical calculations of the electromagnetic (EM) “near-fields”, as well as comprehensive DF spectroscopic characterization. Bimetallic laser-printed nanotextures, which can be easily printed at ultrafast (square millimeters per second) rate, using galvanometric scanning, exhibited strong enhancement of the SEPL (up to 75-fold) and SERS (up to 10 Development of surface structures and nanotextured surfaces supporting plasmon-mediated EM-field enhancement in a broad spectral range, covering whole visible spectrum, as well as its near infrared (near-IR) part, is of mandatory importance for various crucial applications4. In particular, such textured surfaces with superbroadband plasmonic response can be used to build novel high-performance optoelectronic devices6, solar-cell elements7, as well as to develop versatile and flexible biosensing platforms based on various surface-enhanced spectroscopy (SES) processes - surface-enhanced Raman scattering (SERS), photoluminescence (SEPL) and infrared absorption (SEIRA)11.Surface plasmons, resonant oscillations of the electron-ion plasma in the noble-metal nanostructures induced by irradiating photons, are known to provide substantial augmentation of the electromagnetic (EM) field localized near interfaces of the metals with their dielectric environment13 ensuring extremely fast material processing at the speed, reaching several cm2 per second. Several laser-based approaches were efficiently implemented to obtained various nanopatterned surfaces, exhibiting strong plasmonic response. Such approaches include the formation of the laser-induced periodic surface structures14, direct surface texturing via ablative phenomena19, deposition of the nanoparticles resulting from laser-induced spallation of bulk targets21, laser-induced forward transfer24, etc. However, even though good biosensing performance was reported for certain types of laser-fabricated textures, such surfaces exhibit either a low density of the plasmon-related hot spots per unit area or low spot-to-spot reproducibility of the measured signals. Moreover, none of them were certified or optimized so far to exhibit broadband plasmonic response over the whole visible spectral range. Noteworthy, the ability to harness multi-wavelength excitation within a single substrate, which can be produced in a controlled manner using some inexpensive fabrication technology, paves the way toward achieving ultimate biosensing performance and further widespread implementation of the SES-based approaches in various crucial areas as point-of-care diagnostics.As routine SES-based biosensing experiments require expendable nanotextured substrates for various analyte types to provide accurate measuring information, there is a great demand for high-performance economically justified strategies for such substrate fabrication. In the past decade, direct processing of different noble-metal materials using short- and ultrashort laser pulses has become a mature technology, allowing rapid fabrication of various nanotextured morphologies. Currently, pulse repetition rate of commercially available laser systems used in such experiments enters sub-GHz range6) yields from Rhodamine 6G (R6G) organic dye molecules photo-excited at different wavelengths. The spectrally broadband and rather strong plasmonic response of the bimetallic spiky textures, which can be easily printed at high (square millimeter per second) rate using the galvanometric scanning techniques, make the proposed approach promising for various routing biosensing applications.Here, we propose an easy-to-implement double-step approach for fabrication of bimetallic surfaces with cascaded nanotopography, exhibiting strong and broadband plasmonic response. The proposed approach is based on the direct ultrafast femtosecond (fs) laser spallation of the Ag-film surface followed by the deposition of the nanometer-thick layer of the other plasmonic metals, such as Au, Pt or Al. The direct fs-laser ablation of the Ag film by moderately focused pulses produces the surface craters uniformly covered by self-organized nanospikes, while their non-uniform coverage with the nanometer-thick layer of the other noble-metal material produces the cascaded bimetallic nanotopography. The resulting bimetallic textures demonstrate strong broadband plasmonic response over the whole visible spectral range, which is confirmed by supporting calculations of the EM “near fields” as well as comprehensive optical characterization by means of dark-field (DF) scattering microspectroscopy and accompanying RGB color analysis of the DF scattering images. The strong plasmonic response of the bimetallic laser-printed nanotextures is shown to provide considerable enhancement of the corresponding SEPL (up to 75-fold) and SERS (up to 10−1. The fabricated films were then textured by second-harmonic (λ = 515 nm) fs (≈300 fs) pulses generated by a fiber laser system , supporting MHz-level pulse repetition rate. The laser pulses were focused in the air with microscope objectives of different numerical apertures NA = 0.13, 0.3, 0.65, 0.95, allowing to vary gradually the diameter of laser irradiated spot in the range from 18.4 to 0.35 μm2, respectively. For the certain experiments, the linearly-polarized Gaussian-shaped output laser beam was converted into donut-shaped one, using a commercial radial polarization converter . Laser fabrication procedure was performed by scanning the sample surface with the laser beam. To do this, either the translation of the sample with a precise 3D nanopositioning platform (Newport XM and GTS series) or the ultrafast beam scanning with a galvanometric scanner  synchronized to the laser system, were performed27.The 500-nm thick Ag films were first deposited onto pre-cleaned silica glass substrates using a magnetron sputtering procedure (Kurt Lesker) at sputtering rate of 50 nm⋅min−1. The sample holder was rotated at a constant speed of 25 rpm to ensure uniform deposition. The morphology of the laser-textured surface spots before and after the deposition of the capping Au films was characterized by high-resolution scanning electron microscopy . Additionally, several other materials, such as Pt and Al, were also coated as thin films above the Ag textures to check their deposition and coverage features  by s-polarized white-light radiation from a stabilized calibrated tungsten bulb . The DF backscattered images were collected by means of two long-focal-distance lenses with NA = 0.42 and 0.8 to provide variable collection angle of the backscattered radiation. The DF images were captured by a charge-coupled device (CCD) camera . The corresponding back-scattering spectra were measured, using a home-built confocal system, comprising the abovementioned objectives and a grating-type spectrometer  equipped with a sensitive thermoelectrically-cooled camera  to analyze the spectrum. One adjustable pinhole was used to tune the acquisition area size.To perform RGB color analysis, the recorded DF backscattered images were analyzed and decomposed into the corresponding color channels, using ImageJ software (NIH). An IR-cutting filter (Thorlabs GS800) was inserted into the optical scheme to block near-IR radiation in the DF images.−1 for the 50 randomly chosen areas patterned under the same laser-fabrication conditions. The signal acquisition area was adjusted using a knife-edge aperture placed in the image plane. The reflection from the bulk silver substrate was used for calibration. For the textured areas printed at various laser spot sizes, the actual size of the patterned vs unmodified areas was taken into account.Additionally, the optical properties of the fabricated textured surfaces in the near-IR spectral region were probed using a Fourier-transform IR spectrometer  coupled to an infrared microscope . All measurements were performed in the reflection mode with a Cassegrain lens (NA = 0.5) by averaging 1500 scans at the spectral resolution of 8 cm3, while the perfectly matched layers were used to limit the computation volume. The dielectric functions of Ag and Au materials were modeled with a built-in software, fitting the tabulated reference data from28.Finite-difference time-domain calculations  were undertaken to assess approximate enhancement of the EM field amplitude near the isolated spikes. Taking into account the real nanotopography of the spallative crater, we considered a single silver nanospike with its height of 300 nm, diameter of 60 nm and a round tip of 30-nm curvature radius. High-resolution SEM images of the isolated spikes were used to further model the non-uniform coverage of the Ag spike with the Au capping layer of the equivalent thickness of 25, 50 and 100 nm. The modeled structures were excited with a linearly-polarized Gaussian source and wavelengths of 405, 532 and 632 nm. The size of the elementary Yee cell was fixed at 1 × 1 × 1 nm−6 M R6G ethanol solution for 1 h, then rinsed in the distilled water and dried by pressurized gaseous nitrogen. SEPL signal from the R6G layer covering the textured and smooth areas of the sample was first analyzed with a confocal laser scanning microscope  equipped with a 20x dry lens (Plan-Apochromat 20x/0.8 M27) and an Airyscan detection unit29. The microscope allows switching between four diode laser sources, emitting at the wavelengths centered at 405, 488, 561 and 630 nm. Measurements were performed in the Airyscan mode with a scanning area of 50 × 50 μm2 providing the nominal pixel size of 70 nm. Depending on the wavelength of the used laser source, the excitation intensity was adjusted to maintain the reasonable signal-to-noise ratio and account for the spectrally dependent excitation efficiency of R6G molecules  focused onto the sample surface by a 0.42-NA lens. The size of the laser spot in the focal plane was adjusted with a beam expander to fit the diameter of the isolated self-organized spallative texture. To compare reproducible SEPL yields, the PL spectra obtained on the mono- and bimetallic textured surfaces as well as on the smooth Ag film and glass slide were averaged over 50 similar spectra taken from the randomly chosen spallative textures. Specifically, for SERS measurements, the laser radiation was focused with a dry lens (NA = 0.8) and then similar averaging procedure was performed.2 .Spiky nanostructures almost uniformly distributed inside the crater appear on Ag film surface upon single-shot fs-pulse irradiation at pulse energy slightly above the spallation threshold ≈1.4 ± 0.2 J/cm2 Fig. . The bouMore pronounced nanotopography can be obtained using magnetron deposition of the thin metal film above the laser-printed spallative structures as illustrated by the series of the highly magnified SEM images Fig. . As one 33. The average density of spiky features per μm2 versus the objective lens NA is summarized in the Fig. The nanoscale topography of the produced bimetallic surface features generally depends on the thickness of the capping Au layer. Alternatively, the variation of the magnetron deposition rate is found to weakly affect the resulting nanoroughness. In this way, it is important to carefully optimize the first fabrication step  in terms of the maximal density of the spiky structures maintaining, at the same time, their uniform distribution. As the spallation process proceeds on the surface of the thermally thick film, its thickness does not affect the resulting nanotopography. In this way, we performed the systematic studies of the effect of the surface nanotopography on the focusing conditions of the laser radiation controlled by the NA of the objective lens (see Methods for details). For all focusing conditions, we considered the fluence slightly above the spallation threshold which was found to provide rather uniform nanotextured surfaces of the spallative craters optimal in terms of biosensing applicationsλ(NA)−2 mitigating the sample alignment requirements. Additionally, such loose focusing conditions allow utilization of the ultrafast laser beam scanning with a galvanometric scanning system to take advantage of the MHz repetition rate of the fs laser system used in our experiments (see Methods for details). The example of the large-scale array fabricated for only 2 seconds at a printing rate of 105 craters per second is presented in Fig. Utilization of the moderate-NA focusing conditions is beneficial from the point of view of rapid laser printing. specifically, such conditions provide the longer focal depth ≈16. Meanwhile, in terms of achieving the commercially competitive fabrication rates, implementation of such an approach requires substantially longer fabrication cycle per individual element as well as the ordered array containing millions of such elements. In this way, the single-pulse printing with the unusual laser beams providing the specific intensity (temperature) distribution on the metal film surface can be considered as a more efficient way toward controllable tuning of the surface nanotopography. In this study, we utilized a donut-shape beam34  to produce the spallative patterns on the surface of the similar 500-nm thick Ag film  ratio substantiating their polarization-dependent plasmonic response16. Here, for RGB-color analysis we used the side illumination with the s-polarized radiation (see Methods) to predominantly excite the radial oscillations of the electron plasma in the direction which is perpendicular to the major axis of the nanotips. Considering the size and the aspect ratio of the nanospikes, such excitation is expected to be more efficient in the visible spectral range , in agreement with our previous experimental observations16. At the same time, similar radial electron plasma oscillation will be excited upon normal irradiation of the surface textures, which will be implemented further to demonstrate biosensing properties of the produced surfaces.Recently, we proposed the polarization-resolved RGB color analysis of the plasmon-mediated scattering as an efficient and easy-to-implement method allowing to probe plasmonic response of the chaotically textured surfacesThe change of the scattering color on the recorded DF optical images observed for the arrays of the spallative textures upon increasing of the thickness of the capping Au layer clearly indicates the tuning of the corresponding plasmonic response Fig. . By deco35. In this way, for the fixed excitation conditions we measured the scattering intensity from the bare and Au-decorated laser-textured Ag surfaces to compare qualitatively their intensity and density of the electromagnetic hot spots. The strongest scattering over the whole visible spectral range is observed for the Ag spallative craters capped with the 25-nm thick Au layer . The calculation results for the excitation wavelengths of 405, 532 and 632 nm and the excitation of the nanospike from the top are summarized in Fig. μm2) ensures the plasmonic inter-coupling between neighboring structures. Such coupling is expected to work even under excitation with a tightly focused laser radiation  as well as is expected to increase the overall EM enhancement inside the crater.As the nanospikes can be characterized by the broad deviation of the size parameters even within a single spallative crater, the performed FDTD analysis is rather approximate in terms of assessment of the maximal EM enhancement. By performing similar calculations for different sizes of the Ag tip, one can get different geometry-dependent values of the maximal enhancement of the squared EM-field amplitude, besides the previous general trend demonstrating the broadband performance of the bimetallic Ag-Au nanospikes remains the same. Here, owing to limitation of the computation resources for fine mesh simulations, the EM calculations were performed for the isolated nanospike. Meanwhile, their dense arrangement within the spallative crater .The confocal PL images of the R6G nanometer-thick layer covering isolated mono- and bimetallic craters are presented in Fig. 3 counts (s⋅mW)−1 observed for bimetallic Ag-Au textures with the 25-nm thick Au capping layer  as the Raman yield scales with the EM-field amplitude in the produced “hot spots”. By accounting for the average intensity of the several R6G Raman bands at 612, 1360 and 1648 cm−1 we found more than an order of magnitude higher SERS yield for Ag-Au bimetallic textures being compared to the monometallic ones  yields for the organic dye molecules pumped at various excitation wavelengths, making the designed and demonstrated spectrally-broadband textures promising for routine biosensing applications. Additionally, comprehensive optical and biosensing characterization of the laser-printed bimetallic surface structures allows to substantiate the convinient spectroscopy-free RGB color analysis as a valuable tool for predictive assessment of the plasmonic properties of various chaotically and quasi-regularly nanotextured surfaces.Here, an easy-to-implement ultrafast direct laser printing via spallation of thermally thick silver films and subsequent large-scale magnetron deposition of nanometer-thick Au layer was implemented to produce bimetallic textured surfaces with the cascaded nanotopography. The produced bimetallic textures demonstrate the strong broadband plasmonic response over the whole visible spectral range, which was confirmed by our supporting calculations of the EM near-fields, as well as by comprehensive optical characterization via the DF scattering microspectroscopy and the RGB color analysis of the DF scattering images. The produced bimetallic laser-printed nanotextures were shown to provide enhancement of the SEPL (up to 75-fold) and SERS  as well as their multicomponent alloys. When combined with the subsequent deposition of the nanometer-thick plasmonic capping layer, ultrafast laser printing provides the novel platform to design nanotextured surfaces with complex surface distribution of the corresponding chemical elements. It is worth noting that utilization of the multi-metallic alloyed textures can be beneficial in terms of advanced nanotopography and efficient intermixing of the plasmonic responses from different materialsSupplementary information


Photo-receptors are widely present in both prokaryotic and eukaryotic cells, which serves as the foundation of tuning cell behaviors with light. While practices in eukaryotic cells have been relatively established, trials in bacterial cells have only been emerging in the past few years. A number of light sensors have been engineered in bacteria cells and most of them fall into the categories of two-component and one-component systems. Such a sensor toolbox has enabled practices in controlling synthetic circuits at the level of transcription and protein activity which is a major topic in synthetic biology, according to the central dogma. Additionally, engineered light sensors and practices of tuning synthetic circuits have served as a foundation for achieving light based real-time feedback control. Here, we review programming bacteria cells with light, introducing engineered light sensors in bacteria and their applications, including tuning synthetic circuits and achieving feedback controls over microbial cell culture. Synthetic biology aims to rationally design cell functions. A major aspect of synthetic biology is to explore natural and engineer new parts to be assembled into biological circuits for programmed cell behaviors  family proteins.A variety of bacterial and eukaryotic light sensors have evolved to sense ultraviolet, blue, green, red, and near-infrared signals  domain, a GAF  domain, and a PHY (Phytochrome-specific GAF-related) domain. The core architecture can be linked to functional domains like Histidine kinase (HK) to become transmembrane sensors of two component systems. The PHY domain incorporates a bilin chromophore to sense red light by photoisomerization  DNA binding domains evolutionally. A variety of methods including light-induced uncaging, tiltering, and dimerization are adopted to control the fused functional domains  or cytosolic actuators (one-component system)  have been engineered in bacteria to sense ultraviolet (UirS/UirR)  domain  from t Figure   and Calcium and integrin binding 1 (CIB1) protein pair is isolated from the plant y Figure . The basy Figure . The PhySynthetic biological circuits can be controlled at multiple levels according to the central dogma  an adapted cell culture system; (b) a real-time quantification system; and (c) a computer-controlled light delivery system Figure  Milias-. The celCurrent algorithms fall into two categories: the proportional-integral (PI) algorithm , JJ (Figures 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.


A two tier mechanism mediates Hox collinearity. Besides the familiar collinear chromatin modification within each Hox cluster (nanocollinearity), there is also a macrocollinearity tier. Individual Hox clusters and individual cells are coordinated and synchronized to generate multiscale (macro and nano) collinearity in the early vertebrate embryo. Macro-collinearity is mediated by three non-cell autonomous Hox–Hox interactions. These mediate temporal collinearity in early NOM (non-organizer mesoderm), time space translation where temporal collinearity is translated to spatial collinearity along the early embryo’s main body axis and neural transformation, where Hox expression is copied monospecifically from NOM mesoderm to overlying neurectoderm in the late gastrula. Unlike nanocollinearity, which is Hox cluster restricted, axial macrocollinearity extends into the head and EAD domains, thus covering the whole embryonic anterior-posterior (A-P) axis. EAD: extreme anterior domain, the only A-P axial domain anterior to the head. The whole time space translation mechanism interacts with A-P signaling pathways via “decision points,” separating different domains on the axis. We show that this mechanism is mediated by three non-cell autonomous Hox–Hox interactions that have specific roles in generating the vertebrate body plan .A BMP -anti BMP dependent dorsoventral (D-V) mechanism generates the A-P pattern of the vertebrate main body axis by time space translation . Hox tem(1) A ventralized embryo makes no A-P axis. Just a mass of ventral tissue. Adding a Spemann organizer to a ventralized embryo causes it to make an A-P axis. One organizer: one axis, two organizers two axes etc. .(2) Adding a Spemann organizer to a ventralized embryo at different time intervals after the beginning of gastrulation causes rescue of different parts of the A-P axis. The earlier SO is added the bigger the axial section rescued and the more anteriorly it starts. All sections run to the tail. Adding the Spemann organizer anti BMP signal noggin protein to the blastocoel of a VE at different times after the beginning of gastrulation rescues different zones in the axis. Early on: head zones including Otx2., Later: Hoxd1 Later: Krox20, later still: multiple zones representing more and more posterior sections of the axis. The age of the organizer has no effect .(3) These results indicate an A-P timer in the ventralized embryo that imposes incrementally more posterior positional values at incrementally later times and a fixation mechanism, imposed by the Spemann organizer, that can stabilize the current positional value.(4) The timer is in NOM (non-organizer mesoderm). The classical literature shows that mesoderm sets up the A-P pattern by progressively transmitting more and more posterior information to neurectoderm (activation-transformation) . The litFigures 2, 3).(5) Hox genes are involved in determining A-P identity in the trunk. Evidence: Hox loss of function (LOF) and gain of function (GOF) phenotypes. Loss of function of a Drosophila Hox gene or vertebrate Hox paralog group (pg) dramatically changes identity of part of the axis. This effect is not necessarily restricted to a small part of the axis. For example: in Xenopus Hox1pg LOF. This downregulates all posterior Hox genes as well as Hox1. Hox1 is thus necessary for collinearity. Presumably due to interactions between Hox genes  ((6) Hox genes show collinearity: Congruence of spatial and temporal early expression sequences with genomic sequence in Hox clusters . In vertebrates, spatial collinearity is first seen in neurectoderm and dorsal paraxial mesoderm), from end gastrulation (St. 12 in Xenopus). Temporal collinearity is seen earlier . Changes in the Hox expression pattern during gastrulation indicate that the later spatially collinear pattern is generated from earlier temporal collinearity, as shown in astrula) .(7) Ventralized (UV) embryos have only NOM mesoderm (non-organizer) and have the time space translation timer and normal Hox temporal collinearity in their NOM. They have no stable Hox expression and no spatial collinearity. Their temporal collinearity is transient and later ventralized embryos have no Hox expression. Dorsalized (Li+) embryos have no NOM mesoderm (only SO) and no timer and no Hox expression (early or late). They have SO mesoderm and NE. Adding an organizer to a ventralized embryo generates an axial pattern of stable Hox expression. Spatial collinearity thus correlates 100% with the time space translation axial pattern., Temporal collinearity correlates 100% with the time space translation timer.in vivo situation. Embryonic ectoderm is the tissue from which neurectoderm is induced. The Spemnn organizer provides the signals that induce it . The Hox expressing NOM then induces Hox expression in neurectoderm ((8) In wrap recombinates, non-organizer mesoderm (NOM), Spemann organizer mesoderm and animal cap ectoderm (are required to enable expression of Hox genes. These markers are then most strongly expressed in the neurectoderm that develops in the recombinate. Neurectoderm but not embryonic ectoderm can express Hox genes. Omitting any of the above tissues blocks Hox expression. These recombinates mimic the ectoderm .(9) The (trunk limited) Hox TC sequence is part of a wider pan axial anti-BMP dependent timing sequence There is congruence between results on anterior TST  and our (A) Non-cell autonomous Hox autoregulation  mediates vertical transfer of positional information from dorsalized gastrula NOM mesoderm to overlying neurectoderm- neural transformation; part of the vertebrate axial patterning process . This is(B) Posterior Induction- Posterior Hox genes are collinearly induced by more anterior Hox genes- at least sometimes specifically by their anterior nearest neighbors ,b. This (C) Posterior Dominance – suppression of the expression and/or function of anterior Hox genes by more posterior ones and by more posterior Hox associated micro RNA’s ,b startsOther investigations have extensively shown the importance of the same Hox–Hox interactions as above in mediating both later developmental processes and Hox expression in teratocarcinoma cells .Figures 2, 3). Hox associated micro-RNA’s: Mir 10 and Mir 196 participate in the interactive process  in generating collinearities is evident because loss of function for an appropriate Hox gene or paralog group can cut off the A-P axis or a temporally collinear Hox sequence (at the anterior expression boundary of) the gene or group in question  and beca process . These aWhat, then, is the role of BMP in this mechanism? How can it promote temporal collinearity, while inhibiting spatial collinearity and neural transformation? Clearly, BMP promotes (possibly co-mediates) non-cell autonomous posterior induction. It inhibits posterior dominance and presumably vertical signaling related autoregulation. Perhaps BMP/anti-BMP can switch one Hox–Hox interaction to another. , in the early gastrula . It is aInteresting studies in the early chicken embryo show that ectopic expression of a Hox gene early in gastrulation causes the Hox expressing epiblast cells to modulate their ingression time-thus the time they enter the primitive streak and the position where they end up on the A-P axis as if these features were regulated by Hox expression . Thus, tFigure 4).Does Hox collinearity control apply only to Hox genes or does a similar mechanism also apply to determinants in the most anterior Hox free part of the axis? Hox genes are expressed and function in the axis from in the neck back to the tip of the tail. Other genes pattern the head and EAD (extreme anterior domain). The macro-level of Hox collinearity control is not restricted to Hox genes. Markers for different levels in the Hox free head domain are activated A to P, early to late sequentially by timed anti BMP pulses given at different times in the blastula-beginning gastrula stages before the earliest/most anterior Hox activation starts at mid gastrulation , FGFs (fibroblast growth factors) and Wnts (Wingless-Int growth factors). All of these molecules are well known signaling factors in embryogenesis. They and indeed the Hox genes themselves, could of course act independently of the mechanism above which is active in the very first stages of A-P patterning and they may thus later refine or remodel the primary pattern. On the other hand, they clearly do also play a part in relation to the time space translation mechanism above. Different A-P morphogens act in concert with interactions between Hox genes or between anterior determinants or between these and Hox genes to initiate and generate and delimit different axial domains  on the A-P axis. In at least some cases, they act at or from “decision points” , situateMany findings point to the importance of retinoids in regulating the head-neck boundary on the A-P axis. For example, retinoic acid, which inhibits development of the head, collinearly induces Hox genes and initiates the posterior axis in many vertebrate embryos and in embryos of the related chordate Amphioxus. In Amphioxus, retinoid gain and loss of function have been shown, respectively, to anteriorized and posteriorize expression of the entire collinear sequence of Amphi-Hox genes and retinoids are found to act via Amphi-Hox1 which has a loss of function phenotype closely similar to that of retinoid loss of function. These findings concern the cerebral vesicle (brain)-posterior CNS  boundary between Wnt 5 and Amphi-Hox1 in Amphioxus . Hox1 alIn Xenopus, Wnt8 which again is involved in turning off head and turning on neck  also actWhereas neck Hox genes are activated by retinoic acid and Wnt, thorax Hox genes are not, but are activated by FGF (which turns on thorax Hox genes) from a decision point between Hox5 and Hox6 . FGF actBesides these retinoid/Wnt and FGF mediated decision points, there are presumably others in the more anterior and posterior parts of the axis.There is also presumably a second way that Hox genes interact with morphogens in early A-P patterning. Namely that the morphogens act as co-regulators in mediating particular non-cell autonomous Hox–Hox interactions. We think that the BMP pathway cooperates with Hox genes to mediate posterior induction in at least certain parts (anterior-mid) of the axis Abdominal Hox genes (Hox 9–12) show collinear posteriorly increasing inhibition of Wnt signaling . This woThus, we think that BMP either co-mediates non-cell autonomy of posterior induction over the whole axis (above) or that it only does so in the parts of the axis where posterior induction is not mediated by anti-wnt. The findings and reasoning above present a case that Hox–Hox interactions cooperating with morphogen action are involved in the very first phase of vertebrate A-P patterning.The dominant (macro) collinearity tier and lesser (nano) collinearity tier have different domains of action. The macro mechanism extends into the head and EAD as well as through the trunk Hox expressing part of the axis. It thus covers the whole axis. The nano mechanism is specific to Hox clusters and is therefore presumably restricted to the Hox expressing part of the axis  and have a parallel mechanism).Macro collinearity operates and connects to axial patterning via Hox–Hox interactions. This tier is divided into axial domains via interactions at decision points with axial signaling pathways (see above and below). Most of this mechanism was detected and characterized initially in Xenopus. Head macrocollinearity was detected initially in zebrafish and confirmed in Xenopus  and confThese thoughts obviously pose many questions. Nonetheless, they rest on potentially significant recent developments concerning Hox collinearity and the bodyplan and perhaps provide a realistic framework for thinking about vertebrate axial patterning.The findings above lead to the conclusion that Hox–Hox interactions are active in the very first phases of vertebrate A-P patterning. We used timing expts to show that Hox posterior induction and autoregulation are active from the beginning of and posterior dominance from the end of gastrulation (stages 10.5 and 12 respectively in Xenopus) .These Hox–Hox interactions interact with signaling pathways that are known to be relevant for A-P patterning. One type of signaling pathway–Hox interaction is that a signaling pathway initiates a collinear sequence of Hox expression, starting at a specific decision point between two axial domains (as seen with retinoids at the head- neck boundary (CNS) and putatively with Wnt at the head-neck boundary (mesoderm) and putatively with FGF-cdx at the neck-thorax boundary.A second type of interaction is that a signaling pathway cooperates with Hox genes to enable a Hox induced non-cell autonomous interaction. This is true for the BMP pathway which is required for posterior induction among at least some (the more 3′), and possibly all. Hox genes. Conversely the evidence suggests that BMP inhibits (and anti BMP promotes) posterior dominance among the same Hox genes. On the other hand, there is evidence suggesting that anti Wnt may be involved in non-cell autonomous cell interactions among 5′posterior  Hox genes. This could be additionally to or in place of (anti-)BMP.In conclusion, the evidence above points to a two tier collinearity mechanism, that is the basis of the earliest phase of vertebrate A-P patterning (where the pattern is laid down). This acts in the anterior head and EAD parts of the axis as well as in the posterior Hox-expressing parts and cooperates with the known A-P signaling pathways which are concerned with defining the axial domains of the pattern.The author confirms being the sole contributor of this work and approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.