Biology subjects

Ye, X.

Publications and source records attributed to Ye, X..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

PGSFusion streamlines polygenic score construction and epidemiological applications in biobank-scale cohorts

BackgroundThe polygenic score (PGS) is an estimate of an individuals genetic susceptibility to a specific complex trait and has been instrumental to the development of precision medicine. Clinically, the simplest form of PGS, which is calculated as a weighted sum of variant counts, has been widely applied to conduct disease risk classification. Unfortunately, despite the critical importance of PGS, there are few online resources available to biologists and epidemiologists to calculate PGS in a user-friendly manner. ResultsTo address this need, we have developed a web server, PGSFusion, that streamlines the construction of PGS using a large variety of methods targeting different epidemiological requirements. PGSFusion included 16 PGS methods in four categories, which are single-trait, annotation-based, multiple-trait, and cross-ancestry. In addition, PGSFusion also utilizes UK Biobank data to provide two kinds of in-depth analyses: i) prediction performance evaluation to display the consistency between PGS and specific traits and the effect size of PGS in different genetic risk groups; ii) joint effect analysis to investigate the interaction between PGS and covariates, as well as the genetic effect size in different subgroups of covariates. PGSFusion automatically identifies the required information in uploaded summary statistics files, provides a selection of suitable methods, and outputs calculated PGSs and their corresponding epidemiological results, all without requiring prior programming knowledge. To demonstrate the function of PGSFusion, we showcase three case studies in different application scenario, highlighting its versatility and values to researchers. ConclusionsOverall, PGSFusion presents an easy-to-use, effective, and extensible platform for PGS construction, promoting the accessibility and utility of PGS for researchers in the field of precision medicine.

bioinformatics↗

bioRxiv2024

Reinforcement learning guides single-cell sequencing in decoding lineage and cell fate decisions

The rapid development of single-cell sequencing offers an unparalleled opportunity to delineate the heterogeneity of individual cells. However, current methods struggle to pinpoint the states of cell fate decisions. In this study, we introduce a novel approach called Single-cell Reinforcement Learning (scRL), which integrates reinforcement learning into single-cell data analysis using an actor-critic architecture. Among existing dimensionality reduction methods, we identified one with the best interpretability. Based on this latent space and combined with our scRL algorithm, we assessed the intensity of fate decisions at the single-cell level. Extensive evaluations demonstrate that scRL outperforms existing techniques, as well as their variants and alternative approaches, in assessing cell fate decisions. Moreover, scRL offers an alternative method for evaluating the intensity of cell lineage differentiation which shows competitive interpretability as well. The superiority of scRL in assessing fate decisions is confirmed across several types of single-cell datasets.

bioinformatics↗

bioRxiv2024

Large-scale genome analyses provide insights into Hymenoptera evolution

The order Hymenoptera includes a large number of species with diverse lifestyles and is known for its significant contributions to natural ecosystems. To better understand the evolution of this diverse order, we performed large-scale comparative genomics on 131 species from 13 superfamilies, covering most representative groups. We used these genomes to reveal an overall pattern of genomic change in terms of gene content and evolutionary rate throughout hymenopteran history. We identified genes that possibly contributed to the evolution of several key innovations, such as parasitoidism, wasp-waist, sting, and secondary phytophagy. We also discovered the distinct genomic trajectories between the clade containing major parasitoid wasps (Parasitoida) and stinging species (Aculeata) since their divergence, which are involved in many aspects of genomic change, such as rapidly evolving gene families, gene gain and loss, and metabolic pathway evolution. In addition, we explored the genomic features accompanying the three independent evolution of secondary phytophagy. Our work provides insights for understanding genome evolution and the genomic basis of diversification in Hymenoptera.

genomics↗

bioRxiv2024

Role of ACSBG1 in brain lipid metabolism and X-linked adrenoleukodystrophy pathogenesis: Insights from a knockout mouse model

The "bubblegum" acyl-CoA synthetase (ACSBG1) is a pivotal player in lipid metabolism during the development of the mouse brain, facilitating the activation of long-chain fatty acids (LCFAs) and their integration into essential lipid species crucial for brain function. Through its enzymatic activity, ACSBG1 converts LCFAs into acyl-CoA derivatives, supporting vital processes like membrane formation, myelination, and energy production. Its regulatory role significantly influences neuronal growth, synaptic plasticity, and overall brain development, highlighting its importance in maintaining lipid homeostasis and proper brain function. Originally discovered in the fruit fly brain, ACSBG1 attracted attention for its potential implication in X-linked adrenoleukodystrophy (XALD) pathogenesis. Studies using Drosophila melanogaster lacking the ACSBG1 homolog, bubblegum, revealed adult neurodegeneration with elevated levels of very long-chain fatty acids (VLCFA). To explore ACSBG1s role in fatty acid (FA) metabolism and its relevance to XALD, we created an ACSBG1 knockout (Acsbg1-/-) mouse model and examined its impact on lipid metabolism during mouse brain development. Phenotypically, Acsbg1-/- mice resembled wild type (w.t.) mice. Despite its primary expression in tissues affected by XALD, brain, adrenal gland and testis, ACSBG1 depletion did not significantly reduce total ACS enzyme activity in these tissues when using LCFA or VLCFA as substrates. However, analysis unveiled intriguing developmental and compositional changes in FA levels associated with ACSBG1 deficiency. In the adult mouse brain, ACSBG1 expression peaked in the cerebellum, with lower levels observed in other brain regions. Developmentally, ACSBG1 expression in the cerebellum was initially low during the first week of life but increased dramatically thereafter. Cerebellar FA levels were assessed in both w.t. and Acsbg1-/- mouse brains throughout development, revealing notable differences. While saturated VLCFA levels were typically high in XALD tissues and in fruit flies lacking ACSBG1, cerebella from Acsbg1-/- mice displayed lower saturated VLCFA levels, especially after about 8 days of age. Additionally, monounsaturated {omega}9 FA levels exhibited a similar trend as saturated VLCFA, {omega}3 polyunsaturated FA levels werewhile elevated in Acsbg1-/- mice. Further analysis of specific FA levels provided additional insights into potential roles for ACSBG1. Notably, the decreased VLCFA levels in Acsbg1-/- mice primarily stemmed from changes in C24:0 and C26:0, while reduced {omega}9 FA levels were mainly observed in C18:1 and C24:1. ACSBG1 depletion had minimal effects on saturated long-chain FA or {omega}6 polyunsaturated FA levels but led to significant increases in specific {omega}3 FA, such as C20:5 and C22:5. Moreover, the impact of ACSBG1 deficiency on the developmental expression of several cerebellar FA metabolism enzymes, including those required for synthesis of {omega}3 polyunsaturated FA, was assessed; these FA can potentially be converted into bioactive signaling molecules like eicosanoids and docosanoids. In conclusion, despite compelling circumstantial evidence, it is unlikely that ACSBG1 directly contributes to the pathology of XALD. Instead, the effects of ACSBG1 knockout on processes regulated by eicosanoids and/or docosanoids should be further investigated.

biochemistry↗

bioRxiv2024

Transformer-based modeling of Clonal Selection and Expression Dynamics (TraCSED) reveals resistance signatures in breast cancer

Understanding transcriptional heterogeneity in cancer cells and its implication for treatment response is critical to identify how resistance occurs and may be targeted. Such heterogeneity can be captured by in vitro studies through clonal barcoding methods. We present TraCSED (Transformer-based modeling of Clonal Selection and Expression Dynamics), a dynamic deep learning approach for modeling clonal selection. Using single-cell gene expression and the fitness of barcoded clones, TraCSED identifies interpretable gene programs and the timepoints at which they are associated with clonal selection. When applied to cells treated with either giredestrant, an estrogen receptor (ER) antagonist and degrader, or palbociclib, a CDK4/6 inhibitor, time-dependent resistance pathways are revealed. For example, ER activity is associated with positive selection around day four under palbociclib treatment and this adaptive response can be suppressed by combining the drugs. Yet, in the combination treatment, one clone still emerged. Clustering based on partial least squares regression found that high baseline expression of both SNHG25 and SNCG genes was the primary marker of positive selection to co-treatment and thus potentially associated with innate resistance - an aspect that traditional differential analysis methods missed. In conclusion, TraCSED enables associating pathways with phenotypes in a time-dependent manner from scRNA-seq data.

cancer biology↗

bioRxiv2024

Protein Editing using a Concerted Transposition Reaction

Protein engineering through the chemical or enzymatic ligation of polypeptide fragments has proven enormously powerful for studying countless biochemical processes in vitro. In general, this strategy necessitates a protein folding step following ligation of the unstructured fragments, a requirement that constrains the types of systems amenable to the approach. Here, we report an in vitro strategy that allows internal regions of target proteins to be replaced in a single operation. Conceptually, our system is analogous to a DNA transposition reaction, but employs orthogonal pairs of split inteins to swap out a designated region of a host protein with an exogenous molecular cassette. We show using isotopic labeling experiments that this protein transposition reaction is concerted when the kinetics for the embedded intein pairs are suitably matched. Critically, this feature allows for efficient manipulation of protein primary structure in the context of a native fold. The utility of this method is illustrated using several protein systems including the multi-subunit chromatin remodeling complex, ACF, where we also show protein transposition can occur in situ within the cell nucleus. By carrying out a molecular cut and paste on a protein or protein complex under native folding conditions, our approach dramatically expands the scope of protein semisynthesis.

biochemistry↗

bioRxiv2024

DeepVID v2: Self-Supervised Denoising with Decoupled Spatiotemporal Enhancement for Low-Photon Voltage Imaging

SignificanceVoltage imaging is a powerful tool for studying the dynamics of neuronal activities in the brain. However, voltage imaging data are fundamentally corrupted by severe Poisson noise in the low-photon regime, which hinders the accurate extraction of neuronal activities. Self-supervised deep learning denoising methods have shown great potential in addressing the challenges in low-photon voltage imaging without the need for ground truth, but usually suffer from the tradeoff between spatial and temporal performance. AimWe present DeepVID v2, a novel self-supervised denoising framework with decoupled spatial and temporal enhancement capability to significantly augment low-photon voltage imaging. ApproachDeepVID v2 is built on our original DeepVID framework,1,2 which performs frame-based denoising by utilizing a sequence of frames around the central frame targeted for denoising to leverage temporal information and ensure consistency. The network further integrates multiple blind pixels in the central frame to enrich the learning of local spatial information. Additionally, DeepVID v2 introduces a new edge extraction branch to capture fine structural details in order to learn high spatial resolution information. ResultsWe demonstrate that DeepVID v2 is able to overcome the tradeoff between spatial and temporal performance, and achieve superior denoising capability in resolving both high-resolution spatial structures and rapid temporal neuronal activities. We further show that DeepVID v2 is able to generalize to different imaging conditions, including time-series measurements with various signal-to-noise ratios (SNRs) and in extreme low-photon conditions. ConclusionsOur results underscore DeepVID v2 as a promising tool for enhancing voltage imaging. This framework has the potential to generalize to other low-photon imaging modalities and greatly facilitate the study of neuronal activities in the brain.

neuroscience↗

bioRxiv2024

Lipid availability influences ferroptosis sensitivity in cancer cells by regulating polyunsaturated fatty acid trafficking

Ferroptosis is a form of cell death caused by lipid peroxidation that is emerging as a target for cancer therapy, highlighting the need to identify factors that govern ferroptosis susceptibility. Lipid peroxidation occurs primarily on phospholipids containing polyunsaturated fatty acids (PUFAs). Here, we show that even though extracellular lipid limitation reduces cellular PUFA levels, lipid-starved cancer cells are paradoxically more sensitive to ferroptosis. Using mass spectrometry-based lipidomics with stable isotope fatty acid labeling, we show that lipid limitation induces a fatty acid trafficking pathway in which PUFAs are liberated from triglycerides to synthesize highly unsaturated PUFAs such as arachidonic acid and adrenic acid. These PUFAs then accumulate in phospholipids, particularly ether phospholipids, to promote ferroptosis sensitivity. Therefore, PUFA levels within cancer cells do not necessarily correlate with ferroptosis susceptibility. Rather, how cancer cells respond to extracellular lipid levels by trafficking PUFAs into proper phospholipid pools dictates their sensitivity to ferroptosis.

cancer biology↗

bioRxiv2024

First genome assembly of the order Strepsiptera using PacBio HiFi reads reveals a miniature genome

Twisted-wing insects (Strepsiptera) are an enigmatic order of parasites with unusual life histories and striking sexual dimorphism. Males emerge from hosts as free-living winged adults, while females from most species remain as endoparasites that retain larval traits. Due to scarce genomic data and phylogenetic controversies, Strepsiptera was only recently placed as the closest living relative to beetles (Coleoptera). Here, we report the first PacBio HiFi genome assembly of the strepsipteran Xenos peckii (Xenidae). This de novo assembly size is 72.1 Mb, with a BUSCO score of 87.4%, N50 of 7.3 Mb, 23.4% GC content and 38.41% repeat content. We identified 8 contigs that contain > 75% of the assembly and reflect the haploid chromosome number reported from karyotypic data, and 3 contigs that exhibit sex chromosome coverage patterns. Additionally, the mitochondrial genome is 16,111 bp long and has 34 genes. This long-read assembly for Strepsiptera reveals a miniature genome and provides a unique tool to understand complex genome evolution associated with a parasitic lifestyle and extreme sexual dimorphism.

genomics↗

bioRxiv2024

Structural insights into physiological activation and antagonism of melanin-concentrating hormone receptor MCHR1

Melanin-concentrating hormone (MCH) is a 19-amino-acid neuropeptide playing crucial roles in energy homeostasis, sleep, and various physiological processes. It acts through two G protein-coupled receptors, MCHR1 and MCHR2, with MCHR1 being universally present in mammals and a potential target for treating metabolic and mental health conditions. However, drug development efforts have been impeded by the lack of structural information. Here, we present the cryo-EM structures of MCHR1 in its active state complexed with MCH and Gi1, as well as in its inactive state bound to a selective antagonist SNAP-94847. Structural and mutagenesis analyses disclosed the recognition mechanisms for both MCH and SNAP-94847, the activation mechanism and antagonism of MCHR1, and the determinants of ligand specificity. These findings are expected to accelerate the development of better drugs targeting the MCH system.

biophysics↗

bioRxiv2024

DenoiseST: A dual-channel unsupervised deep learning-based denoising method to identify spatial domains and functionally variable genes in spatial transcriptomics

Spatial transcriptomics provides a unique opportunity for understanding cellular organization and function in a spatial context. However, spatial transcriptome exists the problem of dropout noise, exposing a major challenge for accurate downstream data analysis. Here, we proposed DenoiseST, a dual-channel unsupervised adaptive deep learning-based denoising method for data imputing, clustering, and identifying functionally variable genes in spatial transcriptomics. To leverage spatial information and gene expression profiles, we proposed a dual-channel joint learning strategy with graph convolutional networks to sufficiently explore both linear and nonlinear representation embeddings in an unsupervised manner, enhancing the discriminative information learning ability from the global perspectives of data distributions. In particular, DenoiseST enables the adaptively fitting of different gene distributions to the clustered domains and employs tissue-level spatial information to accurately identify functionally variable genes with different spatial resolutions, revealing their enrichment in corresponding gene pathways. Extensive validations on a total of 18 real spatial transcriptome datasets show that DenoiseST obtains excellent performance and results on brain tissue datasets indicate it outperforms the state-of-the-art methods when handling artificial dropout noise with a remarkable margin of [~]15%, demonstrating its effectiveness and robustness. Case study results demonstrate that when applied to identify biological structural regions on human breast cancer spatial transcriptomic datasets, DenoiseST successfully detected biologically significant immune-related structural regions, which are subsequently validated through Gene Ontology (GO), cell-cell communication, and survival analysis. In conclusion, we expect that DenoiseST is a novel and efficient method for spatial transcriptome analysis, offering unique insights into spatial organization and function.

bioinformatics↗

bioRxiv2024

Signaling and transcriptional dynamics underlying early adaptation to oncogenic BRAF inhibition

A major contributor to poor sensitivity to anti-cancer kinase inhibitor therapy is drug-induced cellular adaptation, whereby remodeling of signaling and gene regulatory networks permits a drug-tolerant phenotype. Here, we resolve the scale and kinetics of critical subcellular events following oncogenic kinase inhibition and preceding cell cycle re-entry, using mass spectrometry-based phosphoproteomics and RNA sequencing to capture molecular snapshots within the first minutes, hours, and days of BRAF kinase inhibitor exposure in a human BRAF-mutant melanoma model of adaptive therapy resistance. By enriching specific phospho-motifs associated with mitogenic kinase activity, we monitored the dynamics of thousands of growth- and survival-related protein phosphorylation events under oncogenic BRAF inhibition and drug removal. We observed early and sustained inhibition of the BRAF-ERK axis, gradual downregulation of canonical cell cycle-dependent signals, and three distinct and reversible phase transitions toward quiescence. Statistical inference of kinetically-defined signaling and transcriptional modules revealed a concerted response to oncogenic BRAF inhibition and a dominant compensatory induction of SRC family kinase (SFK) signaling, which we found to be at least partially driven by accumulation of reactive oxygen species via impaired redox homeostasis. This induction sensitized cells to co-treatment with an SFK inhibitor across a panel of patient-derived melanoma cell lines and in an orthotopic mouse xenograft model, underscoring the translational potential for measuring the early temporal dynamics of signaling and transcriptional networks under therapeutic challenge.

systems biology↗

bioRxiv2024

Overloading And unpacKing (OAK) - droplet-based combinatorial indexing for ultra-high throughput single-cell multiomic profiling

Multiomic profiling of single cells by sequencing is a powerful technique for investigating cellular diversity in complex biological systems. Although the existing droplet-based microfluidic methods have advanced single-cell sequencing, they produce a plethora of cell-free droplets and underutilize barcoding capacities due to their low cell concentration prerequisites. Meanwhile, combinatorial indexing on microplates can index cells in a more effective way; however, it requires time-consuming and laborious protocols involving multiple splitting and pooling steps. Addressing these constraints, we have developed "Overloading And unpacKing" (OAK). With reduced labor intensity, OAK can provide cost-effective multiomic profiling for hundreds of thousands of cells, offering detection sensitivity on par with commercial droplet-based methods. To demonstrate OAKs versatility, we conducted single-cell RNA sequencing (scRNA-Seq) as well as joint single-nucleus RNA sequencing (snRNA-Seq) and single-nucleus Assay for Transposase Accessible Chromatin with sequencing (snATAC-Seq) using cell lines. We further showcased OAKs performance on more complex samples, including in vitro differentiated bronchial epithelial cells and primary retinal tissues. Finally, we examined transcriptomic responses of 408,000 melanoma cells across around 1,000 starting lineages over a 90-day treatment with a RAF inhibitor, belvarafenib. We discovered a rare cell population (0.12%) that underwent a sequence of transcriptomic changes, resulting in belvarafenib resistance. Ultra-high throughput, broad compatibility with diverse molecular modalities, high detection sensitivity, and simplified experimental procedures distinguish OAK from previous methods, and render OAK a powerful tool for large-scale analysis of molecular signatures, even for rare cells.

molecular biology↗

bioRxiv2024

Self-inspired learning to denoise for live-cell super-resolution microscopy

Every collected photon is precious in live-cell super-resolution (SR) fluorescence microscopy for contributing to breaking the diffraction limit with the preservation of temporal resolvability. Here, to maximize the utilization of accumulated photons, we propose SN2N, a Self-inspired Noise2Noise engine with self-supervised data generation and self-constrained learning process, which is an effective and data-efficient learning-based denoising solution for high-quality SR imaging in general. Through simulations and experiments, we show that the SN2Ns performance is fully competitive to the supervised learning methods but circumventing the need for large training-set and clean ground-truth, in which a single noisy frame is feasible for training. By one-to-two orders of magnitude increased photon efficiency, the direct applications on various confocal-based SR systems highlight the versatility of SN2N for allowing fast and gentle 5D SR imaging. We also integrated SN2N into the prevailing SR reconstructions for artifacts removal, enabling efficient reconstructions from limited photons. Together, we anticipate our SN2N and its integrations could inspire further advances in the rapidly developing field of fluorescence imaging and benefit subsequent precise structure segmentation irrespective of noise conditions.

bioengineering↗

bioRxiv2024

Signaling events at TMEM doorways provide potential targets for inhibiting breast cancer dissemination

Tumor cell intravasation is essential for metastatic dissemination, but its exact mechanism is incompletely understood. We have previously shown that in breast cancer, the direct and stable association of a tumor cell expressing Mena, a Tie2hi/VEGFhi macrophage, and a vascular endothelial cell, creates an intravasation portal, called a "tumor microenvironment of metastasis" (TMEM) doorway, for tumor cell intravasation, leading to dissemination to distant sites. The density of TMEM doorways, also called TMEM doorway score, is a clinically validated prognostic marker of distant metastasis in breast cancer patients. Although we know that tumor cells utilize TMEM doorway-associated transient vascular openings to intravasate, the precise signaling mechanisms involved in TMEM doorway function are only partially understood. Using two mouse models of breast cancer and an in vitro assay of intravasation, we report that CSF-1 secreted by the TMEM doorway tumor cell stimulates local secretion of VEGF-A from the Tie2hi TMEM doorway macrophage, leading to the dissociation of endothelial junctions between TMEM doorway associated endothelial cells, supporting tumor cell intravasation. Acute blockade of CSF-1R signaling decreases macrophage VEGF-A secretion as well as TMEM doorway-associated vascular opening, tumor cell trans-endothelial migration, and dissemination. These new insights into signaling events regulating TMEM doorway function should be explored further as treatment strategies for metastatic disease.

cancer biology↗

bioRxiv2024

Genomic signatures of the evolution of loss of egg yolk in parasitoid wasps

Trait regression and loss have occurred repeatedly in numerous lineages throughout evolutionary history in response to changing environments. In parasitoid wasps, a mega-diverse group of hymenopteran insects, loss or reduction of yolk in the egg has been reported in many species. This phenotypic change likely evolved as a response to the shift from ectoparasitism to endoparasitism. However, the genetic basis of this trait and the impact of its loss on genome evolution remain poorly understood. Here, we performed a comparative genomic analysis of 64 hymenopteran insects. The conserved insect yolk protein gene vitellogenin (Vg) underwent five independent loss events in four families, involving 23 of the analyzed endoparasitoid species. Whole-genome alignment suggested that Vg loss occurred during genome rearrangement events. Analysis of Vg receptor gene (VgR) loss, selection, and structural variation in lineages lacking Vg demonstrated functional biases in the patterns of gene loss. The ectoparasitism to endoparasitism transition did not appear to be the primary driver of Vg loss or the subsequent VgR evolution. A number of parallel and convergent genomic changes were observed between Vg-loss lineages, including gene family evolution and selection of genes related to transport, development, and metabolism. These changes may have facilitated embryonic development without the yolk in these lineages. Together, these findings reveal the genomic basis underlying a unique trait loss in parasitoid wasps. More broadly, this study enhances our understanding of yolk loss evolution outside the class Mammalia, highlighting a potential evolutionary trend arising from the availability of an alternative nutrient source for embryonic development.

evolutionary biology↗

bioRxiv2023

Effects of fructooligosaccharides and Saccharomyces boulardii on the compositional structure and metabolism of gut microbiota in students

Fructooligosaccharides (FOS) are a common prebiotic widely used in functional foods. Meanwhile, Saccharomyces boulardii is a fungal probiotic frequenly used in the clinical treatment of diarrhea. Compared with single use, the combination of prebiotics and probiotics may be more effective in regulating gut microbiota. Hence, the combined use of prebiotics and probiotics has become a hot spot in synbiotics research in recent years. The present study aimed to investigate the effects of FOS and S. boulardii-- alone or in combination--on the structure and metabolism of the gut microbiota in healthy primary and secondary school students using an in vitro fermentation model. The results indicated that S. boulardii alone could not effectively regulate the community structure and metabolism of the microbiota. However, both FOS and the combination of FOS and S. boulardii could effectively regulate the microbiota, significantly inhibiting the growth of Escherichia-Shigella and Bacteroides, and controlling the production of the gases including H2S and NH3. In addition, both FOS and the combination could significantly promote the growth of Bifidobacteria and Lactobacillus, lower environmental pH, and enhance several physiological functions related to synthesis and metabolism. Nevertheless, the combination had more unique benefits as it promoted the growth of Lactobacillus, significantly increased CO2 production and enhanced the functional pathways of carbon metabolism and pyruvic acid metabolism. These findings provide guidance for clinical application and a theoretical basis for the development of synbiotic preparations.

microbiology↗

bioRxiv2023

Cell surface sculpting using logic-gated protein actuators

Cell differentiation and tissue specialization lead to unique cellular surface landscapes and exacerbated or loss of expression patterns can result in further heterogenicity distinctive of pathological phenotypes1-3. Immunotherapies and emerging protein therapeutics seek to exploit such differences by engaging cell populations selectively based on their surface markers. Since a single surface antigen rarely defines a specific cell type4,5, the development of programmable molecular systems that integrate multiple cell surface features to convert on-target inputs to user-defined outputs is highly desirable. Here, we describe an autonomous decision-making protein device driven by proximity-gated protein trans-splicing that allows local generation of an active protein from two otherwise inactive fragments. We show that this protein actuator platform can perform various Boolean logic operations on cell surfaces, allowing highly selective recruitment of enzymatic and cytotoxic activities to specific cells within mixed populations. Due to its intrinsic modularity and tunability, this technology is expected to be compatible with different types of inputs, targeting modalities and functional outputs, and as such will have broad application in the synthetic biology and biotechnology areas.

synthetic biology↗