Direct Signal Research Articles

A multiparameter sensing spectroscopic system for remote plastic sorting with a single laser shot: a first information report

The spectroscopic methods have been widely applied for plastic characterization and sorting to assist the recycling process. However, most of the reported works have been carried out in conventional laboratory conditions. In this study, we discuss development and application of a single-shot standoff multimodal LIBS-Raman spectroscopy system for plastic identification by modifying existing optical models for such analyses. By utilizing a unique co-axial back-collection optical geometry in standoff signal collection and a direct signal coupling scheme, the system was able to sense LIBS-Raman signals from plastics in single laser shot mode within 20 ms, with a non-gated CCD detector remotely. We examine the application of the system in characterizing four commodity plastic types of polyethylene terephthalate (PET), polypropylene (PP), polyethylene (PE), and polystyrene (PS)) based on atomic emissions (LIBS) and molecular structure (Raman Spectroscopy) by analyzing the respective standoff spectral data recorded from a distance of 4 m. The capability of the system to classify plastics based on standoff data is evaluated by using principal component analysis (PCA), which shows good segregation of the plastic classes depending on the intrinsic spectral features.

Intelligent Cell Profiling and Precision Release: Multimolecular Marker-Activated Transmembrane DNA Computing Nanosystem.

Accurate classification of tumor cells is of importance for cancer diagnosis and further therapy. In this study, we develop multimolecular marker-activated transmembrane DNA computing systems (MTD). Employing the cell membrane as a native gate, the MTD system enables direct signal output following simple spatial events of "transmembrane" and "in-cell target encounter", bypassing the need of multistep signal conversion. The MTD system comprises two intelligent nanorobots capable of independently sensing three molecular markers (MUC1, EpCAM, and miR-21), resulting in comprehensive analysis. Our AND-AND logic-gated system (MTDAND-AND) demonstrates exceptional specificity, allowing targeted release of drug-DNA specifically in MCF-7 cells. Furthermore, the transformed OR-AND logic-gated system (MTDOR-AND) exhibits broader adaptability, facilitating the release of drug-DNA in three positive cancer cell lines (MCF-7, HeLa, and HepG2). Importantly, MTDAND-AND and MTDOR-AND, while possessing distinct personalized therapeutic potential, share the ability of outputting three imaging signals without any intermediate conversion steps. This feature ensures precise classification cross diverse cells (MCF-7, HeLa, HepG2, and MCF-10A), even in mixed populations. This study provides a straightforward yet effective solution to augment the versatility and precision of DNA computing systems, advancing their potential applications in biomedical diagnostic and therapeutic research.

Single-hydrophone-based passive localization for vertically moving targets in shallow water

In this article, we consider underwater passive localization for vertically moving targets in shallow water. A method that utilizes parameters derived from interference structures is introduced, which enables the implementation of both range and depth estimations for targets with a single hydrophone. Underwater moving targets radiate broadband continuous spectrum signals by forming multipath signals through interface reflections, resulting in stable interference structures in the time-frequency domain of the received signals. Ray theory is utilized to derive the trajectory equations of the interference fringes. Considering the periodic characteristics of interference structures, a specialized homomorphic filter is designed to effectively restore the peaks of the fringes to reduce localization errors. Furthermore, with a priori information on the target's initial depth, the Hough Transform is used to extract the target's location information within interference fringes formed by direct signal and surface-reflected signal. Addressing the issue of multi-peaks in parameter domain, parameter information across multiple fringes is fused. The effectiveness and robustness of the proposed method have been demonstrated through numerical simulations, lake trial, and error assessments, even when underwater environments information is not precisely known.

Prediction of m6A and m5C at single-molecule resolution reveals a transcriptome-wide co-occurrence of RNA modifications

The epitranscriptome embodies many new and largely unexplored functions of RNA. A significant roadblock hindering progress in epitranscriptomics is the identification of more than one modification in individual transcript molecules. We address this with CHEUI (CH3 (methylation) Estimation Using Ionic current). CHEUI predicts N6-methyladenosine (m6A) and 5-methylcytosine (m5C) in individual molecules from the same sample, the stoichiometry at transcript reference sites, and differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals to achieve high single-molecule, transcript-site, and stoichiometry accuracies in multiple tests using synthetic RNA standards and cell line data. CHEUI’s capability to identify two modification types in the same sample reveals a co-occurrence of m6A and m5C in individual mRNAs in cell line and tissue transcriptomes. CHEUI provides new avenues to discover and study the function of the epitranscriptome.

CoopeRIS: A framework for the simulation of reconfigurable intelligent surfaces in cooperative driving environments

Future connected vehicles will require high-performance communication technologies for advanced cooperative driving applications such as maneuvering and cooperative perception. mmWave communications can meet the bandwidth requirements of such applications, but the typically harsh propagation conditions of vehicular environments hinder the broad adoption of mmWave devices on cars. Reconfigurable intelligent surfaces (RISs) can help mitigate this problem by enabling the reflection of signals in a configurable direction. In turn, this can result in more stable non-line of sight (NLoS) links whenever a LoS path is not available. RISs have recently gained attention in the vehicular domain but, while providing benefits, they also introduce a lot of research challenges. To measure their effectiveness at scale, it is necessary to develop simulation tools that can reproduce their characteristics with high fidelity and federate them with existing cooperative driving simulation frameworks. In this work we present CoopeRIS, an open-source simulation framework federated within the Plexe/Veins/SUMO ecosystem, capable of modeling and simulating RIS-based mmWave communications in a vehicular environment. We exploit CoopeRIS to perform an initial feasibility study, highlighting the challenges ahead and the performance RISs need to deliver in order to enable this type of communication. In addition we propose a method to combine multiple RIS configurations into a single one to enable multi-user service delivery, showing its performance via CoopeRIS. The insights we presents within this work show the potential of such simulation framework and thus how the community can build further research work on top if it.

Molecularly Imprinted Polymer Biosensor Based on Nitrogen-Doped Electrochemically Exfoliated Graphene/Ti3 CNTX MXene Nanocomposite for Metabolites Detection.

Rapid and accurate quantification of metabolites in different bodily fluids is crucial for a precise health evaluation. However, conventional metabolite sensing methods, confined to centralized laboratory settings, suffer from time-consuming processes, complex procedures, and costly instrumentation. Introducing the MXene/nitrogen-doped electrochemically exfoliated graphene (MXene@N-EEG) nanocomposite as a novel biosensing platform in this work addresses the challenges associated with conventional methods, leveraging the concept of molecularly imprinted polymers (MIP) enables the highly sensitive, specific, and reliable detection of metabolites. To validate our biosensing technology, we utilize agmatine as a significant biologically active metabolite. The MIP biosensor incorporates electrodeposited Prussian blue nanoparticles as a redox probe, facilitating the direct electrical signaling of agmatine binding in the polymeric matrix. The MXene@N-EEG nanocomposite, with excellent metal conductivity and a large electroactive specific surface area, effectively stabilizes the electrodeposited Prussian blue nanoparticles. Furthermore, increasing the content of agmatine-imprinted cavities on the electrode enhances the sensitivity of the MIP biosensor. Evaluation of the designed MIP biosensor in buffer solution and plasma samples reveals a wide linear concentration range of 1.0 nM-100.0 μM (R2 = 0.9934) and a detection limit of 0.1 nM. Notably, the developed microfluidic biosensor offers low cost, rapid response time to the target molecule (10 min of sample incubation), good recovery results for detecting agmatine in plasma samples, and acceptable autonomous performance for on-chip detection. Moreover, its high reliability and sensitivity position this MIP-based biosensor as a promising candidate for miniaturized microfluidic devices with the potential for scalable production for point-of-care applications.

The CD4 T cell-intrinsic C3-like molecule CD109 prevents hyper-inflammatory T cell responses

Abstract Immune cell autonomous complement expression and function emerges as pivotal regulator of tissue immunity and T cell biology. Specifically, CD4 T cell intrinsic engagement of CD46 by TCR stimulation-triggered generation of the CD46 ligand C3b, is required for IFN-γ production. In consequence, CD46-deficient patients are unable to generate normal Th1 responses. We identified CD109, a GPI anchored C3-like protein and a direct CD46 signaling induced target. Despite its known ability to regulate TGF-ßR1 signaling in cancer cells, a direct functional activity for CD109 on T cells remains unexplored. Human CD4 T cells lacking CD109 expression displayed significantly augmented IFN-γ and IL-17 production upon in vitro stimulation under non-skewing conditions. Similarly, CD4 T cells from Cd109–/– mice showed in vitro Th1 and Th17 hyperactivation and caused more severe EAE pathology in vivo. Unexpectedly, T cell CD109 does not control TGF-ß signaling but restrains IFN-γ and/or IL-17 by binding to and inhibiting the activity of a non-canonical costimulatory molecule linked to Th1/Th17 biology. Guided by modeling and Cryo-EM data, we designed a CD109/costimulatory inhibitory peptide that recapitulates the phenotype observed in CD109-deficient T cells. Together, these data suggest that the C3-like protein CD109 serves as an important molecular brake (or switch) on CD4 T cells by fine-tuning costimulatory signals upstream of the hyper-inflammatory Th1/Th17 induction pathways.

Self-Training Boosted Multi-Factor Matching Network for Composed Image Retrieval.

The composed image retrieval (CIR) task aims to retrieve the desired target image for a given multimodal query, i.e., a reference image with its corresponding modification text. The key limitations encountered by existing efforts are two aspects: 1) ignoring the multiple query-target matching factors; 2) ignoring the potential unlabeled reference-target image pairs in existing benchmark datasets. To address these two limitations is non-trivial due to the following challenges: 1) how to effectively model the multiple matching factors in a latent way without direct supervision signals; 2) how to fully utilize the potential unlabeled reference-target image pairs to improve the generalization ability of the CIR model. To address these challenges, in this work, we first propose a CLIP-Transformer based muLtI-factor Matching Network (LIMN), which consists of three key modules: disentanglement-based latent factor tokens mining, dual aggregation-based matching token learning, and dual query-target matching modeling. Thereafter, we design an iterative dual self-training paradigm to further enhance the performance of LIMN by fully utilizing the potential unlabeled reference-target image pairs in a weakly-supervised manner. Specifically, we denote the iterative dual self-training paradigm enhanced LIMN as LIMN+. Extensive experiments on four datasets, including FashionIQ, Shoes, CIRR, and Fashion200 K, show that our proposed LIMN and LIMN+ significantly surpass the state-of-the-art baselines.

Detection and Classification of Rolling Bearing Defects Using Direct Signal Processing with Deep Convolutional Neural Network

Currently, great emphasis is being placed on the electrification of means of transportation, including aviation. The use of electric motors reduces operating and maintenance costs. Electric motors are subjected to various types of damage during operation, of which rolling bearing defects are statistically the most common. This article focuses on presenting a diagnostic tool for bearing conditions based on mechanic vibration signals using convolutional neural networks (CNN). This article presents an alternative to the well-known classical diagnostic tools based on advanced signal processing methods such as the short-time Fourier transform, the Hilbert–Huang transform, etc. The approach described in the article provides fault detection and classification in less than 0.03 s. The proposed structures achieved a classification accuracy of 99.8% on the test set. Special attention was paid to the process of optimizing the CNN structure to achieve the highest possible accuracy with the fewest number of network parameters.

Derivative-synchronous fluorescence spectroscopy enhanced surface plasmon coupled emission for sensitive detection of tannic acid

In this study, we developed a new derivative-synchronous fluorescence spectroscopy enhanced surface plasmon coupled emission (DS-SPCE) technique for the detection of tannic acid (TA). DS-SPCE generates greatly amplified directional fluorescence signal, approaching nearly a 20-fold enhancement. The property of a gradual fluorescence quenching of the monolayer Rhodamine B isothiocyanate (RBITC) with increasing TA concentration was used for the quantitative detection of TA. The method achieved an ultra-low limit of detection (LOD) of 15.3 pM. It exhibited high selectivity for TA detection, even in the presence of common interferences such as glucose, catechin, and ascorbic acid at concentrations exceeding 100-fold. DS-SPCE method demonstrated robust performance in analyzing TA in tea samples, yielding good average recoveries of TA. Consequently, DS-SPCE emerges as a straightforward fluorescence enhancement approach, providing valuable insights for its practical application.

Prediction of the Freshness of Grass Carp during Storage with Electric Nose Based on Signal Sequence Merging and Wavelet Transform

In order to predict the freshness of grass carp, a novel data preprocessing method was proposed for electronic nose (E-nose) signals. The signal sequences from six sensors were selected and subsequently normalized. The direct signal sequence merging (DSSM) and reversed signal sequence merging (RSSM) modes were used for signal sequence merging. Subsequently, the genetic algorithm (GA) was used to evaluate the contribution of diverse sensors, and the merged data sequence was compressed using wavelet transform (WT). Using approximation coefficient and detail coefficient based on different scales and different signal sequence merging modes, principal component analysis (PCA) discriminated successfully storage time of chilled fish fillet. The PCA plots clearly demonstrated that all extracted feature data fully retain the signal characters. The partial least squares (PLS) and artificial neural network (ANN) models were used to establish prediction models for the freshness of grass carp during storage. The DSSM-ANN-A5 and DSSM-PLS-D4 models were chosen as the TVB-N content prediction models, while the DSSM-ANN-D5 and RSSM-PLS-A0 models were selected as the K value prediction models. The R2 values of these models are higher than 0.9, and they have a good coefficient of determination. The results of this study suggest that it using E-nose signals to predict TVB-N content and K value is an effective method for assessing the freshness of grass carp during storage.

Coronary Microvascular Dysfunction in Acute Cholestasis-Induced Liver Injury.

Previous studies have shown cardiac abnormalities in acute liver injury, suggesting a potential role in the associated high mortality. We designed an experimental study exploring the short-term effects of acute cholestasis-induced liver injury on cardiac function and structure in a rodent bile duct ligation (BDL) model to elucidate the potential interplay. Thirty-seven male Sprague-Dawley rats were subjected to BDL surgery (n = 28) or served as sham-operated (n = 9) controls. Transthoracic echocardiography, Doppler evaluation of the left anterior descending coronary artery, and myocardial contrast echocardiography were performed at rest and during adenosine and dobutamine stress 5 days after BDL. Immunohistochemical staining of myocardial tissue samples for hypoxia and inflammation as well as serum analysis were performed. BDL animals exhibited acute liver injury with elevated transaminases, bilirubin, and total circulating bile acids (TBA) 5 days after BDL (TBA control: 0.81 ± 2.54 µmol/L vs. BDL: 127.52 ± 57.03 µmol/L; p < 0.001). Concurrently, cardiac function was significantly impaired, characterized by reduced cardiac output (CO) and global longitudinal strain (GLS) in the echocardiography at rest and under pharmacological stress (CO rest control: 120.6 ± 24.3 mL/min vs. BDL 102.5 ± 16.6 mL/min, p = 0.041; GLS rest control: -24.05 ± 3.8% vs. BDL: -18.5 ± 5.1%, p = 0.01). Myocardial perfusion analysis revealed a reduced myocardial blood flow at rest and a decreased coronary flow velocity reserve (CFVR) under dobutamine stress in the BDL animals (CFVR control: 2.1 ± 0.6 vs. BDL: 1.7 ± 0.5 p = 0.047). Immunofluorescence staining indicated myocardial hypoxia and increased neutrophil infiltration. In summary, acute cholestasis-induced liver injury can lead to impaired cardiac function mediated by coronary microvascular dysfunction, suggesting that major adverse cardiac events may contribute to the mortality of acute liver failure. This may be due to endothelial dysfunction and direct bile acid signaling.

In Situ Nondestructive Detection of Nitrogen Content in Soybean Leaves Based on Hyperspectral Imaging Technology

In this paper, hyperspectral imaging technology, combined with chemometrics methods, was used to detect the nitrogen content of soybean leaves, and to achieve the rapid, non-destructive and in situ detection of the nitrogen content in soybean leaves. Soybean leaves under different fertilization treatments were used as the research object, and the hyperspectral imaging data and the corresponding nitrogen content data of soybean leaves at different growth stages were obtained. Seven spectral preprocessing methods, such as Savitzky–Golay smoothing (SG), first derivative (1-Der), and direct orthogonal signal correction (DOSC), were used to establish the quantitative prediction models for soybean leaf nitrogen content, and the quantitative prediction models of different spectral preprocessing methods for soybean leaf nitrogen content were analyzed and compared. On this basis, successive projections algorithm (SPA), genetic algorithm (GA) and random frog (RF) were employed to select the characteristic wavelengths and compress the spectral data. The results showed the following: (1) The full-spectrum prediction model of soybean leaf nitrogen content based on DOSC pretreatment was the best. (2) The PLS model of soybean leaf nitrogen content based on the five characteristic wavelengths had the best prediction performance. (3) The spatial distribution map of soybean leaf nitrogen content was generated in a pixel manner using the extracted five characteristic wavelengths and the DOSC-RF-PLS model. The nitrogen content level of soybean leaves can be quantified in a simple way; this provides a foundation for rapid in situ non-destructive detection and the spatial distribution difference detection of soybean leaf nitrogen. (4) The overall results illustrated that hyperspectral imaging technology was a powerful tool for the spatial prediction of the nitrogen content in soybean leaves, which provided a new method for the spatial distribution of the soybean nutrient status and the dynamic monitoring of the growth status.

Atypical connectome topography and signal flow in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common pharmaco-resistant epilepsy in adults. While primarily associated with mesiotemporal pathology, recent evidence suggests that brain alterations in TLE extend beyond the paralimbic epicenter and impact macroscale function and cognitive functions, particularly memory. Using connectome-wide manifold learning and generative models of effective connectivity, we examined functional topography and directional signal flow patterns between large-scale neural circuits in TLE at rest. Studying a multisite cohort of 95 patients with TLE and 95 healthy controls, we observed atypical functional topographies in the former group, characterized by reduced differentiation between sensory and transmodal association cortices, with most marked effects in bilateral temporo-limbic and ventromedial prefrontal cortices. These findings were consistent across all study sites, present in left and right lateralized patients, and validated in a subgroup of patients with histopathological validation of mesiotemporal sclerosis and post-surgical seizure freedom. Moreover, they were replicated in an independent cohort of 30 TLE patients and 40 healthy controls. Further analyses demonstrated that reduced differentiation related to decreased functional signal flow into and out of temporolimbic cortical systems and other brain networks. Parallel analyses of structural and diffusion-weighted MRI data revealed that topographic alterations were independent of TLE-related cortical thinning but partially mediated by white matter microstructural changes that radiated away from paralimbic circuits. Finally, we found a strong association between the degree of functional alterations and behavioral markers of memory dysfunction. Our work illustrates the complex landscape of macroscale functional imbalances in TLE, which can serve as intermediate markers bridging microstructural changes and cognitive impairment.

(Don't) look where you are going: Evidence for a travel direction signal in humans that is independent of head direction.

We often assume that travel direction is redundant with head direction, but from first principles, these two factors provide differing spatial information. Although head direction has been found to be a fundamental component of human navigation, it is unclear how self-motion signals for travel direction contribute to forming a travel trajectory. Employing a novel motion adaptation paradigm from visual neuroscience designed to preclude a contribution of head direction, we found high-level aftereffects of perceived travel direction, indicating that travel direction is a fundamental component of human navigation. Interestingly, we discovered a higher frequency of reporting perceived travel toward the adapted direction compared to a no-adapt control-an aftereffect that runs contrary to low-level motion aftereffects. This travel aftereffect was maintained after controlling for possible response biases and approaching effects, and it scaled with adaptation duration. These findings demonstrate the first evidence of how a pure travel direction signal might be represented in humans, independent of head direction. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Immunomodulation and inflammation: Role of GLP-1R and GIPR expressing cells within the gut

Glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) are peptide hormones produced by enteroendocrine cells in the small intestine. Despite being produced in the gut, the leveraging of their role in potentiating glucose-stimulated insulin secretion, also known as the incretin effect, has distracted from discernment of direct intestinal signaling circuits. Both preclinical and clinical evidence have highlighted a role for the incretins in inflammation. In this review, we highlight the discoveries of GLP-1 receptor (GLP-1R)+ natural (TCRαβ and TCRγδ) and induced (TCRαβ+CD4+ cells and TCRαβ+CD8αβ+) intraepithelial lymphocytes. Both endogenous signaling and pharmacological activation of GLP-1R impact local and systemic inflammation, the gut microbiota, whole-body metabolism, as well as the control of GLP-1 bioavailability. While GIPR signaling has been documented to impact hematopoiesis, the impact of these bone marrow-derived cells in gut immunology is not well understood. We uncover gaps in the literature of the evaluation of the impact of sex in these GLP-1R and GIP receptor (GIPR) signaling circuits and provide speculations of the maintenance roles these hormones play within the gut in the fasting-refeeding cycles. GLP-1R agonists and GLP-1R/GIPR agonists are widely used as treatments for diabetes and weight loss, respectively; however, their impact on gut homeostasis has not been fully explored. Advancing our understanding of the roles of GLP-1R and GIPR signaling within the gut at homeostasis as well as metabolic and inflammatory diseases may provide targets to improve disease management.

Ionic Rectification by Dynamic Regulation of the Electric Double Layer at the Hydrogel Interface.

Hydrogels play a pivotal role in the realm of iontronics, contributing to the realization of futuristic human-machine interactions. The electric double layer (EDL) between the hydrogel and electrode provides an essential ionic-electronic coupling interface. While prior investigations primarily delved into elucidating the formation mechanism of the EDL, our study shifts the focus to showcasing the current generation through the mechanical modulation of the EDL at the hydrogel-metal interfaces. The dynamic EDL was constructed by the mechano-driven contact-separation process between the polyacrylamide (PAAm) hydrogel and Au. Influencing factors on the dynamic regulation of the EDL such as ion concentration, types of salt, contact-separation frequency, and deformation degree were investigated. Dehydration usually limits the practical applications of hydrogels, and it is a long-standing and difficult problem. However, it seemed to be able to slow the EDL formation process here, resulting in a sustained continuous direct current signal output. Such hydrogel iontronics could rectify the displacement electronic current of a triboelectric nanogenerator by the ionic current. The directional migration of ions could be further enhanced by using charge-collecting metals with different work functions, for example, Au and Al. It offers a paradigm to enable ionic rectification that could be seamlessly incorporated into electronic systems, ushering in a new era for efficient energy harvesting and biomimetic nervous systems.

Comparison of In Silico Signal Sequence-Phospholipid Results with Described In Vitro and In Vivo Protein Translocation Studies Seems to Underscore the Significance of Phospholipids

The precise role of protein–lipid interactions in protein translocation is, after almost four decades of research, still a matter of debate. The experimental evidence, as described in the literature, indicates that (anionic) phospholipids play a role in numerous events in protein translocation; however, its meaning and relevance are still a matter of debate. This study tries to fill some missing links in the experimental evidence by means of in silico experiments. The study presented here indicates not only that there is a direct signal sequence–phospholipid interaction but also that the corresponding signal peptides can translocate additional amino acids across a pure lipid membrane. Furthermore, results are presented when it comes to the extent of anionic phospholipids’ dependence on this process. The correlations between the in silico results of pure signal peptide–phospholipid interactions and the observed experimental trends in the overall protein translocation effects are at least remarkable. The results emphasize that new models for protein translocation will have to be developed to take all these and previous experimental data into account.

ALISON: Fast and Effective Stylometric Authorship Obfuscation

Authorship Attribution (AA) and Authorship Obfuscation (AO) are two competing tasks of increasing importance in privacy research. Modern AA leverages an author's consistent writing style to match a text to its author using an AA classifier. AO is the corresponding adversarial task, aiming to modify a text in such a way that its semantics are preserved, yet an AA model cannot correctly infer its authorship. To address privacy concerns raised by state-of-the-art (SOTA) AA methods, new AO methods have been proposed but remain largely impractical to use due to their prohibitively slow training and obfuscation speed, often taking hours. To this challenge, we propose a practical AO method, ALISON, that (1) dramatically reduces training/obfuscation time, demonstrating more than 10x faster obfuscation than SOTA AO methods, (2) achieves better obfuscation success through attacking three transformer-based AA methods on two benchmark datasets, typically performing 15% better than competing methods, (3) does not require direct signals from a target AA classifier during obfuscation, and (4) utilizes unique stylometric features, allowing sound model interpretation for explainable obfuscation. We also demonstrate that ALISON can effectively prevent four SOTA AA methods from accurately determining the authorship of ChatGPT-generated texts, all while minimally changing the original text semantics. To ensure the reproducibility of our findings, our code and data are available at: https://github.com/EricX003/ALISON.

Abstract 6633: Tracking cell-cell interactions using intercellular barcode transfer

Abstract Cell-cell interaction is one of the fundamental biological mechanisms by which cells communicate and is a key modality by which cancer cells interact with other cells in their microenvironment. Current methods, such as enzymatic labeling and proximity-based tagging, allow the experimental tracking of these interactions but are not compatible with sequencing-based readouts, which are essential for high-throughput analyses. We have developed a highly scalable intercellular barcode transfer technology that bridges this gap. Our novel “Relay” technology offers a scalable solution to monitor cell-cell interactions with unprecedented single-cell resolution, seamlessly integrating with existing high-throughput and sequencing platforms. Relay employs RNA barcodes to mark and track cell interaction events, providing definitive quantitative data on cell interaction partners. We used Relay to track interactions between cancer cells and T cells, capturing both potent antigen-specific engagements and subtler contacts involved in antigen sampling. Additionally, by integrating CRISPR libraries with Relay, we pioneered a novel approach for pooled CRISPR screens, focusing on intercellular interactions across different cell types. In this context, the cell harboring genetic perturbation may differ from the cell under analysis. To demonstrate this, we are conducting genome-wide perturbations in cancer cells and interrogating cancer cell factors that affect antigen sampling and trogocytosis by T cells and macrophages. Beyond its application in CRISPR screening, Relay can also be used with single-cell RNA sequencing to directly track and measure immune-cancer and immune-immune cell interactions at the single cell level. We applied single-cell Relay to a dendritic cell-T cell system to link dendritic cell signatures to T cell signatures based on direct cell contact and regulatory signaling. The design of Relay as a highly modular system extends its utility beyond cancer research, making it broadly applicable across various biological models and systems where cell-cell communication is a fundamental property in complex multicellular settings. This tool promises to accelerate the transition from theoretical understanding to practical exploration of cell-cell interactions, with profound implications for immunology, oncology, and beyond. Citation Format: Neil Q. Tay, Tiffany Juan, Joseph J. Muldoon, Justin Eyquem, Michael T. McManus. Tracking cell-cell interactions using intercellular barcode transfer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6633.