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Liu, L.

Publications and source records attributed to Liu, L..

At least 451 records · Page 25Linked to original sources
bioRxiv2023

Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data

RNA-sequencing technology provides an effective tool for understanding miRNA regulation in complex human diseases, including cancers. A large number of computational methods have been developed to make use of bulk and single-cell RNA-sequencing data to identify miRNA regulations at the resolution of multiple samples (i.e. group of cells or tissues). However, due to the heterogeneity of individual samples, there is a strong need to infer miRNA regulation specific to individual samples to uncover miRNA regulation at single-sample resolution level. Here, we develop a framework, Scan, for scanning sample-specific miRNA regulation. Since a single network inference method or strategy cannot perform well for all types of new data, Scan incorporates 27 network inference methods and two strategies to infer tissue-specific or cell-specific miRNA regulation from bulk or single-cell RNA-sequencing data. Results on bulk and single-cell RNA-sequencing data demonstrate the effectiveness of Scan in inferring sample-specific miRNA regulation. Moreover, we have found that incorporating priori information of miRNA targets can improve the accuracy of miRNA target prediction. In addition, Scan can contribute to the clustering cells/tissues and construction of cell/tissue correlation networks. Finally, the comparison results have shown that the performance of network inference methods is likely to be data-specific, and selecting optimal network inference methods is required for more accurate prediction of miRNA targets. We have made Scan freely available to the public to help infer sample-specific miRNA regulation for new data, benchmark new network inference methods and deepen the understanding of miRNA regulation at the resolution of individual samples.

bioinformatics↗
bioRxiv2023

H3-OPT: Accurate prediction of CDR-H3 loop structures of antibodies with deep learning

Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D structures of monoclonal antibodies and nanobodies. H3-OPT combines the strengths of AlphaFold2 with a pre-trained protein language model, and provides a 2.24 [A] average RMSDC between predicted and experimentally determined CDR-H3 loops, thus outperforming other current computational methods in our non-redundant high-quality dataset. The model was validated by experimentally solving three structures of anti-VEGF nanobodies predicted by H3-OPT. We examined the potential applications of H3-OPT through analyzing antibody surface properties and antibody-antigen interactions. This structural prediction tool can be used to optimize antibody-antigen binding, and to engineer therapeutic antibodies with biophysical properties for specialized drug administration route.

bioinformatics↗
bioRxiv2023

Engineered 3D Immuno-Glial-Neurovascular Human Brain Model

Patient-specific, human-based cellular models integrating a biomimetic blood-brain barrier (BBB), immune, and myelinated neuron components are critically needed to enable accelerated, translationally relevant discovery of neurological disease mechanisms and interventions. By engineering a novel brain-mimicking 3D hydrogel and co-culturing all six major brain cell types derived from patient iPSCs, we have constructed, characterized, and utilized a multicellular integrated brain (miBrain) immuno-glial-neurovascular model with in vivo-like hallmarks inclusive of neuronal activity, functional connectivity, barrier function, myelin-producing oligodendrocyte engagement with neurons, multicellular interactions, and transcriptomic profiles. We implemented the model to study Alzheimers Disease pathologies associated with APOE4 genetic risk. APOE4 miBrains differentially exhibit amyloid aggregation, tau phosphorylation, and astrocytic GFAP. Unlike the co-emergent fate specification of glia and neurons in organoids, miBrains integrate independently differentiated cell types, a feature we harnessed to identify that APOE4 in astrocytes promotes neuronal tau pathogenesis and dysregulation through crosstalk with microglia.

neuroscience↗
bioRxiv2023

Intracellular Pseudomonas aeruginosa within the airway epithelium of Cystic Fibrosis lung tissues

RATIONALEPseudomonas aeruginosa (P.a.) is the major bacterial pathogen colonizing the airways of adult cystic fibrosis (CF) patients and causes chronic infections that persist despite antibiotic therapy. Intracellular bacteria may represent an unrecognized reservoir of bacteria that evades the immune system and antibiotic therapy. While the ability of P.a. to invade and survive within epithelial cells has been described in vitro in different epithelial cell models, evidence of this intracellular lifestyle in human lung tissues is currently lacking. OBJECTIVESTo detect and characterize intracellular P.a. in CF airway epithelium from human lung explant tissues. METHODSWe sampled the lung explant tissues from CF and non-CF patients undergoing lung transplantation and analyzed lung tissue sections for the presence of intracellular P.a. by quantitative culture and microscopy, in parallel to histopathology and airway morphometry. MEASUREMENTS AND MAIN RESULTSP.a. was isolated from the lungs of 7 CF patients undergoing lung transplantation. Microscopic assessment revealed the presence of intracellular P.a. within airway epithelial cells in 3 out of the 7 lungs analyzed, at a varying but rare frequency. We observed those events occurring in lung regions with high bacterial burden. CONCLUSIONThis is the first study describing the presence of intracellular P.a. in CF lung tissues. While intracellular P.a. in airway epithelial cells are likely relatively rare events, our findings highlight the plausible occurrence of this intracellular bacterial reservoir in chronic CF infections.

microbiology↗
bioRxiv2023

YiNet: An Integrated Traditional Chinese and Western Medicine Platform for Viral Infectious Diseases

Viral infectious diseases (VIDs) impose a heavy burden on global public health. Traditional Chinese medicine (TCM) has previously and is currently contributing to the prevention and treatment of infectious diseases. Omics and information technology enable the precise identification of virus characteristics, virus-host interactions and TCM mechanisms. In this study, we constructed the YiNet platform to better integrate the novel techniques and historical experience of TCM in infectious diseases. YiNet comprises three modules: knowledge base, database and toolkit. The YiNet knowledge base involves 43 VIDs, thereby systematically integrating the knowledge regarding viruses, host symptoms and TCM and Western medicine (commonly used chemical drugs, 6,899 herbs and 2,481 formulas). The YiNet database module includes multiple databases, comprising 57,340 genome sequences of 45,791 viral strains and 5,726 multi-omics datasets such as RNA-seq, ChIP-seq and ATAC-seq from different tissues and cell models. It also integrates 1,105 real-world TCM clinical cases. We adopted visual analysis tool to investigate pathogen-host-herb relationships. To explore pharmacological mechanisms for the core herbs, we added formula data mining and network pharmacology analysis pipelines and visualisation tools. YiNet can facilitate the mechanistic study of TCM and drug development for VIDs. The YiNet platform is publicly available at http://yinet.gene.ac/.

bioinformatics↗
bioRxiv2023

An Unsupervised Contrastive Learning Model for Data Integration of Spatially Resolved Transcriptomics

Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. Here, we propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.

bioinformatics↗
bioRxiv2023

Neuronal Connectivity as a Determinant of Cell Types and Subtypes

Classifications of single neurons at brain-wide scale is a powerful way to characterize the structural and functional organization of a brain. We acquired and standardized a large morphology database of 20,158 mouse neurons, and generated a whole-brain scale potential connectivity map of single neurons based on their dendritic and axonal arbors. With such an anatomy-morphology-connectivity mapping, we defined neuron connectivity types and subtypes (both called "c-types" for simplicity) for neurons in 31 brain regions. We found that neuronal subtypes defined by connectivity in the same regions may share statistically higher correlation in their dendritic and axonal features than neurons having contrary connectivity patterns. Subtypes defined by connectivity show distinct separation with each other, which cannot be recapitulated by morphology features, population projections, transcriptomic, and electrophysiological data produced to date. Within this paradigm, we were able to characterize the diversity in secondary motor cortical neurons, and subtype connectivity patterns in thalamocortical pathways. Our finding underscores the importance of connectivity in characterizing the modularity of brain anatomy, as well as the cell types and their subtypes. These results highlight that c-types supplement conventionally recognized transcriptional cell types (t-types), electrophysiological cell types (e-types), and morphological cell types (m-types) as an important determinant of cell classes and their identities.

neuroscience↗
bioRxiv2023

Mitochondrial DNA released by senescent cells triggers immunosuppression in cancer

DNA is a potent damage-associated molecular pattern signaling that, once in the extracellular space, triggers the activation of the innate immune system. Here we find that senescent cells release mtDNA to both the cytosol and the extracellular space. In cells undergoing cellular senescence, the release of mtDNA precedes that of nuclear DNA resulting in the activation of the cGAS/STING pathway and establishment of cellular senescence. Intriguingly, by exploiting co-culture and in vivo cross-species experiments, we show that extracellular mtDNA released by senescent tumors cells is specifically captured by polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) in the tumor microenvironment (TME). Mechanistically we find that PMN-MDSCs uptake mtDNA to enhance their immunosuppressive ability. Pharmacological inhibition of mtDNA released from senescent tumor cells blocks the PMN-MDSCs immunosuppressive activity, improving the efficacy of therapy-induced senescence (TIS) in cancer. These results reveal the crucial role of mtDNA in initiating cellular senescence and immunosuppression independently of the SASP. Thus, targeting mtDNA release-mediated pathway may hold promise to reprogram the immune suppressive microenvironment in patients treated with chemotherapy.

cell biology↗
bioRxiv2023

Transformation of value signaling in a striatopallidal circuit

The ways in which sensory stimuli acquire motivational valence through association with other stimuli is one of the simplest forms of learning. Though we have identified many brain nuclei that play various roles in reward processing, a significant gap remains in understanding how valence encoding transforms through the layers of sensory processing. To address this gap, we carried out a comparative investigation of the anteromedial olfactory tubercle (OT), and the ventral pallidum (VP) - 2 connected nuclei of the basal ganglia which have both been implicated in reward processing. First, using anterograde and retrograde tracing, we show that both D1 and D2 neurons of the anteromedial OT project primarily to the VP and minimally elsewhere. Using 2-photon calcium imaging, we then investigated how the identity of the odor and reward contingency of the odor are differently encoded by neurons in either structure during a classical conditioning paradigm. We find that VP neurons robustly encode reward contingency, but not identity, in low-dimensional space. In contrast, the OT neurons primarily encode odor identity in high-dimensional space. Although D1 OT neurons showed larger responses to rewarded odors than other odors, consistent with prior findings, we interpret this as identity encoding with enhanced contrast. Finally, using a novel conditioning paradigm that decouples reward contingency and licking vigor, we show that both features are encoded by non-overlapping VP neurons. These results provide a novel framework for the striatopallidal circuit in which a high-dimensional encoding of stimulus identity is collapsed onto a low-dimensional encoding of motivational valence.

neuroscience↗
bioRxiv2023

In vivo characterization of a secologanin transporter from Catharanthus roseus

Monoterpenoid indole alkaloid (MIA) biosynthesis in Catharanthus roseus is a paragon of the spatiotemporal complexity achievable by plant specialized metabolism. Spanning a range of tissues, four cell types, and five cellular organelles, MIA metabolism is intricately regulated and organized. This high degree of metabolic differentiation requires inter-cellular and organellar transport, which remains understudied. Here, we have fully characterized a vacuolar importer of secologanin belonging to the multidrug and toxic compound extrusion (MATE) family, named CrMATE1/SLTr. Phylogenetic analyses of MATEs suggested a role in alkaloid transport for CrMATE1, and in planta silencing in two varieties of C. roseus resulted in a shift in the secoiridoid and MIA profiles. Subcellular localization of CrMATE1 confirmed tonoplast localization. A full panel of in vivo biochemical characterization using the Xenopus laevis oocyte expression system was used to determine substrate range, directionality, and rate. We can confirm that CrMATE1 is a vacuolar importer of secologanin, rapidly transporting 1 mM of secologanin within 25 min. Notably, the absence of CrMATE1 leads to a transport bottleneck, resulting in the conversion of secologanin to its reduced form, secologanol, both in planta and in the X. laevis system. The unique substrate-specific activity of CrMATE1 showcases the utility of transporters as gatekeepers of metabolic flux, mediating the balance between anti-herbivory potency and cell homeostasis in planta. MIA and secoiridoid transporters could also be deployed in heterologous systems to guide biosynthetic pathways and improve titers of valuable and life-saving MIAs. SIGNIFICANCEWe have fully characterized CrMATE1, a multidrug and toxic compound extrusion (MATE) family transporter in Catharanthus roseus, as a vacuolar importer of secologanin. The translocation of secologanin into the vacuole is necessary for the first committed step of monoterpenoid indole alkaloid (MIA) biosynthesis.

plant biology↗
bioRxiv2023

Single-cell and spatial transcriptomics reveals the human liver immunological landscape and myeloid dysfunction in PSC.

BackgroundPrimary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease characterized by bile retention, biliary tree destruction, and progressive fibrosis leading to end stage liver disease and transplantation. There is an unmet need to understand the cellular composition of the PSC liver and how it underlies disease pathogenesis. As such, we generated a comprehensive atlas of the PSC liver and a reference healthy liver dataset using multiple multi-omic modalities and functional validation. MethodsIn this work, we employed single-cell (12,000 cells), single-nuclei (23,000 nuclei), and spatial transcriptomics (1 sample by 10x Visium and 3 samples with multi-region profiling by Nanostring GeoMx DSP) to profile the cellular ecosystem in 5 patients with PSC. Transcriptomic profiles were compared to 100k single cell transcriptomes and spatial transcriptomics controls from 24 healthy neurologically deceased donor (NDD) livers. Flow cytometry and intracellular cytokine staining was performed to validate PSC-specific differences in immune phenotype and function. ResultsPSC explants with cirrhosis of the liver parenchyma and prominent periductal fibrosis were associated with a unique population of hepatocytes which transformed to a cholangiocyte-like phenotype. These hepatocytes were surrounded by diverse immune cell populations, including monocyte-like macrophages, liver-resident and circulating natural killer (NK) cells. Inflamed cholangiocytes, fibrosis-resident hepatic stellate cells, and endothelial cells released cytokines that recruited CD4+T-cells, dendritic cells, and neutrophils to the PSC liver. Tissue-resident macrophages, by contrast, were reduced in number and exhibited a dysfunctional inflammatory response to LPS and IFN-{gamma} stimulation. ConclusionsWe present the first comprehensive atlas of the PSC liver and demonstrate hyper-activation and exhaustion-like phenotypes of myeloid cells and markers of chronic cytokine expression in late-stage PSC lesions. Lay SummaryPrimary sclerosing cholangitis (PSC) is a rare liver disease characterized by chronic inflammation and irreparable damage to the bile ducts. Due to a limited understanding of the underlying pathogenesis of disease, there remains a paucity of treatment options. As such, we sequenced healthy and diseased livers to compare the activity, interactions, and localization of immune and non-immune cells. This revealed that outside PSC scar regions, hepatocytes are transitioning to bile duct cells, whereas within the scars, there is an accumulation of immune cells. Of these cells, macrophages that typically contribute to tissue repair were enriched in immunoregulatory genes and were less responsive to stimulation. These cells are likely involved in maintaining hepatic inflammation and could be targeted in novel therapeutic development.

cell biology↗
bioRxiv2023

Deep spatial proteomic exploration of severe COVID-19-related pulmonary injury in post-mortem specimens

The lung, as a primary target of SARS-CoV-2, exhibits heterogeneous microenvironment accompanied by various histopathological changes following virus infection. However, comprehensive insight into the protein basis of COVID-19-related pulmonary injury with spatial resolution is currently deficient. Here, we generated a region-resolved quantitative proteomic atlas of seven major pathological structures within the lungs of COVID-19 victims by integrating histological examination, laser microdissection, and ultrasensitive proteomic technologies. Over 10,000 proteins were quantified across 71 dissected FFPE post-mortem specimens. By comparison with control samples, we identified a spectrum of COVID-19-induced protein and pathway dysregulations in alveolar epithelium, bronchial epithelium, and pulmonary blood vessels, providing evidence for the proliferation of transitional-state pneumocytes. Additionally, we profiled the region-specific proteomes of hallmark COVID-19 pulmonary injuries, including bronchiole mucus plug, pulmonary fibrosis, airspace inflammation, and hyperplastic alveolar type 2 cells. Bioinformatic analysis revealed the enrichment of cell-type and functional markers in these regions (e.g. enriched TGFBI in fibrotic region). Furthermore, we identified the up-regulation of proteins associated with viral entry, host restriction, and inflammatory response in COVID-19 lungs, such as FURIN and HGF. Collectively, this study provides spatial proteomic insights for understanding COVID-19-caused pulmonary injury, and may serve as a valuable reference for improving therapeutic intervention for severe pneumonia.

systems biology↗
bioRxiv2023

Long-read Transcriptome Landscapes of Primary and Metastatic Liver Cancers at Transcript Resolution

BackgroundThe liver is the sixth most common site of primary cancer in humans and is frequently colonized by metastases from cancers of other organs. Few studies have investigated the transcriptomic profiles of matched primary tumor and hepatic metastases of patients. Moreover, the read length of 100-200 bases in conventional short-read RNA sequencing is too short, which makes it difficult to directly infer the full-length transcript structure. To help develop effective treatments and improve survival, it is crucial to understand the complex and diverse molecular mechanisms of primary and metastatic liver cancers. MethodsNinety-five primary and secondary liver cancer patients who underwent hepatic resection were included with long-read sequencing isoform-sequencing and short-read RNA sequencing. We compared the transcriptome landscapes of primary and metastatic liver cancers and systematically investigated HCC, paired primary tumors and liver metastases, and matched non-tumor liver tissues. ResultsWe defined the full-length isoform-level transcriptome of human primary and metastatic liver cancers and identified isoform-level diversity in HCC and metastasis-associated transcriptome variations in metastatic liver cancers. Specific RNA transcripts and isoform switching events with clinical implications were profoundly discovered in liver cancer. Metastasis-specific transcripts that can predict the metastatic risk and identify the primary sites of cancers of unknown primary liver metastasis patients were defined. Additionally, we found that adjacent paracancerous liver tissues are abnormal and characterized the premetastatic immunological and metabolic alterations in the liver that favor the spread of cancer metastases. ConclusionsOur findings strongly highlight the powerfulness of full-length transcriptome profiling to yield novel biological insights into understanding the molecular basis of tumorigenesis and will further benefit the treatment of primary and metastatic liver cancers.

genomics↗
bioRxiv2023

Functional variations in gamma-secretase activity are critical determinants of the clinical, biomarker, and cognitive progression of autosomal dominant Alzheimer's disease

BackgroundThe balance between production, clearance, and toxicity of A{beta} peptides is central to Alzheimers disease (AD) pathobiology. Though highly variable in terms of age at symptom onset (AAO), hundreds of variants in PSEN1 cause autosomal dominant forms of AD (ADAD) with nearly complete penetrance. PSEN1 forms the catalytic core of the {gamma}-secretase complex and thereby directly mediates the production of longer, aggregation-prone A{beta} peptides relative to shorter, non-aggregating peptides. We hypothesized that the broad AAO and biomarker heterogeneity seen across ADAD would be predictable based on mutation-specific differences in the production of A{beta} species. MethodsA{beta}-37, 38, 40, 42, and 43 production was quantified from 161 unique PSEN1 variants expressed in HEK293 cells. Prediction of AAO was carried out in 106 variants with available AAO and then replicated in 55 variants represented across 190 PSEN1 mutation carriers who have detailed cognitive and biomarker data from the Dominantly Inherited Alzheimers Network (DIAN). ResultsVariations in A{beta} production across the 161 mutations examined in cell-based models were highly predictive of AAO. In those with corresponding in vivo data from the DIAN study, our cell-based {gamma}-secretase composite was strongly associated with biomarker and cognitive trajectories. ConclusionsThese findings elucidate the critical link between {gamma}-secretase function, A{beta} production, and AD progression and offer mechanistic support for the amyloid hypothesis. The approach used here represents a powerful tool to account for heterogeneity in disease progression in ADAD clinical trials and to assess the pathogenicity of variants of unknown significance or with limited family history.

neuroscience↗
bioRxiv2023

A pre-trained large language model for translating single-cell transcriptome to proteome

Proteins are crucial for life, and measuring their abundance at the single-cell level can facilitate a high-resolution understanding of biological mechanisms in cellular processes and disease progression. However, current single-cell proteomic technologies face challenges such as limited coverage, throughput, and sensitivity, as well as batch effects, high costs, and stringent experimental operations. Drawing inspiration from the translation procedure of both natural language processing (NLP) and the genetic central dogma, we propose a pre-trained, large generative model named scTranslator (single-cell translator). scTranslator is align-free and capable of generating multi-omics data by inferring the missing single-cell proteome based on the transcriptome. Systematic benchmarking confirms the accuracy, stability, and flexibility of scTranslator across various quantification techniques, cell types, and conditions. Furthermore, scTranslator has demonstrated its superiority in assisting various downstream analyses and applications, including gene/protein interaction inference, gene pseudo-knockout, cell clustering, batch correction, and cell origin recognition on pan-cancer data.

bioinformatics↗
bioRxiv2023

Pan-cancer characterization of ncRNA synergistic competition uncovers potential carcinogenic biomarkers

Non-coding RNAs (ncRNAs) act as important modulators of gene expression and they have been confirmed to play critical roles in the physiology and development of malignant tumors. Understanding the synergism of multiple ncRNAs in competing endogenous RNA (ceRNA) regulation can provide important insights into the mechanisms of malignant tumors caused by ncRNA regulation. In this work, we present a framework, SCOM, for identifying ncRNA synergistic competition. We systematically construct the landscape of ncRNA synergistic competition across 31 malignant tumors, and reveal that malignant tumors tend to share hub ncRNAs rather than the ncRNA interactions involved in the synergistic competition. In addition, the synergistic competition ncRNAs (i.e. ncRNAs involved in the synergistic competition) are likely to be involved in drug resistance, contribute to distinguishing molecular subtypes of malignant tumors, and participate in immune regulation. Furthermore, SCOM can help to infer ncRNA synergistic competition across malignant tumors and uncover potential diagnostic and prognostic biomarkers of malignant tumors. Altogether, the SCOM framework and the resulting web-based database SCOMdb (www.comblab.cn/SCOMdb/) serve as a useful resource for exploring ncRNA regulation and to accelerate the identification of carcinogenic biomarkers. Author summaryAbundant evidence reveals that ncRNAs are important modulators of gene expression, and it is important for us to understand the regulation of ncRNAs in malignant tumors. In this work, we hypothesize that ncRNAs acting as ceRNAs can crosstalk with each other in the form of synergistic competition. We name the hypothesis as the ncRNA synergistic competition hypothesis. To uncover potential carcinogenic biomarkers, we present a framework, SCOM, for identifying ncRNA synergistic competition in malignant tumors. From the perspective of synergistic competition, we have shown that the synergistic competition ncRNAs are likely to involve in drug resistance, contribute to distinguishing molecular subtypes of malignant tumors, and participate in immune regulation. In addition, the synergistic competition ncRNAs are potential diagnostic and prognostic biomarkers of malignant tumors. We believe that the proposed framework SCOM and the web-based database SCOMdb can contribute to the design of reliable diagnosis and treatment biomarkers for malignant tumors.

bioinformatics↗
bioRxiv2023

An adversarial collaboration to critically evaluate theories of consciousness

Different theories explain how subjective experience arises from brain activity1,2. These theories have independently accrued evidence, yet, confirmation bias and dependence on design choices hamper progress in the field3. Here, we present an open science adversarial collaboration which directly juxtaposes Integrated Information Theory (IIT)4,5 and Global Neuronal Workspace Theory (GNWT)6-10, employing a theory-neutral consortium approach11,12. We investigate neural correlates of the content and duration of visual experience. The theory proponents and the consortium developed and preregistered the experimental design, divergent predictions, expected outcomes, and their interpretation12. 256 human subjects viewed suprathreshold stimuli for variable durations while neural activity was measured with functional magnetic resonance imaging, magnetoencephalography, and electrocorticography. We find information about conscious content in visual, ventro-temporal and inferior frontal cortex, with sustained responses in occipital and lateral temporal cortex reflecting stimulus duration, and content-specific synchronization between frontal and early visual areas. These results confirm some predictions of IIT and GNWT, while substantially challenging both theories: for IIT, a lack of sustained synchronization within posterior cortex contradicts the claim that network connectivity specifies consciousness. GNWT is challenged by the general lack of ignition at stimulus offset and limited representation of certain conscious dimensions in prefrontal cortex. Beyond challenging the theories themselves, we present an alternative approach to advance cognitive neuroscience through a principled, theory-driven, collaborative effort. We highlight the challenges to change peoples mind 13 and the need for a quantitative framework integrating evidence for systematic theory testing and building.

neuroscience↗
bioRxiv2023

Dual-color GRAB sensors for monitoring spatiotemporal serotonin release in vivo

The serotonergic system plays important roles in both physiological and pathological processes, and is a widely used therapeutic target for many psychiatric disorders. Although several genetically encoded GFP-based serotonin (5-HT) sensors were recently developed, their sensitivities and spectral profiles are relatively limited. To overcome these limitations, we optimized green fluorescent G-protein-coupled receptor (GPCR)-activation-based 5-HT (GRAB5-HT) sensors and developed a new red fluorescent GRAB5-HT sensor. These sensors have excellent cell surface trafficking, high specificity, sensitivity, and spatiotemporal resolution, making them suitable for monitoring 5-HT dynamics in vivo. Besides recording subcortical 5-HT release in freely moving mice, we observed both uniform and gradient 5-HT release in the mouse dorsal cortex with mesoscopic imaging. Finally, we performed dual-color imaging and observed seizure-induced waves of 5-HT release throughout the cortex following calcium and endocannabinoid waves. In summary, these 5-HT sensors can offer valuable insights regarding the serotonergic system in both physiological and pathological states.

neuroscience↗