Molecular and cellular mechanisms of teneurin signaling in synaptic partner matching.

In developing brains, axons exhibit remarkable precision in selecting synaptic partners among many non-partner cells. Evolutionarily conserved teneurins are transmembrane proteins that instruct synaptic partner matching. However, how intracellular signaling pathways execute teneurins' functions is unclear. Here, we use in situ proximity labeling to obtain the intracellular interactome of a teneurin (Ten-m) in the Drosophila brain. Genetic interaction studies using quantitative partner matching assays in both olfactory receptor neurons (ORNs) and projection neurons (PNs) reveal a common pathway: Ten-m binds to and negatively regulates a RhoGAP, thus activating the Rac1 small GTPases to promote synaptic partner matching. Developmental analyses with single-axon resolution identify the cellular mechanism of synaptic partner matching: Ten-m signaling promotes local F-actin levels and stabilizes ORN axon branches that contact partner PN dendrites. Combining spatial proteomics and high-resolution phenotypic analyses, this study advanced our understanding of both cellular and molecular mechanisms of synaptic partner matching.

Statistical Detection of Relatives Typed with Disjoint Forensic and Biomedical Loci.

In familial searching in forensic genetics, a query DNA profile is tested against a database to determine whether it represents a relative of a database entrant. We examine the potential for using linkage disequilibrium to identify pairs of profiles as belonging to relatives when the query and database rely on nonoverlapping genetic markers. Considering data on individuals genotyped with both microsatellites used in forensic applications and genome-wide SNPs, we find that ∼30%-32% of parent-offspring pairs and ∼35%-36% of sib pairs can be identified from the SNPs of one member of the pair and the microsatellites of the other. The method suggests the possibility of performing familial searches of microsatellite databases using query SNP profiles, or vice versa. It also reveals that privacy concerns arising from computations across multiple databases that share no genetic markers in common entail risks, not only for database entrants, but for their close relatives as well.

Cryogenic electron microscopy and single-particle analysis.

About 20 years ago, the first three-dimensional (3D) reconstructions at subnanometer (<10-Å) resolution of an icosahedral virus assembly were obtained by cryogenic electron microscopy (cryo-EM) and single-particle analysis. Since then, thousands of structures have been determined to resolutions ranging from 30 Å to near atomic (<4 Å). Almost overnight, the recent development of direct electron detectors and the attendant improvement in analysis software have advanced the technology considerably. Near-atomic-resolution reconstructions can now be obtained, not only for megadalton macromolecular complexes or highly symmetrical assemblies but also for proteins of only a few hundred kilodaltons. We discuss the developments that led to this breakthrough in high-resolution structure determination by cryo-EM and point to challenges that lie ahead.

Chemical Principles in Tissue Clearing and Staining Protocols for Whole-Body Cell Profiling.

Mammalian bodies have more than a billion cells per cubic centimeter, which makes whole-body cell (WBC) profiling of an organism one of the ultimate challenges in biology and medicine. Recent advances in tissue-clearing technology have enabled rapid and comprehensive cellular analyses in whole organs and in the whole body by a combination of state-of-the-art technologies of optical imaging and image informatics. In this review, we focus mainly on the chemical principles in currently available techniques for tissue clearing and staining to facilitate our understanding of their underlying mechanisms. Tissue clearing is usually conducted by the following steps: (a) fixation, (b) permeabilization, (c) decolorizing, and (d) refractive index (RI) matching. To phenotype individual cells after tissue clearing, it is important to visualize genetically encoded fluorescent reporters and/or to stain tissues with fluorescent dyes, fluorescent labeled antibodies, or nucleic acid probes. Although some technical challenges remain, the chemical principles in tissue clearing and staining for WBC profiling will enable various applications, such as identifying cellular circuits across multiple organs and measuring their dynamics in stochastic and proliferative cellular processes, for example, autoimmune and malignant neoplastic diseases.

Irreversible coherent matching bonding of van der Waals heterostructure lattice by pressure.

The key of heterostructure is the combinations created by stacking various vdW materials, which can modify interlayer coupling and electronic properties, providing exciting opportunities for designer devices. However, this simple stacking does not create chemical bonds, making it difficult to fundamentally alter the electronic structure. Here, we demonstrate that interlayer interactions in heterostructures can be fundamentally controlled using hydrostatic pressure, providing a bonding method to modify electronic structures. By covering graphene with boron nitride and inducing an irreversible phase transition, the conditions for graphene lattice-matching bonding (IMB) were created. We demonstrate that the increased bandgap of graphene under pressure is well maintained in ambient due to the IMB in the interface. Comparison to theoretical modeling emphasizes the process of pressure-induced interfacial bonding, systematically generalizes, and predicts this model. Our results demonstrate that pressure can irreversibly control interlayer bonding, providing opportunities for high-pressure technology in ambient applications and IMB engineering in heterostructures.

Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes.

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.

Quantification Quality Control Emerges as a Crucial Factor to Enhance Single-Cell Proteomics Data Analysis.

Mass spectrometry (MS)-based single-cell proteomics (SCP) provides us the opportunity to unbiasedly explore biological variability within cells without the limitation of antibody availability. This field is rapidly developed with the main focuses on instrument advancement, sample preparation refinement, and signal boosting methods; however, the optimal data processing and analysis are rarely investigated which holds an arduous challenge because of the high proportion of missing values and batch effect. Here, we introduced a quantification quality control to intensify the identification of differentially expressed proteins (DEPs) by considering both within and across SCP data. Combining quantification quality control with isobaric matching between runs (IMBR) and PSM-level normalization, an additional 12% and 19% of proteins and peptides, with more than 90% of proteins/peptides containing valid values, were quantified. Clearly, quantification quality control was able to reduce quantification variations and q-values with the more apparent cell type separations. In addition, we found that PSM-level normalization performed similar to other protein-level normalizations but kept the original data profiles without the additional requirement of data manipulation. In proof of concept of our refined pipeline, six uniquely identified DEPs exhibiting varied fold-changes and playing critical roles for melanoma and monocyte functionalities were selected for validation using immunoblotting. Five out of six validated DEPs showed an identical trend with the SCP dataset, emphasizing the feasibility of combining the IMBR, cell quality control, and PSM-level normalization in SCP analysis, which is beneficial for future SCP studies.

Temperature-induced DNA density transition in phage λ capsid revealed with contrast-matching SANS.

Structural details of a genome packaged in a viral capsid are essential for understanding how the structural arrangement of a viral genome in a capsid controls its release dynamics during infection, which critically affects viral replication. We previously found a temperature-induced, solid-like to fluid-like mechanical transition of packaged λ-genome that leads to rapid DNA ejection. However, an understanding of the structural origin of this transition was lacking. Here, we use small-angle neutron scattering (SANS) to reveal the scattering form factor of dsDNA packaged in phage λ capsid by contrast matching the scattering signal from the viral capsid with deuterated buffer. We used small-angle X-ray scattering and cryoelectron microscopy reconstructions to determine the initial structural input parameters for intracapsid DNA, which allows accurate modeling of our SANS data. As result, we show a temperature-dependent density transition of intracapsid DNA occurring between two coexisting phases-a hexagonally ordered high-density DNA phase in the capsid periphery and a low-density, less-ordered DNA phase in the core. As the temperature is increased from 20 °C to 40 °C, we found that the core-DNA phase undergoes a density and volume transition close to the physiological temperature of infection (~37 °C). The transition yields a lower energy state of DNA in the capsid core due to lower density and reduced packing defects. This increases DNA mobility, which is required to initiate rapid genome ejection from the virus capsid into a host cell, causing infection. These data reconcile our earlier findings of mechanical DNA transition in phage.

Quantification of gallium cryo-FIB milling damage in biological lamellae.

Cryogenic electron microscopy (cryo-EM) can reveal the molecular details of biological processes in their native, cellular environment at atomic resolution. However, few cells are sufficiently thin to permit imaging with cryo-EM. Thinning of frozen cells to <500 nm lamellae by focused-ion-beam (FIB) milling has enabled visualization of cellular structures with cryo-EM. FIB milling represents a significant advance over prior approaches because of its ease of use, scalability, and lack of large-scale sample distortions. However, the amount of damage it causes to a thinned cell section has not yet been determined. We recently described an approach for detecting and identifying single molecules in cryo-EM images of cells using 2D template matching (2DTM). 2DTM is sensitive to small differences between a molecular model (template) and the detected structure (target). Here, we use 2DTM to demonstrate that under the standard conditions used for machining lamellae of biological samples, FIB milling introduces a layer of variable damage that extends to a depth of 60 nm from each lamella surface. This layer of damage limits the recovery of information for in situ structural biology. We find that the mechanism of FIB milling damage is distinct from radiation damage during cryo-EM imaging. By accounting for both electron scattering and FIB milling damage, we estimate that FIB milling damage with current protocols will negate the potential improvements from lamella thinning beyond 90 nm.

Improving forensic perpetrator identification with Super-Recognizers.

About a decade ago, Super-Recognizers (SRs) were first described as individuals with exceptional face identity processing abilities. Since then, various tests have been developed or adapted to assess individuals' abilities and identify SRs. The extant literature suggests that SRs may be beneficial in police tasks requiring individual identification. However, in reality, the performance of SRs has never been examined using authentic forensic material. This not only limits the external validity of test procedures used to identify SRs, but also claims concerning their deployment in policing. Here, we report the first-ever investigation of SRs' ability to identify perpetrators using authentic case material. We report the data of 73 SRs and 45 control participants. These include (a) performance on three challenging tests of face identity processing recommended by Ramon (2021) for SR identification; (b) performance for perpetrator identification using four CCTV sequences depicting five perpetrators and police line-ups created for criminal investigation purposes. Our findings demonstrate that the face identity processing tests used here are valid in measuring such abilities and identifying SRs. Moreover, SRs excel at perpetrator identification relative to control participants, with more correct perpetrator identifications, the better their performance across lab tests. These results provide external validity for the recently proposed diagnostic framework and its tests used for SR identification (Ramon, 2021). This study provides the first empirical evidence that SRs identified using these measures can be beneficial for forensic perpetrator identification. We discuss theoretical and practical implications for law enforcement, whose procedures can be improved via a human-centric approach centered around individuals with superior abilities.

Family secrets: Experiences and outcomes of participating in direct-to-consumer genetic relative-finder services.

In recent decades, genetic genealogy has become popular as a result of direct-to-consumer (DTC) genetic testing. Some DTC genetic testing companies offer genetic relative-finder (GRF) services that compare the DNA of consenting participants to identify genetic relatives among them and provide each participant a list of their relative matches. We surveyed a convenience sample of GRF service participants to understand the prevalence of discoveries and associated experiences. Almost half (46%) of the 23,196 respondents had participated in GRF services only for non-specific reasons that included interest in building family trees and general curiosity. However, most (82%) also learned the identity of at least one genetic relative. Separately, most respondents (61%) reported learning something new about themselves or their relatives, including potentially disruptive information such as that a person they believed to be their biological parent is in fact not or that they have a sibling they had not known about. Respondents generally reported that discovering this new information had a neutral or positive impact on their lives, and most had low regret regarding their decision to participate in GRF services. Yet some reported making life changes as a result of their discoveries. Compared to respondents making other types of discoveries, those who learned that they were donor conceived reported the highest decisional regret and represented the largest proportion reporting net-negative consequences for themselves. Our findings indicate that discoveries from GRF services may be common and that the consequences for individuals, while generally positive, can be far-reaching and complex.

Neurotransmitter Switching in the Developing and Adult Brain.

Neurotransmitter switching is the gain of one neurotransmitter and the loss of another in the same neuron in response to chronic stimulation. Neurotransmitter receptors on postsynaptic cells change to match the identity of the newly expressed neurotransmitter. Neurotransmitter switching often appears to change the sign of the synapse from excitatory to inhibitory or from inhibitory to excitatory. In these cases, neurotransmitter switching and receptor matching thus change the polarity of the circuit in which they take place. Neurotransmitter switching produces up or down reversals of behavior. It is also observed in response to disease. These findings raise the possibility that neurotransmitter switching contributes to depression, schizophrenia, and other illnesses. Many early discoveries of the single gain or loss of a neurotransmitter may have been harbingers of neurotransmitter switching.

An integrated deep learning framework for the interpretation of untargeted metabolomics data.

Untargeted metabolomics is gaining widespread applications. The key aspects of the data analysis include modeling complex activities of the metabolic network, selecting metabolites associated with clinical outcome and finding critical metabolic pathways to reveal biological mechanisms. One of the key roadblocks in data analysis is not well-addressed, which is the problem of matching uncertainty between data features and known metabolites. Given the limitations of the experimental technology, the identities of data features cannot be directly revealed in the data. The predominant approach for mapping features to metabolites is to match the mass-to-charge ratio (m/z) of data features to those derived from theoretical values of known metabolites. The relationship between features and metabolites is not one-to-one since some metabolites share molecular composition, and various adduct ions can be derived from the same metabolite. This matching uncertainty causes unreliable metabolite selection and functional analysis results. Here we introduce an integrated deep learning framework for metabolomics data that take matching uncertainty into consideration. The model is devised with a gradual sparsification neural network based on the known metabolic network and the annotation relationship between features and metabolites. This architecture characterizes metabolomics data and reflects the modular structure of biological system. Three goals can be achieved simultaneously without requiring much complex inference and additional assumptions: (1) evaluate metabolite importance, (2) infer feature-metabolite matching likelihood and (3) select disease sub-networks. When applied to a COVID metabolomics dataset and an aging mouse brain dataset, our method found metabolic sub-networks that were easily interpretable.

Qmatey: an automated pipeline for fast exact matching-based alignment and strain-level taxonomic binning and profiling of metagenomes.

Metagenomics is a powerful tool for understanding organismal interactions; however, classification, profiling and detection of interactions at the strain level remain challenging. We present an automated pipeline, quantitative metagenomic alignment and taxonomic exact matching (Qmatey), that performs a fast exact matching-based alignment and integration of taxonomic binning and profiling. It interrogates large databases without using metagenome-assembled genomes, curated pan-genes or k-mer spectra that limit resolution. Qmatey minimizes misclassification and maintains strain level resolution by using only diagnostic reads as shown in the analysis of amplicon, quantitative reduced representation and shotgun sequencing datasets. Using Qmatey to analyze shotgun data from a synthetic community with 35% of the 26 strains at low abundance (0.01-0.06%), we revealed a remarkable 85-96% strain recall and 92-100% species recall while maintaining 100% precision. Benchmarking revealed that the highly ranked Kraken2 and KrakenUniq tools identified 2-4 more taxa (92-100% recall) than Qmatey but produced 315-1752 false positive taxa and high penalty on precision (1-8%). The speed, accuracy and precision of the Qmatey pipeline positions it as a valuable tool for broad-spectrum profiling and for uncovering biologically relevant interactions.

IK-DDI: a novel framework based on instance position embedding and key external text for DDI extraction.

Determining drug-drug interactions (DDIs) is an important part of pharmacovigilance and has a vital impact on public health. Compared with drug trials, obtaining DDI information from scientific articles is a faster and lower cost but still a highly credible approach. However, current DDI text extraction methods consider the instances generated from articles to be independent and ignore the potential connections between different instances in the same article or sentence. Effective use of external text data could improve prediction accuracy, but existing methods cannot extract key information from external data accurately and reasonably, resulting in low utilization of external data. In this study, we propose a DDI extraction framework, instance position embedding and key external text for DDI (IK-DDI), which adopts instance position embedding and key external text to extract DDI information. The proposed framework integrates the article-level and sentence-level position information of the instances into the model to strengthen the connections between instances generated from the same article or sentence. Moreover, we introduce a comprehensive similarity-matching method that uses string and word sense similarity to improve the matching accuracy between the target drug and external text. Furthermore, the key sentence search method is used to obtain key information from external data. Therefore, IK-DDI can make full use of the connection between instances and the information contained in external text data to improve the efficiency of DDI extraction. Experimental results show that IK-DDI outperforms existing methods on both macro-averaged and micro-averaged metrics, which suggests our method provides complete framework that can be used to extract relationships between biomedical entities and process external text data.

Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities.

Human Phenotype Ontology (HPO)-based approaches have gained popularity in recent times as a tool for genomic diagnostics of rare diseases. However, these approaches do not make full use of the available information on disease and patient phenotypes. We present a new method called Phen2Disease, which utilizes the bidirectional maximum matching semantic similarity between two phenotype sets of patients and diseases to prioritize diseases and genes. Our comprehensive experiments have been conducted on six real data cohorts with 2051 cases (Cohort 1, n = 384; Cohort 2, n = 281; Cohort 3, n = 185; Cohort 4, n = 784; Cohort 5, n = 208; and Cohort 6, n = 209) and two simulated data cohorts with 1000 cases. The results of the experiments showed that Phen2Disease outperforms the three state-of-the-art methods when only phenotype information and HPO knowledge base are used, particularly in cohorts with fewer average numbers of HPO terms. We also observed that patients with higher information content scores have more specific information, leading to more accurate predictions. Moreover, Phen2Disease provides high interpretability with ranked diseases and patient HPO terms presented. Our method provides a novel approach to utilizing phenotype data for genomic diagnostics of rare diseases, with potential for clinical impact. Phen2Disease is freely available on GitHub at https://github.com/ZhuLab-Fudan/Phen2Disease.

Matching single cells across modalities with contrastive learning and optimal transport.

Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. Still, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality and multiple sources of acquisition noise. Computational methods able to match cells across different modalities offer an appealing alternative towards this goal. In this work, we propose MatchCLOT, a novel method for modality matching inspired by recent promising developments in contrastive learning and optimal transport. MatchCLOT uses contrastive learning to learn a common representation between two modalities and applies entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance on two curated benchmarking datasets and an independent test dataset, improving the top scoring method by 26.1% while preserving the underlying biological structure of the multimodal data. Importantly, MatchCLOT offers high gains in computational time and memory that, in contrast to existing methods, allows it to scale well with the number of cells. As single-cell datasets become increasingly large, MatchCLOT offers an accurate and efficient solution to the problem of modality matching.

Feasibility and impact of haplogroup matching for mitochondrial replacement treatment.

Mitochondrial replacement technology (MRT) aims to reduce the risk of serious disease in children born to women who carry pathogenic mitochondrial DNA (mtDNA) variants. By transplanting nuclear genomes from eggs of an affected woman to enucleated eggs from an unaffected donor, MRT creates new combinations of nuclear and mtDNA. Based on sets of shared sequence variants, mtDNA is classified into ~30 haplogroups. Haplogroup matching between egg donors and women undergoing MRT has been proposed as a means of reducing mtDNA sequence divergence between them. Here we investigate the potential effect of mtDNA haplogroup matching on clinical delivery of MRT and on mtDNA sequence divergence between donor/recipient pairs. Our findings indicate that haplogroup matching would limit the availability of egg donors such that women belonging to rare haplogroups may have to wait > 4 years for treatment. Moreover, we find that intra-haplogroup sequence variation is frequently within the range observed between randomly matched mtDNA pairs. We conclude that haplogroup matching would restrict the availability of MRT, without necessarily reducing mtDNA sequence divergence between donor/recipient pairs.

Nontrivial phase matching in helielectric polarization helices: Universal phase matching theory, validation, and electric switching.

Second-order optical nonlinearity is the essential concept for realizing modern technologies of optical wavelength conversion. The emerging helical polarization fluid, dubbed helielectric nematic, now makes it possible to design and easily fabricate various polarization structures and control their optical responses. The matter family is demonstrated as an ideal liquid platform for nonlinear optical conversion and amplification with electric-reconfigurable tunability. We here develop a universal phase matching theory and reveal a nonclassic chirality-sensitive phase-matching condition in the polarization helices through both the numerical calculation and the experimental validations. The nonlinear optical amplification can be dramatically modulated with a contrast ratio of >100:1 by an in-plane electric field. Furthermore, we employ the director relaxation under electric fields coupled with nonlinear optical simulation to clarify the topology-light interactions.

Matching protein surface structural patches for high-resolution blind peptide docking.

Peptide docking can be perceived as a subproblem of protein­protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about peptide sequences to generate representative conformer ensembles, which can then be rigid-body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely, that the protein surface defines the peptide-bound conformation. Here, we present PatchMAN (Patch-Motif AligNments), a global peptide-docking approach that uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a nonredundant set of protein­peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide­protein complexes in 58%/84% within 2.5 Å/5 Å interface backbone RMSD, with corresponding sampling in 81%/100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the bound peptide conformation is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide­protein association, and to the design of new peptide binders. PatchMAN is available as a server at https://furmanlab.cs.huji.ac.il/patchman/.