Biology subjects

Yan, X.

Publications and source records attributed to Yan, X..

At least 235 records · Page 13Linked to original sources

bioRxiv2020

SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement

Accurate identification of cell types from single-cell RNA sequencing (scRNA-seq) data plays a critical role in a variety of scRNA-seq analysis studies. It corresponds to solving an unsupervised clustering problem, in which the similarity measurement between cells in a high dimensional space affects the result significantly. Although many approaches have been proposed recently, the accuracy of cell type identification still needs to be improved. In this study, we proposed a novel single-cell clustering framework based on similarity learning, called SSRE. In SSRE, we model the relationships between cells based on subspace assumption and generate a sparse representation of the cell-to-cell similarity, which retains the most similar neighbors for each cell. Besides, we adopt classical pairwise similarities incorporated with a gene selection and enhancement strategy to further improve the effectiveness of SSRE. For performance evaluation, we applied SSRE in clustering, visualization, and other exploratory data analysis processes on various scRNA-seq datasets. Experimental results show that SSRE achieves superior performance in most cases compared to several state-of-the-art methods.

bioinformatics↗

bioRxiv2020

G2S3: a gene graph-based imputation method for single-cell RNA sequencing data

Single-cell RNA sequencing provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses. We propose a novel method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and other existing methods to seven single-cell datasets to compare their performance. Our results demonstrated that G2S3 is superior in recovering true expression levels, identifying cell subtypes, improving differential expression analyses, and recovering gene regulatory relationships, especially for mildly expressed genes.

bioinformatics↗

bioRxiv2020

Therapeutic genetic variation revealed in diverse Hsp104 homologs

The AAA+ protein disaggregase, Hsp104, increases fitness under stress by reversing stress-induced protein aggregation. We have engineered potentiated Hsp104 variants to antagonize proteotoxic misfolding linked to human neurodegenerative diseases. However, these Hsp104 variants can exhibit off-target toxicity, which may limit their therapeutic utility. Hsp104 is conserved among all nonmetazoan eukaryotes, which raises the possibility that natural variants might exist with enhanced, selective activity against neurodegenerative disease substrates. To assess this possibility, we screened a cross-kingdom collection of Hsp104 homologs in several yeast proteotoxicity models. We uncovered therapeutic genetic variation among several Hsp104 homologs that specifically antagonize TDP-43 or -synuclein condensate formation and toxicity in yeast, human cells, and C. elegans. Surprisingly, this variation manifested as increased passive chaperone activity, distinct from disaggregase activity, which neutralizes proteotoxicity of specific substrates. Thus, by exploring natural tuning of passive chaperone activity we elucidated enhanced, substrate-specific agents to counter proteotoxicity underlying neurodegenerative disease.

biochemistry↗

bioRxiv2020

Accurate Diagnosis of Colorectal Cancer Based on Histopathology Images Using Artificial Intelligence

BackgroundAccurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on their daily image analyses. MethodsBased on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC prediction/diagnosis using weakly labeled pathological whole slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, >14,680 WSIs, from >9,631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, U.S., and Germany. ResultsOur innovative AI tool was consistently nearly perfectly agreed with (average Kappa-statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multi-centers. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.981 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. ConclusionsThis first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. Hence, it will drastically alleviate the heavy clinical burden of daily pathology diagnosis, and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

pathology↗

bioRxiv2020

Identification of novel mitochondrial and mitochondrial related genetic loci associated with exercise response in the Gene SMART study

Mitochondria supply intracellular energy requirements during exercise. Specific mitochondrial haplogroups and mitochondrial genetic variants have been associated with athletic performance, and exercise responses. However, these associations were discovered using underpowered, candidate gene approaches, and consequently have not been replicated. Here, we used whole-mitochondrial genome sequencing, in conjunction with high-throughput genotyping arrays, to discover novel genetic variants associated with exercise responses in the Gene SMART (Skeletal Muscle Adaptive Response to Training) cohort (n=62 completed). We performed a Principal Component Analysis of cohort aerobic fitness measures to build composite traits and test for variants associated with exercise outcomes. None of the mitochondrial genetic variants but nine nuclear encoded variants in eight separate genes were found to be associated with exercise responses (FDR<0.05) (rs11061368: DIABLO, rs113400963: FAM185A, rs6062129 and rs6121949: MTG2, rs7231304: AFG3L2, rs2041840: NDUFAF7, rs7085433: TIMM23, rs1063271: SPTLC2, rs2275273: ALDH18A1). Additionally, we outline potential mechanisms by which these variants may be contributing to exercise phenotypes. Our data suggest novel nuclear-encoded SNPs and mitochondrial pathways associated with exercise response phenotypes. Future studies should focus on validating these variants across different cohorts and ethnicities. AUTHOR SUMMARYPrevious exercise genetic studies contain many flaws that impede the growth in knowledge surrounding change in exercise outcomes. In particular, exercise studies looking at mtDNA variants have looked at very small portions of the mitochondrial genome. Mitochondria are the power house of the cell and therefore understanding the mitochondrial genetics behind adaptations to training can help us fill knowledge gaps in current research. Here, we utilised a new mitochondrial genetic sequencing technique to examine all mitochondrial and mitochondrial related genetic variations. We have shown that there were no mitochondrial specific variants that influenced exercise training however there were 9 related variants that were significantly associated with exercise phenotypes. Additionally, we have shown that building composite traits increased the significance of our association testing and lead to novel findings. We will be able to understand why response to training is so varied and increase the effectiveness of exercise training on a host of metabolic disorders.

genetics↗

bioRxiv2020

Quantifying microbially mediated fitness differences reveals the tendency for plant-soil feedbacks to drive species exclusion among California annual plants

Soil microorganisms influence a variety of processes in plant communities. Many the-oretical and empirical studies have shown that dynamic feedbacks between plants and soil microbes can stabilize plant coexistence by generating negative frequency-dependent plant population dynamics. However, inferring the net effects of soil microbes on plant coexistence requires also quantifying the degree to which they provide one species an average fitness advantage, an effect that has received little empirical attention. We conducted a greenhouse study to quantify microbially mediated stabilization and fitness differences among fifteen pairs of annual plants that co-occur in southern California grasslands. We found that although soil microbes frequently generate negative frequency-dependent dynamics that stabilize plant interactions, they simultaneously mediate large average fitness differences between species. The net result is that if the plant species are otherwise competitively equivalent, the impact of plant-soil feedbacks is often to favor species exclusion over coexistence, a result that only becomes evident by quantifying the microbially mediated fitness difference. Our work highlights that comparing the stabilizing effects of plant-soil feedbacks to the fitness difference they generate is essential for understanding the influence of soil microbes on plant diversity.

ecology↗

bioRxiv2020

Identifying Nootropic Drug Targets via Large-Scale Cognitive GWAS and Transcriptomics

BackgroundCognitive traits demonstrate significant genetic correlations with many psychiatric disorders and other health-related traits. Many neuropsychiatric and neurodegenerative disorders are marked by cognitive deficits. Therefore, genome-wide association studies (GWAS) of general cognitive ability might suggest potential targets for nootropic drug repurposing. Our previous effort to identify "druggable genes" (i.e., GWAS-identified genes that produce proteins targeted by known small molecules) was modestly powered due to the small cognitive GWAS sample available at the time. Since then, two large cognitive GWAS meta-analyses have reported 148 and 205 genome-wide significant loci, respectively. Additionally, large-scale gene expression databases, derived from post-mortem human brain, have recently been made available for GWAS annotation. Here, we 1) reconcile results from these two cognitive GWAS meta-analyses to further enhance power for locus discovery; 2) employ several complementary transcriptomic methods to identify genes in these loci with variants that are credibly associated with cognition; and 3) further annotate the resulting genes to identify "druggable" targets. MethodsGWAS summary statistics were harmonized and jointly analysed using Multi-Trait Analysis of GWAS [MTAG], which is optimized for handling sample overlaps. Downstream gene identification was carried out using MAGMA, S-PrediXcan/S-TissueXcan Transcriptomic Wide Analysis, and eQTL mapping, as well as more recently developed methods that integrate GWAS and eQTL data via Summary-statistics Mendelian Randomization [SMR] and linkage methods [HEIDI], Available brain-specific eQTL databases included GTEXv7, BrainEAC, CommonMind, ROSMAP, and PsychENCODE. Intersecting credible genes were then annotated against multiple chemoinformatic databases [DGIdb, KI, and a published review on "druggability"]. ResultsUsing our meta-analytic data set (N = 373,617) we identified 241 independent cognition-associated loci (29 novel), and 76 genes were identified by 2 or more methods of gene identification. 26 genes were associated with general cognitive ability via SMR, 16 genes via STissueXcan/S-PrediXcan, 47 genes via eQTL mapping, and 68 genes via MAGMA pathway analysis. The use of the HEIDI test permitted the exclusion of candidate genes that may have been artifactually associated to cognition due to linkage, rather than direct causal or indirect pleiotropic effects. Actin and chromatin binding gene sets were identified as novel pathways that could be targeted via drug repurposing. Leveraging on our various transcriptome and pathway analyses, as well as available chemoinformatic databases, we identified 16 putative genes that may suggest drug targets with nootropic properties. DiscussionResults converged on several categories of significant drug targets, including serotonergic and glutamatergic genes, voltage-gated ion channel genes, carbonic anhydrase genes, and phosphodiesterase genes. The current results represent the first efforts to apply a multi-method approach to integrate gene expression and SNP level data to identify credible actionable genes for general cognitive ability.

genomics↗

bioRxiv2020

RFDTI: Using Rotation Forest with Feature Weighted for Drug-Target Interaction Prediction from Drug Molecular Structure and Protein Sequence

The identification and prediction of Drug-Target Interactions (DTIs) is the basis for screening drug candidates, which plays a vital role in the development of innovative drugs. However, due to the time-consuming and high cost constraints of biological experimental methods, traditional drug target identification technologies are often difficult to develop on a large scale. Therefore, in silico methods are urgently needed to predict drug-target interactions in a genome-wide manner. In this article, we design a new in silico approach, named RFDTI to predict the DTIs combine Feature weighted Rotation Forest (FwRF) classifier with protein amino acids information. This model has two outstanding advantages: a) using the fusion data of protein sequence and drug molecular fingerprint, which can fully carry information; b) using the classifier with feature selection ability, which can effectively remove noise information and improve prediction performance. More specifically, we first use Position-Specific Score Matrix (PSSM) to numerically convert protein sequences and utilize Pseudo Position-Specific Score Matrix (PsePSSM) to extract their features. Then a unified digital descriptor is formed by combining molecular fingerprints representing drug information. Finally, the FwRF is applied to implement on Enzyme, Ion Channel, GPCR, and Nuclear Receptor data sets. The results of the five-fold cross-validation experiment show that the prediction accuracy of this approach reaches 91.68%, 88.11%, 84.72% and 78.33% on four benchmark data sets, respectively. To further validate the performance of the RFDTI, we compare it with other excellent methods and Support Vector Machine (SVM) model. In addition, 7 of the 10 highest predictive scores in predicting novel DTIs were validated by relevant databases. The experimental results of cross-validation indicated that RFDTI is feasible in predicting the relationship among drugs and target, and can provide help for the discovery of new candidate drugs.

bioinformatics↗

bioRxiv2019

Cep57 and Cep57l1 cooperate to recruit the Cep63-Cep152 complex for centriole biogenesis

Cep152 and Cep63 act as the cradle and recruit Plk4 to initiate the centriole biogenesis in a mother centriole dependent manner. However, how the Cep152-Cep63 complex is targeted to the proximal end of mother centrioles is unclear. In this study, we show that Cep57 and its paralog, Cep57l1, colocalize with Cep63 and Cep152 at the proximal end of mother centrioles in both cycling cells and differentiated multiciliated cells. Both Cep57 and Cep57l1 associate with the Cep152-Cep63 complex by directly binding to the centrosomal targeting region of Cep63. Co-depletion of Cep57 and Cep57l1 blocks the loading of Cep63-Cep152 to the centriole and subsequently prevents centriole duplication. We propose that Cep57 and Cep57l1 act together to ensure the recruitment of the Cep63-Cep152 complex to the mother centrioles for procentriole formation. Summary statementCep57 and its paralog, Cep57l1, cooperatively act as a molecular scaffold to recruit the Cep63-Cep152 cradle to the proximal end of centrioles in the early stages of centriole duplication.

cell biology↗

bioRxiv2019

Distinct architecture and composition of mouse axonemal radial spoke head revealed by cryo-EM

The radial spoke (RS) transmits mechanochemical signals from the central pair apparatus (CP) to axonemal dynein arms to coordinate ciliary motility. The RS head, directly contacting with CP, differs dramatically in morphology between protozoan and mammal. Here we show the murine RS head is compositionally distinct from the Chlamydomonas one. Our reconstituted murine RS head core complex consists of Rsph1, Rsph3b, Rsph4a, and Rsph9, lacking Rsph6a whose orthologue exists in the Chlamydomonas RS head. We present the unprecedented cryo-EM structure of RS head core complex at 4.5-[A] resolution and identified the subunit location and their interaction network. In this complex, Rsph3b, Rsph4a, and Rsph9 forms a compact body with Rsph4a serving possibly as an assembly scaffold and Rsph3b in a location that might link the head with stalk. Interestingly, two Rsph1 subunits constitute the two stretching-arms possibly for optimized RS-CP interaction. We also propose a sawtooth model for the RS-CP interaction. Our study suggests that the RS head experiences profound remodeling to probably comply with both structural and functional alterations of the axoneme during evolution.

cell biology↗

bioRxiv2019

An upstream open reading frame represses translation of chicken PPARγ transcript variant 1

Peroxisome proliferator-activated receptor {gamma} (PPAR{gamma}) is a master regulator of adipogenesis. The PPAR{gamma} gene produces various transcripts with different 5'-untranslated regions (5' UTRs) because of alternative promoter usage and splicing. The 5' UTR plays important roles in posttranscriptional gene regulation. However, to date, the regulatory role and underlying mechanism of 5' UTRs in the posttranscriptional regulation of PPAR{gamma} expression remain largely unclear. In this study, we investigated the effects of 5' UTRs on posttranscriptional regulation using reporter assays. Our results showed that the five PPAR{gamma} 5' UTRs exerted different effects on reporter gene activity. Bioinformatics analysis showed that chicken PPAR{gamma} transcript 1 (PPAR{gamma}1) possessed an upstream open reading frame (uORF) in its 5' UTR. Mutation analysis showed that a mutation in the uORF led to increased Renilla luciferase activity and PPAR{gamma} protein expression, but decreased Renilla luciferase and PPAR{gamma}1 mRNA expression. mRNA stability analysis using real-time RT-PCR showed that the uORF mutation did not interfere with mRNA stability, but promoter activity analysis of the cloned 5' UTR showed that the uORF mutation reduced promoter activity. Furthermore, in vitro transcription/translation assays demonstrated that the uORF mutation markedly increased the translation of PPAR{gamma}1 mRNA. Collectively, our results indicate that the uORF represses the translation of chicken PPAR{gamma}1 mRNA.

molecular biology↗

bioRxiv2019

PointSite: a point cloud segmentation tool for identication of protein ligand binding atoms

Accurate identifications of ligand binding sites (LBS) on protein structure is critical for understanding protein function and designing structure-based drug. As the previous pocket-centric methods are usually based on the investigation of pseudo surface points (PSPs) outside the protein structure, thus inherently cannot incorporate the local connectivity and global 3D geometrical information of the protein structure. In this paper, we propose a novel point clouds segmentation method, PointSite, for accurate identification of protein ligand binding atoms, which performs protein LBS identification at the atom-level in a protein-centric manner. Specifically, we first transfer the original 3D protein structure to point clouds and then conduct segmentation through Submanifold Sparse Convolution (SSC) based U-Net. With the fine-grained atom-level binding atoms representation and enhanced feature learning, PointSite can outperform previous methods in atom-IoU by a large margin. Furthermore, our segmented binding atoms can work as a filter on predictions achieved by previous pocket-centric approaches, which significantly decreases the false-positive of LBS candidates. Through cascaded filter and re-ranking aided by the segmented atoms, state-of-the-art performance can be achieved over various canonical benchmarks and CAMEO hard targets in terms of the commonly used DCA criteria. Our code is publicly available through https://github.com/PointSite.

bioinformatics↗

bioRxiv2019

Measurement of organ-specific and acute-phase blood protein levels in early Lyme disease

Lyme disease results from infection of humans with the spirochete Borrelia burgdorferi. The first and most common clinical manifestation is the circular, inflamed skin lesion referred to as erythema migrans; later manifestations result from infections of other body sites. Laboratory diagnosis of Lyme disease can be challenging in patients with erythema migrans because of the time delay in the development of specific diagnostic antibodies against Borrelia. Reliable blood biomarkers for the early diagnosis of Lyme disease in patients with erythema migrans are needed. Here, we performed selected reaction monitoring, a targeted mass spectrometry-based approach, to measure selected proteins that 1) are known to be predominantly expressed in one organ (i.e., organ-specific blood proteins) and whose blood concentrations may change as a result of Lyme disease, or 2) are involved in acute immune responses. In a longitudinal cohort of 40 Lyme disease patients and 20 healthy controls, we identified 10 proteins with significantly altered serum levels in patients at the time of diagnosis, and we also developed a 10-protein panel identified through multivariate analysis. In an independent cohort of patients with erythema migrans, six of these proteins, APOA4, C9, CRP, CST6, PGLYRP2 and S100A9, were confirmed to show significantly altered serum levels in patients at time of presentation. Nine of the 10 proteins from the multivariate panel were also verified in the second cohort. These proteins, primarily innate immune response proteins or proteins specific to liver, skin or white blood cells, may serve as candidate blood biomarkers requiring further validation to aid in the laboratory diagnosis of early Lyme disease.

systems biology↗

bioRxiv2019

Differential protein metabolism and regeneration in hypertrophic diaphragm and atrophic gastrocnemius muscles in hibernating Daurian ground squirrels

Whether differences in regulation of protein metabolism and regeneration are involved in the different phenotypic adaptation mechanisms of muscle hypertrophy and atrophy in hibernators? Two fast-type muscles (diaphragm and gastrocnemius) in summer active and hibernating Daurian ground squirrels were selected to detect changes in cross-sectional area (CSA), fiber type distribution, and protein expression indicative of protein synthesis metabolism (protein expression of P-Akt, P-mTORC1, P-S6K1, and P-4E-BP1), protein degradation metabolism (MuRF1, atrogin-1, calpain-1, calpain-2, calpastatin, desmin, troponin T, Beclin1, and LC3-II), and muscle regeneration (MyoD, myogenin, and myostatin). Results showed the CSA of the diaphragm muscle increased significantly by 26.1%, whereas the CSA of the gastrocnemius muscle decreased significantly by 20.4% in the hibernation group compared with the summer active group. Both muscles displayed a significant fast-to-slow fiber-type transition in hibernation. Our study further indicated that increased protein synthesis, decreased protein degradation, and increased muscle regeneration potential contributed to diaphragm muscle hypertrophy, whereas decreased protein synthesis, increased protein degradation, and decreased muscle regeneration potential contributed to gastrocnemius muscle atrophy. In conclusion, the differences in muscle regeneration and regulatory pattern of protein metabolism may contribute to the different adaptive changes observed in the diaphragm and gastrocnemius muscles of ground squirrels.

physiology↗

bioRxiv2019

Integration of eye-centered and landmark-centered codes in frontal eye field gaze responses

The visual system is thought to separate egocentric and allocentric representations, but behavioral experiments show that these codes are optimally integrated to influence goal-directed movements. To test if frontal cortex participates in this integration process, we recorded primate frontal eye field (FEF) activity during a cue-conflict memory delay saccade task. To dissociate egocentric and allocentric coordinates, we surreptitiously shifted a visual landmark during the delay period, causing saccades to deviate by 37% in the same direction. To assess the cellular mechanisms, we fit neural response fields against an egocentric (eye centered target-to-gaze) continuum, and an allocentric shift (eye-to-landmark centered) continuum. Initial visual responses best fit target position. Motor responses (after the landmark shift) predicted future gaze position but embedded within the motor code was a 29% shift toward allocentric coordinates. This shift appeared transiently in memory-related visuomotor activity, and then reappeared in motor activity before saccades. Notably, fits along the egocentric and allocentric shift continua were initially independent, but became correlated just before the motor burst. Overall, these results implicate frontal cortex in the integration of egocentric and allocentric visual information for goal-directed action, and demonstrate the cell-specific, temporal progression of signal multiplexing for this process in the gaze system.

neuroscience↗

bioRxiv2019

A new strategy for selection of crop cultivated varieties based on yield factors driving and meteorological prediction

Meteorological conditions, as an uncontrollable factor, have a direct impact on the growth and yield of crops in the field. Its particularly important to choose the cultivated varieties suitable for environmental change. As for this problem, a new strategy for selection of crop cultivated varieties is proposed in this paper. The experiment selected Harbin pilot (Heilongjiang, China) and Changchun pilot (Jilin, China). In the experiment, the cultivated varieties suitable for forecasting meteorological conditions in 2015 could be provided according to the meteorological data of 2005-2014 years and the yield data of different soybean varieties in Harbin pilot. The same method was also taken in Changchun pilot. The results showed that the recommended varieties of Harbin pilot and Changchun pilot had 70% and 50% probability respectively for selection of soybean cultivated varieties when the yield evaluation trait was 100-seed-weight. As for seed weight per plant, the possibility of more than 60% of the recommended varieties in two pilots is to provide prejudgment. Therefore, this method can help to improve the current situation of the impact of uncontrollable meteorological conditions on crop yield and also can be applied to the selection of any other crops cultivated varieties under a certain environment.

plant biology↗

bioRxiv2019

Approaches for integrating heterogeneous RNA-seq data reveals cross-talk between microbes and genes in asthmatic patients

Sputum induction is a non-invasive method to evaluate the airway environment, particularly for asthma. RNA sequencing (RNAseq) can be used on sputum, but it can be challenging to interpret because sputum contains a complex and heterogeneous mixture of human cells and exogenous (microbial) material. In this study, we developed a methodology that integrates dimensionality reduction and statistical modeling to grapple with the heterogeneity. We use this to relate bulk RNAseq data from 115 asthmatic patients with clinical information, microscope images, and single-cell profiles. First, we mapped sputum RNAseq to human and exogenous sources. Next, we decomposed the human reads into cell-expression signatures and fractions of these in each sample; we validated the decomposition using targeted single-cell RNAseq and microscopy. We observed enrichment of immune-system cells (neutrophils, eosinophils, and mast cells) in severe asthmatics. Second, we inferred microbial abundances from the exogenous reads and then associated these with clinical variables -- e.g., Haemophilus was associated with increased white blood cell count and Candida, with worse lung function. Third, we applied a generative model, Latent Dirichlet allocation (LDA), to identify patterns of gene expression and microbial abundances and relate them to clinical data. Based on this, we developed a method called LDA-link that connects microbes to genes using reduced-dimensionality LDA topics. We found a number of known connections, e.g. between Haemophilus and the gene IL1B, which is highly expressed by mast cells. In addition, we identified novel connections, including Candida and the calcium-signaling gene CACNA1E, which is highly expressed by eosinophils. These results speak to the mechanism by which gene-microbe interactions contribute to asthma and define a strategy for making inferences in heterogeneous and noisy RNAseq datasets.

genomics↗

bioRxiv2019

Assessing the utility of MAGNETO to control neuronal excitability in the somatosensory cortex

Magnetic neuromodulation has outstanding promise for the development of novel neural interfaces without direct physical intervention with the brain. Here we tested the utility of Magneto in the adult somatosensory cortex by performing whole-cell intracellular recordings in vitro and extracellular recordings in freely moving mice. Results show that magnetic stimulation does not alter subthreshold membrane excitability or contribute to the generation of action potentials in virally transduced neurons expressing Magneto.

neuroscience↗