Biology subjects

Zhu, T.

Publications and source records attributed to Zhu, T..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

RGS10 deficiency facilitates distant metastasis by inducing epithelial-mesenchymal transition in breast cancer

AbstractDistant metastasis is the major cause of death in patients with breast cancer. Epithelial-mesenchymal transition (EMT) contributes to breast cancer metastasis. Regulator of G protein-signaling (RGS) proteins modulate metastasis in various cancers. This study identified a novel role for RGS10 in EMT and metastasis in breast cancer. RGS10 protein levels were significantly lower in breast cancer tissues compared to normal breast tissues, and deficiency in RGS10 protein predicted a worse prognosis in patients with breast cancer. RGS10 protein levels were lower in the highly aggressive cell line MDA-MB-231 than in the poorly aggressive, less invasive cell lines MCF7 and SKBR3. Silencing RGS10 in SKBR3 cells enhanced EMT and caused SKBR3 cell migration and invasion. The ability of RGS10 to suppress EMT and metastasis in breast cancer was dependent on lipocalin-2 and miR-539-5p. These findings identify RGS10 as a tumor suppressor, prognostic biomarker, and potential therapeutic target for breast cancer.

cancer biology↗

bioRxiv2024

Novel transcriptional activator TAC3 regulates age-dependent floral transition in Chinese fir

Plant undergo juvenile-to-adult transition to become competent for age-dependent floral induction and reproductive transition, which is of great significance for improving the seed quality and maintaining desirable genetic traits of Chinese fir, but the underlying molecular mechanize still remains unknown. Here, we investigated the function of our newly identified spermatophyte specific transcriptional co-activator TAC3 (Transcriptional Activator in Chinese fir 3) and its homologues (TAL1) in the model plant Arabidopsis. Both TAC3 and TAL1 can negatively regulate flowering, and activate miR156 expression to delay the phase transition. Moreover, we found that HDA9 and its its homologues in Chinese fir, ClHDA9, can directly binding to the promoter region of MIR156A and ClMIR156A, respectively. Directly interaction with ClHDA9 and HDA9 are necessary for the transcriptional activation of TAC3 and TAL1 on miR156, respectively. TAC3 and TAL1 also involve in the chromatin remodeling, shown as up-regulated H3K27ac level within the promoter region of ClMIR156A and MIR156A. Together, this work shows that TAC3 and its homologues are a new group of transcriptional co-activator that involving in aging-dependent flowering signal pathway of both angiosperms and gymnosperms.

plant biology↗

bioRxiv2024

Endoplasmic reticulum stress delays choroid development in the HCAR1 knock-out mouse

The sub-retina, composed of the choroid and the retinal pigment epithelium (RPE), bears a critical role in proper vision. In addition to phagocytosis of photoreceptor debris, the RPE shuttles oxygen and nutrients to the neuroretina. For their own energy production, RPE cells mainly rely on lactate, a major by-product of glycolysis. Lactate in turn is believed to convey most of its biological effects via the HCAR1 receptor. Here, we show that the lactate-specific receptor, HCAR1, is exclusively expressed in the RPE cells and that Hcar1-/- mice exhibit a substantially thinner choroid vasculature during development. Notably, the angiogenic properties of lactate on the choroid are impacted by the absence of Hcar1. Hcar1-deficient mice exhibit elevated endoplasmic reticulum (ER) stress along with eIF2 phosphorylation, a significant decrease in the global protein translation rate, and a lower proliferation rate of choroidal vasculature. Strikingly, inhibition of the Integrated Stress Response using an inhibitor of eIF2 phosphorylation (ISRIB) restores protein translation and rescues choroidal thinning. These results provide evidence that lactate signalling via HCAR1 is important for choroidal development/angiogenesis and highlight the importance of this receptor in establishing mature vision.

cell biology↗

bioRxiv2023

Functional Connectivity, Tissue Microstructure And T2 At 11.1 Tesla Distinguishes Neuroadaptive Differences In Two Contusive Brain Injury Models In Rats

Traumatic brain injuries (TBIs), particularly contusive types, are associated with disruptions in neuronal communication due to focal and diffuse axonal injury, as well as alterations in the neuronal chemical environment. These changes can negatively impact neuronal networks beyond the primary injury site. In this Translational Outcomes Project in NeuroTrauma (TOP-NT) UG3 phase study, we sought to use multimodal neuroimaging biomarker approach to assess functional connectivity and brain tissue microstructure, along with T2 relaxometry, in two experimental rat models of TBI: controlled cortical impact (CCI) and lateral fluid percussive injury (LFPI). Rats underwent imaging using an 11.1 Tesla scanner at 2 and 30 days post-injury. Naive controls were scanned once to establish baseline comparisons for both TBI groups. Imaging modalities included functional magnetic resonance imaging (fMRI), diffusion-weighted imaging (DWI), and multi-echo T2 imaging. fMRI data were analyzed to evaluate functional connectivity across lateral and medial regions of interest (ROIs) in the cortical mantle, hippocampus, and dorsal striatum. DWI scans were used to generate maps of fractional anisotropy (FA) and mean, axial, and radial diffusivities (MD, AD, RD), focusing on cortical and white matter (WM) regions near the injury epicenter. Our findings revealed significantly increased contralateral intra-cortical connectivity at 2 days post-injury in both CCI and LFPI models, localized to similar cortical areas. This increased connectivity persisted at day 30 in the CCI model but not in LFPI. Changes in WM and cortical FA and diffusivities were observed in both models, with WM alterations predominating in CCI and cortical changes being more pronounced in LFPI. These results highlight the utility of multimodal MR imaging for characterizing distinct injury mechanisms in contusive and skull-penetrating TBI models.

neuroscience↗

bioRxiv2023

cisDynet: an integrated platform for modeling gene-regulatory dynamics and networks

Chromatin accessibility sequencing has been widely used for uncovering genetic regulatory mechanisms and inferring gene regulatory networks. However, effectively integrating large-scale chromatin accessibility datasets has posed a significant challenge. This is due to the lack of a comprehensive end-to-end solution, as many existing tools primarily emphasize data pre-processing and overlook downstream analyses. To bridge this gap, we have introduced cisDynet, a holistic solution that combines streamlined data pre-processing using Snakemake and R functions with advanced downstream analysis capabilities. cisDynet excels in conventional data analyses, encompassing peak statistics, peak annotation, differential analysis, motif enrichment analysis and more. Additionally, it allows to perform sophisticated data exploration such as tissue-specific peak identification, time-course data fitting, integration of RNA-seq data to establish peak-to-gene associations, constructing regulatory networks, and conducting enrichment analysis of GWAS variants. As a proof of concept, we applied cisDynet to re-analyze the comprehensive ATAC-seq datasets across various tissues from the ENCODE project. The analysis successfully delineated tissue-specific open chromatin regions (OCRs), established connections between OCRs and target genes, and effectively linked these discoveries with 1,861 GWAS variants. Furthermore, cisDynet was instrumental in dissecting the time-course open chromatin data of mouse embryonic development, revealing the dynamic behavior of OCRs over time and identifying key transcription factors governing differentiation trajectories. In summary, cisDynet offers researchers a user-friendly solution that minimizes the need for extensive coding, ensures the reproducibility of results, and greatly simplifies the exploration of epigenomic data. Graphical AbstractcisDynet enables the exploration of the cis-regulatory chromatin dynamics and networks. It offers a streamlined preprocessing pipeline built on Snakemake, along with an R package for advanced downstream data analysis and visualization. It is free to access on GitHub (https://github.com/tzhu-bio/cisDynet). HighlightsO_LIThe cisDynet enables comprehensive and efficient processing of chromatin accessibility data, including pre-processing, advanced downstream data analysis and visualization. C_LIO_LIcisDynet provides a range of analytical features such as processing of time course data, co-accessibility analysis, linking OCRs to genes, building regulatory networks, and GWAS variant enrichment analysis. C_LIO_LIcisDynet simplifies the identification of tissue/cell type-specific OCRs or dynamic OCR changes over time and facilitates the integration of RNA-seq data to depict temporal trajectories. C_LI

bioinformatics↗

bioRxiv2023

A three-node Turing gene circuit forms periodic spatial patterns in bacteria

Turing patterns1 are well-known self-organising systems that can form spots, stripes, or labyrinths. They represent a major theory of patterning in tissue organisation, due to their remarkable similarity to some natural patterns, such as skin pigmentation in zebrafish2, digit spacing3,4, and many others. The involvement of Turing patterns in biology has been debated because of their stringent fine-tuning requirements, where patterns only occur within a small subset of parameters5,6. This has complicated the engineering of a synthetic gene circuit for Turing patterns from first principles, even though natural genetic Turing networks have been successfully identified4,7. Here, we engineered a synthetic genetic reaction-diffusion system where three nodes interact according to a non-classical Turing network with improved parametric robustness6. The system was optimised in E. coli and reproducibly generated stationary, periodic, concentric stripe patterns in growing colonies. The patterns were successfully reproduced with a partial differential equation model, in a parameter regime obtained by fitting to experimental data. Our synthetic Turing system can contribute to novel nanotechnologies, such as patterned biomaterial deposition8,9, and provide insights into developmental patterning programs10.

synthetic biology↗

bioRxiv2023

Spatial transcriptomics and single-nucleus RNA sequencing reveal a transcriptomic atlas of adult human spinal cord

Despite the recognized importance of the spinal cord in sensory processing, motor behaviors, and neural diseases, the underlying organization of neuronal clusters and their spatial location remain elusive. Recently, several studies have attempted to define the neuronal types and functional heterogeneity in the spinal cord using single-cell or single-nucleus RNA sequencing in animal models or developing humans. However, molecular evidence of cellular heterogeneity in the adult human spinal cord is limited. Here, we classified spinal cord neurons into 21 subclusters and determined their distribution from nine human donors using single-nucleus RNA sequencing and spatial transcriptomics. Moreover, we compared the human findings with previously published single-nucleus data of the mouse adult spinal cord, which revealed an overall similarity in the neuronal composition of the spinal cord between the two species while simultaneously highlighting some degree of heterogeneity. Additionally, we examined the sex differences in the spinal neuronal subclusters. Several genes, such as SCN10A and HCN1, showed sex differences in motor neurons. Finally, we classified human dorsal root ganglia (DRG) neurons using spatial transcriptomics and explored the putative interactions between DRG and spinal cord neuronal subclusters. In summary, these results illustrate the complexity and diversity of spinal neurons in humans and provide an important resource for future research to explore the molecular mechanisms underlying spinal cord physiology and diseases.

neuroscience↗

bioRxiv2023

Structure-based self-supervised learning enables ultrafast prediction of stability changes upon mutation at the protein universe scale

Predicting free energy changes ({Delta}{Delta}G) is of paramount significance in advancing our comprehension of protein evolution and holds profound implications for protein engineering and pharmaceutical development. Traditional methods, however, often suffer from limitations such as sluggish computational speed or heavy reliance on biased training datasets. These challenges are magnified when aiming for accurate {Delta}{Delta}G prediction across the vast universe of protein sequences. In this study, we present Pythia, a self-supervised graph neural network tailored for zero-shot {Delta}{Delta}G predictions. In comparative benchmarks with other self-supervised pre-training models and force field-based methods, Pythia outshines its contenders with superior correlations while operating with the fewest parameters, and exhibits a remarkable acceleration in computational speed, up to 105-fold. The efficacy of Pythia is corroborated through its application in predicting thermostable mutations of limonene epoxide hydrolase (LEH) with significant higher experimental success rates. This efficiency propels the exploration of 26 million high-quality protein structures. Such a grand-scale application signifies a leap forward in our capacity to traverse the protein sequence space and potentially enrich our insights into the intricacies of protein genotype-phenotype relationships. We provided a web app at https://pythia.wulab.xyz for users to conveniently execute predictions. Keywords: self-supervised learning, protein mutation prediction, protein thermostability

bioinformatics↗

bioRxiv2023

Correlation of a pan-tissue epigenetic mitotic clock with tumor cell-of-origin

The cumulative number of stem cell divisions in a tissue, known as mitotic age, is thought to be a major determinant of cancer-risk. Somatic mutational and DNA methylation (DNAm) clocks are promising tools to molecularly track mitotic age, yet their relationship is underexplored and their potential for cancer risk prediction in normal tissues remains to be demonstrated. Here we build and validate an improved pan-tissue DNAm counter of total mitotic age called stemTOC. We demonstrate that stemTOCs mitotic age proxy increases with the tumor cell-of-origin fraction in each of 15 cancer-types, in precancerous lesions, and in normal tissues exposed to major cancer risk factors. Extensive benchmarking against 6 other mitotic counters shows that stemTOC compares favorably, specially in the preinvasive and normal-tissue contexts. By cross-correlating stemTOC to two clock-like somatic mutational signatures, we confirm the mitotic-like nature of only one of these. Our data points towards DNAm as a promising molecular substrate for detecting mitotic-age increases in normal tissues and precancerous lesions, and hence for developing cancer-risk prediction strategies.

cancer biology↗

bioRxiv2023

Predicting Gene Spatial Expression and Cancer Prognosis: An Integrated Graph and Image Deep Learning Approach Based on HE Slides

Interpreting the tumor microenvironment (TME) heterogeneity within solid tumors presents a cornerstone for precise disease diagnosis and prognosis. However, while spatial transcriptomics offers a wealth of data, ranging from gene expression and spatial location to corresponding Hematoxylin and Eosin (HE) images, to explore the TME of various cancers, its high cost and demanding infrastructural needs significantly limit its clinical application, highlighting the need for more accessible alternatives. To bridge this gap, we introduce the Integrated Graph and Image Deep Learning (IGI-DL) model. This innovation, a fusion of Convolutional Neural Networks and Graph Neural Networks, is designed to predict gene spatial expression using HE images. The IGI-DL model outperforms its predecessors in analyzing colorectal cancer (CRC), breast cancer, and cutaneous squamous cell carcinoma (cSCC) by leveraging both pixel intensity and structural features in images. Significantly, across all cancer types, the IGI-DL model enhances the mean correlation of the top five genes by an average of 0.125 in internal and external test sets, rising from 0.306 to 0.431, surpassing existing state-of-the-art (SOTA) models. We further present a novel risk score derived from a super-patch graph, where gene expression predicted by IGI-DL serves as node features. Demonstrating superior prognostic accuracy, this risk score, with a C-index of 0.713 and 0.741 for CRC and breast cancer, supersedes traditional HE-based risk scores. In summary, the approach augments our understanding of the TME from the aspect of histological images, portending a transformation in cancer prognostics and treatment planning and ushering in a new era of personalized and precision oncology.

bioinformatics↗

bioRxiv2023

Accelerated somatic mutation calling for whole-genome and whole-exome sequencing data from heterogenous tumor samples

Accurate detection of somatic mutations in DNA sequencing data is a fundamental prerequisite for cancer research. Previous analytical challenge was overcome by consensus mutation calling from four to five popular callers. This, however, increases the already nontrivial computing time from individual callers. Here, we launch MuSE2.0, powered by multi-step parallelization and efficient memory allocation, to resolve the computing time bottleneck. MuSE2.0 speeds up 50 times than MuSE1.0 and 8-80 times than other popular callers. Our benchmark study suggests combining MuSE2.0 and the recently expedited Strelka2 can achieve high efficiency and accuracy in analyzing large cancer genomic datasets.

bioinformatics↗

bioRxiv2023

Pan-genome and Haplotype Map of Cultivars and Their Wild Ancestors Provides Insights into Adaptive Evolution of Cassava (Manihot esculenta Crantz)

Cassava is the most important starch sources, a tropical model crop. We constructed nearly T2T genomes of cultivar AM560, wild ancestors FLA4047 and W14, pan-genome of 24 representatives and a clarified evolutionary tree with 486 accessions. Comparison of SVs and SNVs between the ancestors and cultivated cassavas revealed predominant expansion, contraction of genes and gene families. Significantly selective sweeping occurred in the cassava genomes in 122 footprints with 1,519 candidate domestication genes. We identified selective mutations in MeCSK and MeFNR3 promoting photoreaction associated with MeNADP-ME of C4 assimilation in modern cassava. Co-evolved retardation of floral primordia and initiation of storage roots arose from MeCOL5 mutants with altered bindings to MeFT1, MeFT2 and MeTFL2. MebHLHs evolved to regulate the biosynthesis, transport and endogenous remobilization of cyanogenic glucosides, with new functionalities of MeMATE1, MeGTR in selected sweet cassava. These findings enhanced comprehensive knowledge and database on the evolution and breeding of cassava. HIGHLIGHTSO_LIThree nearly T2T cassava genomes of cultivar AM560 and its wild ancestors FLA4047 and W14. C_LIO_LIA species-level cassava panSV haplotype map across 346,322 structural variations over 31,362 gene families and 96,032,008 SNPs and InDels variations globally and a clarified evolutionary tree with 486 accessions. C_LIO_LISelective mutations in MeCSK and MeFNR3 promoted photoreaction associated with MeNADP-ME of C4 assimilation shaped the C3-C4 intermediate photosynthesis of modern cassava. C_LIO_LICoevolution of floral primordia contrary to initiated storage root is pivotal for the domestication of cassava, and arose from MeCOL5 mutants altered the binding with MeFT1, MeFT2 (SP6A), and MeTFL2. C_LI

systems biology↗

bioRxiv2023

SWI/SNF chromatin remodeling determines brassinosteroid-induced transcriptional activation

The brassinosteroid (BR) hormone is a central modulator of plant growth, development, and responses to stresses by activating or repressing the expression of thousands of genes through the transcription factor BRASSINAZOLE-RESISTANT 1 (BZR1) and its homologues. However, the molecular mechanism that determines the transcriptional activation versus repression activity of BZR1 remains largely unclear. Here, we show that BZR1-responsive transcriptional activation at thousands of loci requires the Switch defective/sucrose non-fermentable (SWI/SNF)-complexes-mediated chromatin accessibility regulation. BR-activated BZR1 controls the activation or repression of thousands of BZR1 target genes through reprograming genome-wide chromatin accessibility landscape in Arabidopsis thaliana. BZR1 physically interacts with the BRAHMA (BRM)-Associated SWI/SNF complexes (BAS), co-localizes with BRM on the genome, and enhances BRM occupancy at sites of increased accessibility by BR. Loss of BRM abrogates the capacity of BZR1 to increase but not decrease chromatin accessibility, blocks BR-induced hypocotyl elongation, and diminishes BZR1-mediated transcriptional activation rather than repression. Together, our work reveals that the BAS chromatin remodeling complex is a critical epigenetic regulatory partner in dictating BZR1-mediated transcriptional activation ability, thus providing a long sought mechanistic explanation for how BR signaling activates gene transcription in shaping diverse developmental programs. TeaserBZR1-responsive transcriptional activation activity at thousands of loci requires the SWI/SNF-complexes-mediated chromatin accessibility regulation.

plant biology↗

bioRxiv2023

A novel bacterial signal transduction system specifically senses and responds to peptidoglycan damage

The peptidoglycan (PG) layer is a mesh-like structure within the cell envelope essential for maintenance of cell shape and resistance to osmotic stress, and therefore is a primary target of many important and widely used antibiotics, such as {beta}-lactams. In Gram-negative bacteria, while signal transduction systems that monitor the state of the inner- and outer-membranes have been extensively studied and well understood, much less is known about how cells sense and respond to PG-targeting stresses. Here we show that a novel bacterial two-component system (PghKR) from Shewanella oneidensis is capable of sensing and responding to PG damage. This system is specifically activated in cells exposed to various PG-targeting antibiotics or carrying a defect in PG synthesis, resulting in induced expression of blaA and relV, which encode a {beta}-lactamase conferring resistance to {beta}-lactams and a small ppGpp synthetase responsible for antibiotic tolerance, respectively. Intriguingly, the PghKR homologs are widespread among several classes of Proteobacteria and the periplasmic domain of sensor kinase PghK contains a family 9 carbohydrate-binding module that is required for signal perception, implying that the signals could be the glycan fragments of PG. Overall, our results provide critical insights into the regulation of PG homeostasis in Gram-negative bacteria.

microbiology↗

bioRxiv2023

A meta-analysis of immune cell fractions at high resolution reveals novel associations with common phenotypes and health outcomes

AbstractO_ST_ABSBackgroundC_ST_ABSChanges in cell-type composition of complex tissues are associated with a wide range of diseases, environmental risk factors and may be causally implicated in disease development and progression. However, these shifts in cell-type fractions are often of a low magnitude, or involve similar cell-subtypes, making their reliable identification challenging. DNA methylation profiling in a tissue like blood is a promising approach to discover shifts in cell-type abundance, yet studies have only been performed at a relatively low cellular resolution and in isolation, limiting their power to detect these shifts in tissue composition. MethodsHere we derive a DNA methylation reference matrix for 12 immune cell-types in human blood and extensively validate it with flow-cytometric count data and in whole-genome bisulfite sequencing data of sorted cells. Using this reference matrix and Stouffers method, we perform a meta-analysis encompassing 25,629 blood samples from 22 different cohorts, to comprehensively map associations between the 12 immune-cell fractions and common phenotypes, including health outcomes. ResultsOur meta-analysis reveals many associations with age, sex, smoking and obesity, many of which we validate with single-cell RNA-sequencing. We discover that T-regulatory and naive T-cell subsets are higher in women compared to men, whilst the reverse is true for monocyte, natural killer, basophil and eosinophil fractions. In a large subset encompassing 5000 individuals we find associations with stress, exercise, sleep and health outcomes, revealing that naive T-cell and B-cell fractions are associated with a reduced risk of all-cause mortality independently of age, sex, race, smoking, obesity and alcohol consumption. We find that decreased natural killer cell counts are associated with smoking, obesity and stress levels, whilst an increased count correlates with exercise, sleep and a reduced risk of all-cause mortality. ConclusionsThis work derives and extensively validates a high resolution DNAm reference matrix for blood, and uses it to generate a comprehensive map of associations between immune cell fractions and common phenotypes, including health outcomes. AvailabilityThe 12 immune cell-type DNAm reference matrices for Illumina 850k and 450k beadarrays alongside tools for cell-type fraction estimation are freely available from our EpiDISH Bioconductor R-package http://www.bioconductor.org/packages/devel/bioc/html/EpiDISH.html

bioinformatics↗

bioRxiv2023

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or over-treatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomise prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

cancer biology↗

bioRxiv2023

Defective mechanosensory transduction of the new inner hair cells prevents hearing recover in the damaged cochlea

ABSTRACTHearing loss is a major health problem worldwide. Numerous attempts at regenerating functional hair cells (HCs) have been unsuccessful, but little is known about the main barrier that prevents us from achieving it and improving the hearing ability after damage. Here, we developed an in vivo genetic mouse model, by which the inner HCs (IHCs), the primary sound receptors innervated by the auditory neurons, were specifically damaged and the neighboring nonsensory supporting cells (SCs) were transformed into IHCs by ectopic expression of transient Atoh1 and permanent Tbx2. Despite [~]477 new IHCs were regenerated per cochlea and their differentiation status was more advanced than reported previously, no significant hearing improvement was achieved. By taking advantage of this unique model, we further found that the new IHCs expressed the functional marker vGlut3, harbored the similar transcriptomic profiles and electrophysiological properties as the endogenous IHCs. However, the mechanosensory transduction (MET) current could not be recorded in the new IHCs. Thus, our study indicated that the defective MET should be the main barrier that stops us from restoring the hearing capacity in the damaged cochlea and would pave the way for regenerating IHCs in vivo.

neuroscience↗

bioRxiv2023

Wireless in vivo Recording of Cortical Activity by an Ion-Sensitive Field Effect Transistor

Wireless brain technologies are empowering basic neuroscience and clinical neurology by offering new platforms that minimize invasiveness and refine possibilities during electrophysiological recording and stimulation. Despite their advantages, most systems require on-board power supply and sizeable transmission circuitry, enforcing a lower bound for miniaturization. Designing new minimalistic architectures that can efficiently sense neurophysiological events will open the door to standalone microscale sensors and minimally invasive delivery of multiple sensors. Here we present a circuit for sensing ionic fluctuations in the brain by an ion-sensitive field effect transistor that detunes a single radiofrequency resonator in parallel. We establish sensitivity of the sensor by electromagnetic analysis and quantify response to ionic fluctuations in vitro. We validate this new architecture in vivo during hindpaw stimulation in rodents and verify correlation with local field potential recordings. This new approach can be implemented as an integrated circuit for wireless in situ recording of brain electrophysiology.

neuroscience↗