Biology subjects

Liu, R.

Publications and source records attributed to Liu, R..

At least 181 records · Page 10Linked to original sources

bioRxiv2023

SpatialCTD: a large-scale TME spatial transcriptomic dataset to evaluate cell type deconvolution for immuno-oncology

Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular resolution. The task of cell type deconvolution has been introduced to disentangle discrete cell types from such multi-cellular spots. However, existing datasets for cell type deconvolution are limited in scale, predominantly encompassing data on mice, and are not designed for human immuno-oncology. In order to overcome these limitations and promote comprehensive investigation of cell type deconvolution for human immuno-oncology, we introduce a large-scale spatial transcriptomic dataset named SO_SCPLOWPATIALC_SCPLOWCTD, encompassing 1.8 million cells from the human tumor microenvironment across the lung, kidney, and liver. Distinct from existing approaches that primarily depend on single-cell RNA sequencing data as a reference without incorporating spatial information, we introduce Graph Neural Network-based method (i.e., GNNDO_SCPLOWECONVOLVERC_SCPLOW) that effectively utilize the spatial information from reference samples, and extensive experiments show that GNNDO_SCPLOWECONVOLVERC_SCPLOW often outperforms existing state-of-the-art methods by a substantial margin, without requiring single-cell RNA-seq data. To enable comprehensive evaluations on spatial transcriptomics data from flexible protocols, we provide an online tool capable of converting spatial transcriptomic data from other platforms (e.g., 10x Visium, MERFISH and sci-Space) into pseudo spots, featuring adjustable spot size. The SO_SCPLOWPATIALC_SCPLOWCTD dataset and GNNDO_SCPLOWECONVOLVERC_SCPLOW implementation are available at https://github.com/OmicsML/SpatialCTD, and the online converter tool can be accessed at https://omicsml.github.io/SpatialCTD/.

bioinformatics↗

bioRxiv2023

Single-cell RNA-sequencing identifies a novel subset of CD9+ reparative macrophages modulated by CCN2 derived from Nlrx1 deficient cardiomyocytes in the infarcted heart

RationaleNlrx1, a pattern recognition receptor belonging to the NLR family, is highly expressed in the heart and is significantly increased after myocardial infarction in vivo, however, the role of Nlrx1 in post-infarction inflammation and healing is poorly understood. Although the effects of infiltrating macrophages on survival of cardiomyocytes and healing of injured hearts have been intensely studied, whether or not and how surviving cardiomyocytes might influence the phenotype and function of macrophages in the infarcted hearts was largely unexplored. ObjectiveTo investigate the role of Nlrx1 in post-infarction inflammation and healing and to dissect the underlying mechanism. Methods and resultsSingle cell RNA-sequencing of 23,805 cells isolated from infarcted hearts revealed 7 clusters of infarct macrophages including CCR2+, Isg+, Mki67+ proliferative, and LYVE1+ cardiac resident macrophages, and identified a novel subset of CD9+Il1rn+ macrophages, which were characterized by high expression of Cd9, Il1rn, Gpnmb and Spp1, and downregulation of proinflammatory cytokines and chemokines. CD9+Il1rn+ macrophages were largely found in infarcted hearts of Nlrx1KO mice, and were transcriptionally unique with reparative phenotype. Loss of Nlrx1 attenuated ventricular dysfunction and adverse remodeling, and decreased post-infarction mortality. Flow cytometry analysis showed markedly increased number of CD9+ macrophages and higher mean fluorescence intensity of CD9 in Nlrx1KO mice. RNA-seq analysis revealed that CD9+ macrophages of Nlrx1KO infarcted hearts exhibited a reparative and anti-inflammatory phenotype as featured by upregulation of wound healing-associated genes including CD9, Spp1 and Fn1, and downregulation of proinflammatory genes. Conditioned medium from Nlrx1 deficient cardiomyocytes upon hypoxia stimulation skewed macrophage polarization towards a reparative and inflammation-suppressed phenotype. Gain-and loss-of function of Nlrx1 combined with RNA-seq analysis identified CCN2, whose expression and secretion were significantly increased in Nlrx1 deficient cardiomyocytes upon hypoxia, as a cardiomyocytes-derived mediator responsible, at least partly, for reprograming macrophage towards a reparative phenotype. In vivo, CCN2 expression was markedly elevated in Nlrx1KO infarcted hearts. Moreover, exogenous CCN2 partially mediated macrophage transition towards a reparative phenotype. ConclusionsCD9+ IL1rn+ macrophages modulated by Nlrx1 deficient cardiomyocytes-derived CCN2, is a novel subset of reparative macrophages contributing to infarct healing. Inhibition of myocardial Nlrx1 and/or activation of CCN2 to boost CD9+ IL1rn+ reparative macrophages may represent new therapeutics for infarct healing following ischemic injury.

immunology↗

bioRxiv2023

mRNA challenge predicts brain cancer immunogenicity and response to checkpoint inhibitors

To prospectively determine whether brain tumors will respond to immune checkpoint inhibitors (ICIs), we developed a novel mRNA vaccine as a viral mimic to elucidate cytokine release from brain cancer cells in vitro. Our results indicate that cytokine signatures following mRNA challenge differ substantially from ICI responsive versus non-responsive murine tumors. These findings allow for creation of a diagnostic assay to quickly assess brain tumor immunogenicity, allowing for informed treatment with ICI or lack thereof in poorly immunogenic settings.

cancer biology↗

bioRxiv2023

Machine Learning to Identify Molecular Markers for Metabolic Disease Development Using Mouse Models

BackgroundAging, Western diet (WD) intake, and bile acid (BA) receptor farnesoid X receptor (FXR) inactivation are risk factors for metabolic disease development including nonalcoholic fatty liver disease (NAFLD) and chronic inflammation-related health issues such as dementia. The progression of the metabolic disease can be escalated when those risks are combined. Inactivation of FXR is cancer prone in both humans and mice. The current study used omics data generated within the gut-liver axis to classify those risks using bioinformatics and machine learning approaches. MethodsDifferent ages (5, 10, and 15 months) of wild-type (WT) and FXR knockout (KO) male mice were fed with either a healthy control diet (CD) or a WD since weaning. Hepatic transcripts, liver, serum, and urine metabolites, hepatic bile acids (BAs), as well as gut microbiota were used for risk prediction. A linear support vector machine with K-fold cross-validation was used for classification and feature selection. ResultsIncreased urine sucrose alone achieved 91% accuracy in predicting WD intake. Hepatic lithocholic acid (LCA) and serum pyruvate had 100% and 95% accuracy, respectively to classify age. Association analyses showed hepatic LCA was positively associated with serum concentrations of acetone, a ketone body, and 1,3-dihydroxyacetone (DHA), but negatively correlated with serum pyruvate. Urine metabolites (decreased creatinine and taurine as well as increased succinate) or gut microbiota (increased Dorea, Dehalobacterium, and Oscillospira) could predict FXR functional status with greater than 90% accuracy. Integrated pathway analyses revealed that the predictors for diet and FXR expression were implicated in the central carbon metabolism in cancer. To assess the translational relevance, mouse hepatic transcripts were crosschecked with human NAFLD and hepatocellular carcinoma (HCC) datasets. WD-affected hepatic Cyp39a1 and Gramd1b expression were associated with human HCC and NAFLD, respectively. The metabolites and diseases interaction analyses uncovered that the identified features are implicated in human metabolic diseases, mental disorders, and cancer. ConclusionThe risk prediction using mouse models contributes to the identification of noninvasive biomarkers for early diagnosis of metabolic disease development.

bioinformatics↗

bioRxiv2023

Structure-Kinetics Relationships of Opioids from Metadynamics and Machine Learning

The nations opioid overdose deaths reached an all-time high in 2021. The majority of deaths are due to synthetic opioids represented by fentanyl. Naloxone, which is a FDA-approved reversal agent, antagonizes opioids through competitive binding at the -opioid receptor (mOR). Thus, knowledge of opioids residence time is important for assessing the effectiveness of naloxone. Here we estimated the residence times of 15 fentanyl and 4 morphine analogs using metadynamics, and compared them with the most recent measurement of the opioid kinetic, dissociation, and naloxone inhibitory constants (Mann, Li et al, Clin. Pharmacol. Therapeut. 2022). Importantly, the microscopic simulations offered a glimpse at the common binding mechanism and molecular determinants of dissociation kinetics for fentanyl analogs. The insights inspired us to develop a machine learning (ML) approach to analyze the kinetic impact of fentanyls substituents based on the interactions with mOR residues. This proof-of-concept approach is general; for example, it may be used to tune ligand residence times in computeraided drug discovery. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/531338v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@15e4093org.highwire.dtl.DTLVardef@e17398org.highwire.dtl.DTLVardef@16c2aaaorg.highwire.dtl.DTLVardef@6527a2_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

Extensive re-modelling of the cell wall during the development of Staphylococcus aureus bacteraemia

Introductory Paragraph / AbstractThe bloodstream represents a hostile environment that bacteria must overcome to cause bacteraemia. To understand how the major human pathogen Staphylococcus aureus manages this we have utilised a functional genomics approach to identify a number of new loci that affect the ability of the bacteria to survive exposure to serum, the critical first step in the development of bacteraemia. The expression of one of these genes, tcaA, was found to be induced upon exposure to serum, and we show that it is involved in the elaboration of a critical virulence factor, the wall teichoic acids (WTA), within the cell envelope. The activity of the TcaA protein alters the sensitivity of the bacteria to cell wall attacking agents, including antimicrobial peptides, human defence fatty acids, and several antibiotics. This protein also affects the autolytic activity and lysostaphin sensitivity of the bacteria, suggesting that in addition to changing WTA abundance in the cell envelope, it also plays a role in peptidoglycan crosslinking. With TcaA rendering the bacteria more susceptible to serum killing, while simultaneously increasing the abundance of WTA in the cell envelope, it was unclear what effect this protein may have during infection. To explore this, we examined human data and performed murine experimental infections. Collectively, our data suggests that whilst mutations in tcaA are selected for during bacteraemia, this protein positively contributes to the virulence of S. aureus through its involvement in altering the cell wall architecture of the bacteria, a process that appears to play a key role in the development of bacteraemia.

microbiology↗

bioRxiv2023

Three-Dimensional Bioconjugated Liquid-Like Solid (LLS) Enhance Characterization of Solid Tumor - Chimeric Antigen Receptor T cell Interactions

Cancer immunotherapy offers lifesaving treatments for cancers, but the lack of reliable preclinical models that could enable the mechanistic studies of tumor-immune interactions hampers the identification of new therapeutic strategies. We hypothesized 3D confined microchannels, formed by interstitial space between bio-conjugated liquid-like solids (LLS), enable CAR T dynamic locomotion within an immunosuppressive TME to carry out anti-tumor function. Murine CD70-specific CAR T cells cocultured with the CD70-expressing glioblastoma and osteosarcoma demonstrated efficient trafficking, infiltration, and killing of cancer cells. The anti-tumor activity was clearly captured via longterm in situ imaging and supported by upregulation of cytokines and chemokines including IFNg, CXCL9, CXCL10, CCL2, CCL3, and CCL4. Interestingly, target cancer cells, upon an immune attack, initiated an "immune escape" response by frantically invading the surrounding microenvironment. This phenomenon however was not observed for the wild-type tumor samples which remained intact and produced no relevant cytokine response. Single cells collection and transcriptomic profiling of CAR T cells at regions of interest revealed feasibility of identifying differential gene expression amongst the immune subpopulations. Complimentary 3D in vitro platforms are necessary to uncover cancer immune biology mechanisms, as emphasized by the significant roles of the TME and its heterogeneity.

bioengineering↗

bioRxiv2023

Single-cell RNA sequencing to explore the immune process difference between Kawasaki disease and febrile patients

BackgroundKawasaki disease (KD) is an acute systemic vasculitis that can lead to acquired heart disease in children mostly from in developed countries. The previous research showed that B cells in KD patients underwent a profound change in both the cell numbers and types after intravenous immunoglobulin (IVIG) therapy. MethodsWe performed the single-cell RNA-sequencing for the peripheral blood mononuclear cells (PBMCs) from three febrile patients and three KD patients to investigate the possible mechanism underlying B cell developmental dysfunction in KD. A previously published single-cell sequencing KD dataset (GSE168732) was also utilized in study for sample size expansion and validation. The comprehensive single-cell data analyses were applied for our dataset and GSE168732 dataset including single-cell trajectory analysis. To validate the immune disorders in KD, we measured immune-related indicators from 28 KD and 28 febrile patients. ResultOverall single-cell expression profiles show that the biological processes of immunity, B cell activation pathway and their related biological entities are repressed in KD patients before IVIG treatment compared to febrile patient and KD patients after IVIG treatment. The differentially expressed gene analyses further demonstrate that B cell signaling pathway is downregulated in B cells and plasma blast cells of KD patients before treatment while cell cycle genes and MYC gene are upregulated in dendritic cells (DCs) and hematopoietic stem and progenitor cells (HSPCs) of KD patients before treatment. The biological process of immune response is upregulated in the HSPCs of KD patients before treatment in our dataset while the biological process of inflammatory response is upregulated in the HSPCs of KD patients before treatment in GSE168732 dataset. Single-cell trajectory analyses demonstrate that KD patients before treatment have a shortened developmental path in which B cells and T cells are failed to differentiate into separate lineages. HSPD1 and HSPE1 genes show an elevated expression level in the early cell development stage of KD patients before treatment accompanied with the repression of MYC, SPI1, MT2A and UBE2C genes. Our analyses of all B cells from KD patients before treatment show most of B cells are arrested in a transitional state with an ill developmental path compared with febrile patients and KD patients after treatment. The percentage and absolute value of CD8 T cells in KD were lower than those in febrile patients. The ratio of CD4/CD8 in KD was higher than it in febrile patients. The serum levels of IgG and IgM in KD were lower than those in febrile patients. ConclusionsOur results indicate that the immune premature HSPCs accompanied with the abnormal expression dynamics of cell cycle and SPI1 genes are the mechanism underlying B cell developmental dysfunction in KD patients. FundingThis work is jointly supported by National Natural Science Foundation of China (82170518) and the Shanghai Science and Technology Committee research Funding (22Y11909700) and Shanghai Jinshan District medical key specialty Funding (JSZK2023A04).

immunology↗

bioRxiv2023

Fecal virome transplantation is sufficient to alter fecal microbiota and drive lean and obese body phenotypes in mice

BackgroundThe gastrointestinal microbiome plays a significant role in numerous host processes and has an especially large impact on modulating the host metabolism. Prior studies have shown that when mice receive fecal transplants from obese donors that were fed high-fat diets (HFD) (even when recipient mice are fed normal diets after transplantation), they develop obese phenotypes. These studies demonstrate the prominent role that the gut microbiota play in determining lean and obese phenotypes. While much of the credit has been given to gut bacteria, studies have not measured the impact of gut viruses on these phenotypes. To address this shortcoming, we gavaged mice with viromes isolated from donors fed HFD or normal chow. By characterizing the mices gut bacterial biota and weight-gain phenotypes over time, we demonstrate that viruses can shape the gut bacterial community and affect weight gain or loss. ResultsWe gavaged mice longitudinally over 4 weeks while measuring their body weights and collecting fecal samples for 16S rRNA amplicon sequencing. We evaluated mice that were fed normal chow or high-fat diets, and gavaged each group with either chow-derived fecal viromes, HFD-derived fecal viromes, or phosphate buffered saline controls. We found a significant effect of gavage type, where mice fed chow but gavaged with HFD-derived viromes gained significantly more weight than their counterparts receiving chow-derived viromes. The converse was also true: mice fed HFD but gavaged with chow-derived viromes gained significantly less weight than their counterparts receiving HFD-derived viromes. These results were replicated in two separate experiments and the phenotypic changes were accompanied by significant and identifiable differences in the fecal bacterial biota. Notably, there were differences in Lachnospirales and Clostridia in mice fed chow but gavaged with HFD-derived fecal viromes, and in Peptostreptococcales, Oscillospirales, and Lachnospirales in mice fed HFD but gavaged with chow-derived fecal viromes. Due to methodological limitations, we were unable to identify specific bacterial species or strains that were responsible for respective phenotypic changes. ConclusionsThis study confirms that virome-mediated perturbations can alter the fecal microbiome in an in vivo model and indicates that such perturbations are sufficient to drive lean and obese phenotypes in mice.

microbiology↗

bioRxiv2023

Microbiome induced complement synthesized in the gut protects against enteric infections

Canonically, complement is a serum-based host defense system that protects against systemic microbial invasion. Little is known about the production and function of complement components on mucosal surfaces. Here we show gut complement component 3 (C3), central to complement function, is regulated by the composition of the microbiota in healthy humans and mice, leading to host-specific gut C3 levels. Stromal cells in intestinal lymphoid follicles (LFs) are the predominant source of intestinal C3. During enteric infection with Citrobacter rodentium or enterohemorrhagic Escherichia coli, luminal C3 levels increase significantly and are required for protection. C. rodentium is remarkably more invasive to the gut epithelium of C3-deficient mice than of wild-type mice. In the gut, C3-mediated phagocytosis of C. rodentium functions to clear pathogens. Our study reveals that variations in gut microbiota determine individuals intestinal mucosal C3 levels, dominantly produced by LF stromal cells, which directly correlate with protection against enteric infection. HighlightsO_LIGut complement component 3 (C3) is induced by the microbiome in healthy humans and mice at a microbiota-specific level. C_LIO_LIGut stromal cells located in intestinal lymphoid follicles are a major source of luminal C3 C_LIO_LIDuring enteric infections with Citrobacter rodentium or enterohemorrhagic Escherichia coli, gut luminal C3 levels increase and are required for protection. C_LIO_LIC. rodentium is significantly more invasive of the gut epithelium in C3-deficient mice when compared to WT mice. C_LIO_LIIn the gut, C3-mediated opsonophagocytosis of C. rodentium functions to clear pathogens. C_LI

immunology↗

bioRxiv2023

Deep Learning-based Identification of Intraocular Pressure-Associated Genes Influencing Trabecular Meshwork Cell and Organelle Morphology

PURPOSEThe exact pathogenesis of primary open-angle glaucoma (POAG) is poorly understood. Genome-wide association studies (GWAS) have recently uncovered many loci associated with variation in intraocular pressure (IOP); a crucial risk factor for POAG. Artificial intelligence (AI) can be used to interrogate the effect of specific genetic knockouts on the morphology of trabecular meshwork cells (TMCs), the regulatory cells of IOP. METHODSSixty-two genes at fifty-five loci associated with IOP variation were knocked out in primary TMC lines. All cells underwent high-throughput microscopy imaging after being stained with a five-channel fluorescent cell staining protocol. A convolutional neural network (CNN) was trained to distinguish between gene knockout and normal control cell images. The area under the receiver operator curve (AUC) metric was used to quantify morphological variation in gene knockouts to identify potential pathological perturbations. RESULTSCells where RALGPS1 had been perturbed demonstrated the greatest morphological variation from normal TMCs (AUC 0.851, SD 0.030), followed by LTBP2 (AUC 0.846, SD 0.029) and BCAS3 (AUC 0.845, SD 0.020). Of seven multi-gene loci, five had statistically significant differences in AUC (p<0.05) between genes, allowing for pathological gene prioritisation. The mitochondrial channel most frequently showed the greatest degree of morphological variation (33.9% of cell lines). CONCLUSIONSWe demonstrate a robust method for functionally interrogating genome-wide association signals using high-throughput microscopy and AI. Genetic variations inducing marked morphological variation can be readily identified, allowing for the gene-based dissection of loci associated with complex traits.

genetics↗

bioRxiv2023

Guide to the construction and use of an adaptive optics two-photon microscope with direct wavefront sensing

Two-photon microscopy, combined with appropriate optical labeling, has enabled the study of structure and function throughout nervous systems. This methodology enables, for example, the measurement and tracking of sub-micrometer structures within brain cells, the spatio-temporal mapping of spikes in individual neurons, and the spatio-temporal mapping of transmitter release in individual synapses. Yet the spatial resolution of two-photon microscopy rapidly degrades as imaging is attempted at depths more than a few scattering lengths into tissue, i.e., below the superficial layers that constitute the top 300 to 400 {micro}m of neocortex. To obviate this limitation, we measure the wavefront at the focus of the excitation beam and utilize adaptive optics that alters the incident wavefront to achieve an improved focal volume. We describe the constructions, calibration, and operation of a two-photon microscopy that incorporates adaptive optics to restore diffraction-limited resolution throughout the nearly 900 {micro}m depth of mouse cortex. Our realization utilizes a guide star formed by excitation of red-shifted dye within the blood serum to directly measure the wavefront. We incorporate predominantly commercial optical, optomechanical, mechanical, and electronic components; computer aided design models of the exceptional custom components are supplied. The design is modular and allows for expanded imaging and optical excitation capabilities. We demonstrate our methodology in mouse neocortex by imaging the morphology of somatostatin-expressing neurons at 700 {micro}m beneath the pia, calcium dynamics of layer 5b projection neurons, and glutamate transmission to L4 neurons.

neuroscience↗

bioRxiv2023

nleval: A Python Toolkit for Generating Benchmarking Datasets for Machine Learning with Biological Networks

Over the past decades, network biology has been a major driver of computational methods developed to better understand the functional roles of each gene in the human genome in their cellular context. Following the application of traditional semi-supervised and supervised machine learning (ML) techniques, the next wave of advances in network biology will come from leveraging graph neural networks (GNN). However, to test new GNN-based approaches, a systematic and comprehensive benchmarking resource that spans a diverse selection of biomedical networks and gene classification tasks is lacking. Here, we present the Open Biomedical Network Benchmark (OBNB), a collection of benchmarking datasets derived using networks from 15 sources and tasks that include predicting genes associated with a wide range of functions, traits, and diseases. The accompanying Python package, obnb, contains reusable modules that enable researchers to download source data from public databases or archived versions and set up ML-ready datasets that are compatible with popular GNN frameworks such as PyG and DGL. Our work lays the foundation for novel GNN applications in network biology. obob will also help network biologists easily set-up custom benchmarking datasets for answering new questions of interest and collaboratively engage with graph ML practitioners to enhance our understanding of the human genome. OBNB is released under the MIT license and is freely available on GitHub: https://github.com/krishnanlab/obnb

bioinformatics↗

bioRxiv2023

Insulin determines the effects of TGF-beta on HNF4alpha transcription and epithelial-to-mesenchymal transition in hepatocytes

To date, epithelial-to-mesenchymal transition (EMT) has been observed in cultured hepatocytes, but not in vivo. TGF-{beta} is supposed to initiate EMT in hepatocytes by inhibiting HNF4 through the SMAD2/3 complex. We report that TGF-{beta} does not directly inhibit HNF4, but contributes to its transcriptional regulation by SMAD2/3 recruiting acetyltransferase CBP/p300 to the HNF4 promoter. The recruitment of CBP/p300 is indispensable for C/EBPa binding, another essential requirement for constitutive HNF4 expression in hepatocytes. In contrast to the observed induction of HNF4, SMAD2/3 inhibits C/EBP transcription. Therefore, long-term TGF-{beta} incubation results in C/EBP depletion, which abrogates HNF4 expression. Intriguingly, SMAD2/3 inhibitory binding to the C/EBP promoter is abolished by insulin. Thus, maintaining a high insulin concentration in culture medium ensures constitutive HNF4 and thereby prevents TGF-{beta}-induced hepatocyte EMT. Furthermore, insulin inhibits TGF-{beta}-induced SMAD2/3 binding to the promoters of core EMT transcription factors e.g., SNAI1. SNAI1 transcription requires both SMAD2/3 and FOXO1 in nuclei. Insulin inhibits SNAI1 transcription through impeding SMAD2/3 binding to its promoter and inducing FOXO1 phosphorylation. Hence, insulin is the key factor that prevents TGF-{beta}-induced EMT in hepatocytes.

cell biology↗

bioRxiv2022

MIDAS: a deep generative model for mosaic integration and knowledge transfer of single-cell multimodal data

AO_SCPLOWBSTRACTC_SCPLOWRapidly developing single-cell multi-omics sequencing technologies generate increasingly large bodies of multimodal data. Integrating multimodal data from different sequencing technologies, i.e. mosaic data, permits larger-scale investigation with more modalities and can help to better reveal cellular heterogeneity. However, mosaic integration involves major challenges, particularly regarding modality alignment and batch effect removal. Here we present a deep probabilistic framework for the mosaic integration and knowledge transfer (MIDAS) of single-cell multimodal data. MIDAS simultaneously achieves dimensionality reduction, imputation, and batch correction of mosaic data by employing self-supervised modality alignment and information-theoretic latent disentanglement. We demonstrate its superiority to other methods and reliability by evaluating its performance in full trimodal integration and various mosaic tasks. We also constructed a single-cell trimodal atlas of human peripheral blood mononuclear cells (PBMCs), and tailored transfer learning and reciprocal reference mapping schemes to enable flexible and accurate knowledge transfer from the atlas to new data. Applications in mosaic integration, pseudotime analysis, and cross-tissue knowledge transfer on bone marrow mosaic datasets demonstrate the versatility and superiority of MIDAS.

bioinformatics↗

bioRxiv2022

A Scalable and Adaptive Ultra-high-density Fan-out Strategy for High-throughput Flexible Microelectrodes

Flexible neural microelectrodes demonstrate higher compliance and better biocompatibility than rigid electrodes. They have multiple microfilaments can be freely distributed across different brain regions. However, high-density fan-out of high-throughput flexible microelectrodes remains a challenge since monolithic integration between electrodes and circuits is not currently available as it is for silicon electrodes. Here, we proposed a high-density fan-out strategy for high-throughput flexible electrodes. The flexible electrodes are partially overlapped on the printed circuit board (PCB). A modified wire bonding method is used to reduce the pads area on the PCB. Both the traces and pads on the PCB are optimized to minimize the back-end package. It is significantly reducing the connection area. In addition, the vertical rather than horizontal connection between the connector and the PCB further decrease the volume of the package. 1024 channel flexible electrodes are bonded to the high-density PCB within an area of 11.8x10mm2, and the success connection rate can reach 100%. The high-density fan-out strategy proposed in this paper can effectively reduce the volume of high-throughput flexible electrodes after packaging, which facilitates the miniaturization of in vivo multichannel recording devices and contributes to long-term recording.

bioengineering↗

bioRxiv2022

Cooperatively Designed Aptamer-PROTACs for Spatioselective Degradation of Nucleocytoplasmic Shuttling Protein for Enhanced Combinational Therapy

Nucleocytoplasmic shuttling proteins (NSPs) has emerged as a promising class of therapeutic targets for many diseases including cancer. However, most reported NSPs-based therapies largely rely on small molecule inhibitors with limited efficacy and off-target effects. Proteolysis targeting chimera (PROTAC) represents a revolutionary inhibitory modality for targeted protein degradation with many advantages, including substoichiometric catalytic activity, improved selectivity, and high efficacy. However, the majority of reported PROTACs so far are still limited to the degradation of cytoplasmic proteins and lack of tumor-specific targeting. To realize the full potential of the PROTAC technology and broaden its applications for the degradation of NSPs, we herein report a conceptual approach for the design of a new archetype of PROTAC (denoted as PS-ApTCs) by introducing phosphorothioate-modified AS1411 aptamer to a ligand of the CRBN E3 ligase, realizing tumor-targeting and spatioselective degradation with improved efficacy. We have demonstrated that PS-ApTCs is capable of effectively degrading nucleolin in target cell membrane and cytoplasm but not in the nucleus, in a CRBN- and proteasome-dependent manner. In addition, PS-ApTCs exhibits superior antiproliferation, pro-apoptotic, and cell cycle arrest potencies. Importantly, we demonstrate for the first time that combination of PS-ApTCs-mediated nucleolin degradation with aptamer-drug conjugates-based chemotherapy can enable an AND-gated synergistic effect on tumor inhibition. Collectively, our results suggest that PS-ApTCs possess the ability to expand the PROTACs toolbox to even wider range of targets in subcellular localization and accelerate the discovery of new combinational therapeutic approaches. For Table of Contents Only O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/514682v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@ed8331org.highwire.dtl.DTLVardef@1400b91org.highwire.dtl.DTLVardef@1b795a6org.highwire.dtl.DTLVardef@1c1f141_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2022

Ratio-based quantitative multiomics profiling using universal reference materials empowers data integration

Multiomics profiling is a powerful tool to characterize the same samples with complementary features orchestrating the genome, epigenome, transcriptome, proteome, and metabolome. However, the lack of ground truth hampers the objective assessment of and subsequent choice from a plethora of measurement and computational methods aiming to integrate diverse and often enigmatically incomparable omics datasets. Here we establish and characterize the first suites of publicly available multiomics reference materials of matched DNA, RNA, proteins, and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters, providing built-in truth defined by family relationship and the central dogma. We demonstrate that the "ratio"-based omics profiling data, i.e., by scaling the absolute feature values of a study sample relative to those of a concurrently measured universal reference sample, were inherently much more reproducible and comparable across batches, labs, platforms, and omics types, thus empower the horizontal (within-omics) and vertical (cross-omics) data integration in multiomics studies. Our study identifies "absolute" feature quantitation as the root cause of irreproducibility in multiomics measurement and data integration, and urges a paradigm shift from "absolute" to "ratio"-based multiomics profiling with universal reference materials.

systems biology↗