Multi-organ proteomic landscape of COVID-19 autopsies.

The molecular pathology of multi-organ injuries in COVID-19 patients remains unclear, preventing effective therapeutics development. Here, we report a proteomic analysis of 144 autopsy samples from seven organs in 19 COVID-19 patients. We quantified 11,394 proteins in these samples, in which 5,336 were perturbed in the COVID-19 patients compared to controls. Our data showed that cathepsin L1, rather than ACE2, was significantly upregulated in the lung from the COVID-19 patients. Systemic hyperinflammation and dysregulation of glucose and fatty acid metabolism were detected in multiple organs. We also observed dysregulation of key factors involved in hypoxia, angiogenesis, blood coagulation, and fibrosis in multiple organs from the COVID-19 patients. Evidence for testicular injuries includes reduced Leydig cells, suppressed cholesterol biosynthesis, and sperm mobility. In summary, this study depicts a multi-organ proteomic landscape of COVID-19 autopsies that furthers our understanding of the biological basis of COVID-19 pathology.

COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas.

A dysfunctional immune response in coronavirus disease 2019 (COVID-19) patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 196 COVID-19 patients and controls and created a comprehensive immune landscape with 1.46 million cells. The large dataset enabled us to identify that different peripheral immune subtype changes are associated with distinct clinical features, including age, sex, severity, and disease stages of COVID-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was found in diverse epithelial and immune cell types, accompanied by dramatic transcriptomic changes within virus-positive cells. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis of and developing effective therapeutic strategies for COVID-19.

Identification of alternative splicing-derived cancer neoantigens for mRNA vaccine development.

Messenger RNA (mRNA) vaccines have shown great potential for anti-tumor therapy due to the advantages in safety, efficacy and industrial production. However, it remains a challenge to identify suitable cancer neoantigens that can be targeted for mRNA vaccines. Abnormal alternative splicing occurs in a variety of tumors, which may result in the translation of abnormal transcripts into tumor-specific proteins. High-throughput technologies make it possible for systematic characterization of alternative splicing as a source of suitable target neoantigens for mRNA vaccine development. Here, we summarized difficulties and challenges for identifying alternative splicing-derived cancer neoantigens from RNA-seq data and proposed a conceptual framework for designing personalized mRNA vaccines based on alternative splicing-derived cancer neoantigens. In addition, several points were presented to spark further discussion toward improving the identification of alternative splicing-derived cancer neoantigens.

CBLRR: a cauchy-based bounded constraint low-rank representation method to cluster single-cell RNA-seq data.

The rapid development of single-cel+l RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for exploring biological phenomena at the single-cell level. The discovery of cell types is one of the major applications for researchers to explore the heterogeneity of cells. Some computational methods have been proposed to solve the problem of scRNA-seq data clustering. However, the unavoidable technical noise and notorious dropouts also reduce the accuracy of clustering methods. Here, we propose the cauchy-based bounded constraint low-rank representation (CBLRR), which is a low-rank representation-based method by introducing cauchy loss function (CLF) and bounded nuclear norm regulation, aiming to alleviate the above issue. Specifically, as an effective loss function, the CLF is proven to enhance the robustness of the identification of cell types. Then, we adopt the bounded constraint to ensure the entry values of single-cell data within the restricted interval. Finally, the performance of CBLRR is evaluated on 15 scRNA-seq datasets, and compared with other state-of-the-art methods. The experimental results demonstrate that CBLRR performs accurately and robustly on clustering scRNA-seq data. Furthermore, CBLRR is an effective tool to cluster cells, and provides great potential for downstream analysis of single-cell data. The source code of CBLRR is available online at https://github.com/Ginnay/CBLRR.

DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data.

MOTIVATION: Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify CCIs from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity. RESULTS: Here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively. Powered by the flexible and easy-to-use software, DeepCCI can provide the one-stop solution to discover meaningful intercellular interactions and build CCI networks from scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: The source code of DeepCCI is available online at https://github.com/JiangBioLab/DeepCCI.

Progress on network modeling and analysis of gut microecology: a review.

The gut microecological network is a complex microbial community within the human body that plays a key role in linking dietary nutrition and host physiology. To understand the complex relationships among microbes and their functions within this community, network analysis has emerged as a powerful tool. By representing the interactions between microbes and their associated omics data as a network, we can gain a comprehensive understanding of the ecological mechanisms that drive the human gut microbiota. In addition, the network-based approach provides a more intuitive analysis of the gut microbiota, simplifying the study of its complex dynamics and interdependencies. This review provides a comprehensive overview of the methods used to construct and analyze networks in the context of gut microecological background. We discuss various types of network modeling approaches, including co-occurrence networks, causal networks, dynamic networks, and multi-omics networks, and describe the analytical techniques used to identify important network properties. We also highlight the challenges and limitations of network modeling in this area, such as data scarcity and heterogeneity, and provide future research directions to overcome these limitations. By exploring these network-based methods, researchers can gain valuable insights into the intricate relationships and functional roles of microbial communities within the gut, ultimately advancing our understanding of the gut microbiota's impact on human health.

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset.

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55%, and 7.0-33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

Langevin dynamics simulations for the critical adsorption of end-grafted active polymers.

The critical adsorption of end-grafted active polymer chains on an attractive surface is studied using Langevin dynamics simulations. The active polymers are composed of an active Langevin particle located at the head and a sequential passive chain. Results show that the active force exerted by the active head pulls the active polymer away from the surface. Consequently, the adsorption of the active polymer is hindered, and the critical surface attraction strength, , increases proportionally to the square of the active force, Fa2. The increase in depends on the rotation behavior of the active head. Specifically, for the restricted rotating active polymer (RRAP) chain with a longer rotational persistence time as the rotation of the active head is restricted, increases significantly with Fa. On the other hand, for the freely rotating active polymer (FRAP) chain with a shorter rotational persistence time as the rotation of the active head is free, shows a weak dependence on Fa. The results show that the active force has a significantly stronger pulling effect on the RRAP chain than on the FRAP chain. Furthermore, knotted conformations are observed for the adsorbed RRAP chain at large Fa. These knots reduce the adsorption of monomers near the grafted end. In contrast, no knotted conformations are observed for the FRAP chains due to the comparatively weaker pulling effect of the active force.

Adsorption of active polymers on attractive nanoparticles.

The adsorption of active polymers on an attractive nanoparticle (NP) is studied using Langevin dynamics simulations. The active polymers consist of an active Brownian particle (ABP) at the head and a subsequent passive polymer chain. The ABP experiences an active force of magnitude Fa. The interactions between the active polymer and NP are modeled as Lennard-Jones potential with a strength εpn. We find the critical adsorption point εpn* increases with increasing the active force Fa. The increment of εpn*, denoted as Δεpn*, due to Fa can be expressed approximately as Δεpn* ∝ Fa2.5 for the restricted rotating active polymer (RRAP) where the rotation of the head ABP is restricted and Δεpn* ∝ Fa1.7 for the freely rotating active polymer (FRAP) where the ABP rotates freely. Meanwhile, the conformation of the adsorbed polymer, such as adsorbed trains on NP and the tail near the ABP, are also dependent on Fa. When the tail near the ABP is short, the adsorption is significantly affected by the active force. However, when the tail is long, the whole polymer can be viewed as a long tail stretched by the active force and unperturbed adsorption monomers. Simulation results show that the active force has a direct and significant effect on εpn* and the structure of the adsorbed active polymers.

Decomposition of methanol-d4 on Rh nanoclusters supported by thin-film Al2O3/NiAl(100) under near-ambient-pressure conditions.

The decomposition of methanol-d4 (CD3OD) on Rh nanoclusters grown by the deposition of Rh vapors onto an ordered thin film of Al2O3/NiAl(100) was studied, with various surface-probe techniques and largely under near-ambient-pressure (NAP) conditions. The results showed a superior reactivity of small Rh clusters (diameter < 1.5 nm) exposed to CD3OD at 5 × 10-3-0.1 mbar at 400 K; the gaseous production of CO and D2 from decomposed methanol-d4 per Rh surface site on the small Rh clusters with diameters of ∼1.1 nm was nearly 8 times that on large ones with diameters of ∼3.5 nm. The promotion of reactivity with decreased cluster size under NAP conditions was evidently greater than that under ultrahigh vacuum conditions. Moreover, the concentration of atomic carbon (C*; where * denotes adsorbate)-a key catalyst poisoner-yielded from the dissociation of CO* from dehydrogenated methanol-d4 was significantly smaller on small clusters (diameter < 1.5 nm). The NAP size effect on methanol-d4 decomposition involved the surface hydroxyl (OH*) from the little co-adsorbed water (H2O*) that was dissociated at a probability dependent on the cluster size. H2O* was more likely dissociated into OH* on small Rh clusters, by virtue of their more reactive d-band structure, and the OH* then effectively promoted the O-D cleavage of methanol-d4, as the rate-determining step, and thus the reaction probability; on the other hand, the OH* limited CO* dissociation on small Rh clusters via both adsorbate and lateral effects. These results suggest that the superior properties of small Rh clusters in both reactivity and anti-poisoning would persist and be highly applicable under "real-world" catalysis conditions.

Translocation of two-dimensional active polymers through nanopores using Langevin dynamics simulations.

The translocation of polymers through nanopores is a complex process influenced by various factors. In this study, the translocation behavior of a two-dimensional active polymer chain, comprised of a head active Brownian particle (ABP) and a tail passive polymer chain, through a nanopore is studied using Langevin dynamics simulations. Results show that the effect of the self-propulsion force of the ABP on the translocation differs significantly from the driving force inside the pore for traditional polymer translocations. Specifically, the translocation time τ initially increases with increasing the magnitude fs of the self-propulsion force and then decreases with a further increase in fs. A small fs lowers the potential barrier for the translocation and thus promotes slow translocations, whereas a large fs directly pulls the polymer chain through the nanopore following the scaling relation τ ∝ fs-1. Moreover, two asymptotic scaling relations between τ and polymer length N, τ ∝ Nα, are found, with the exponent α of about 2.5 for small fs or long N and the exponent α of about 1.4 for short active polymers with large fs. We discover that the slow rotation of the ABP accelerates the translocation process.

DeepST: identifying spatial domains in spatial transcriptomics by deep learning.

Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expression and histology in situ. Here, we present DeepST, an accurate and universal deep learning framework to identify spatial domains, which performs better than the existing state-of-the-art methods on benchmarking datasets of the human dorsolateral prefrontal cortex. Further testing on a breast cancer ST dataset, we showed that DeepST can dissect spatial domains in cancer tissue at a finer scale. Moreover, DeepST can achieve not only effective batch integration of ST data generated from multiple batches or different technologies, but also expandable capabilities for processing other spatial omics data. Together, our results demonstrate that DeepST has the exceptional capacity for identifying spatial domains, making it a desirable tool to gain novel insights from ST studies.

Dibutyl phthalate (DBP) promotes Epithelial-Mesenchymal Transition (EMT) to aggravate liver fibrosis into cirrhosis and portal hypertension (PHT) via ROS/TGF-ß1/Snail-1 signalling pathway in adult rats.

OBJECTIVE: The primary objective of this study was to investigate the toxicological impact of Dibutyl phthalate (DBP) on the process of liver fibrosis transitioning into cirrhosis and the subsequent development of portal hypertension (PHT) through the mechanism of epithelial-mesenchymal transition (EMT) mediated by the ROS/TGF-ß/Snail-1 signaling pathway. METHOD: Carbon tetrachloride (CCl4) (1 mg/kg) was introduced in adult rats by oral feeding in CCl4 and CCl4+DBP groups twice a week for 8 weeks, and twice for another 8 week in CCl4 group. DBP was introduced by oral feeding in the CCl4+DBP group twice over the following 8 weeks. We subsequently analyzed hemodynamics measurements and liver cirrhosis degree, hepatic inflammation and liver function in the different groups. EMT related genes expression in rats in the groups of Control, DBP, CCl4 and CCl4+DBP were measured by immunohistochemistry (IHC). Enzyme-linked immunosorbent Assay (ELISA), qRT-PCR, western blot were used to detect the EMT related proteins and mRNA gene expression levels in rats and primary hepatocytes (PHCs). Reactive oxygen species (ROS) were examined with a ROS detection kit. RESULTS: The results showed that the CCl4+DBP group had higher portal pressure (PP) and lower mean arterial pressure (MAP) than the other groups. Elevated collagen deposition, profibrotic factor, inflammation, EMT levels were detected in DBP and CCl4+DBP groups. ROS, TGF-ß1 and Snail-1 were highly expressed after DBP exposure in vitro. TGF-ß1 had the potential to regulate Snail-1, and both of them were subject to regulation by ROS. CONCLUSION: DBP could influence the progression of EMT through its toxicological effect by ROS/TGF-ß1/Snail-1 signalling pathway, causing cirrhosis and PHT in final. The findings of this research might contribute to a novel comprehension of the underlying toxicological mechanisms and animal model involved in the progression of cirrhosis and PHT, and potentially offered a promising therapeutic target for the treatment of the disease.

Transcriptome analysis of mesenteric arterioles changes and its mechanisms in cirrhotic rats with portal hypertension.

Portal hypertension (PHT) is a major cause of liver cirrhosis. The formation of portosystemic collateral vessels and splanchnic vasodilation contribute to the development of hyperdynamic circulation, which in turn aggravates PHT and increases the risk of complications. To investigate the changes in mesenteric arterioles in PHT, cirrhotic rat models were established by ligating the common bile ducts. After 4 weeks, the cirrhotic rats suffered from severe PHT and splanchnic hyperdynamic circulation, characterized by increased portal pressure (PP), cardiac output (CO), cardiac index (CI), and superior mesenteric artery (SMA) flow. Mesenteric arterioles in cirrhotic rats displayed remarkable vasodilation, vascular remodeling, and hypocontractility. RNA sequencing was performed based on these findings. A total of 1,637 differentially expressed genes (DEGs) were detected, with 889 up-regulated and 748 down-regulated genes. Signaling pathways related to vascular changes were enriched, including the vascular endothelial growth factor (VEGF), phosphatidylinositol-3-kinase-AKT (PI3K-AKT), and nuclear factor kappa light chain enhancer of activated B cells (NF-κB) signaling pathway, among others. Moreover, the top ten hub genes were screened according to the degree nodes in the protein-protein interaction (PPI) network. Functional enrichment analyses indicated that the hub genes were involved in cell cycle regulation, mitosis, and cellular response to oxidative stress and nitric oxide (NO). In addition, promising candidate drugs for ameliorating PHT, such as resveratrol, were predicted based on hub genes. Taken together, our study highlighted remarkable changes in the mesenteric arterioles of cirrhotic rats with PHT. Transcriptome analyses revealed the potential molecular mechanisms of vascular changes in splanchnic hyperdynamic circulation.

Regional transcriptional vulnerability to basal forebrain functional dysconnectivity in mild cognitive impairment patients.

Nucleus basalis of Meynert (NbM), one of the earliest targets of Alzheimer's disease (AD), may act as a seed for pathological spreading to its connected regions. However, the underlying basis of regional vulnerability to NbM dysconnectivity remains unclear. NbM functional dysconnectivity was assessed using resting-state fMRI data of health controls and mild cognitive impairment (MCI) patients from the Alzheimer's disease Neuroimaging Initiative (ADNI2/GO phase). Transcriptional correlates of NbM dysconnectivity was explored by leveraging public intrinsic and differential post-mortem brain-wide gene expression datasets from Allen Human Brain Atlas (AHBA) and Mount Sinai Brain Bank (MSBB). By constructing an individual-level tissue-specific gene set risk score (TGRS), we evaluated the contribution of NbM dysconnectivity-correlated gene sets to change rate of cerebral spinal fluid (CSF) biomarkers during preclinical stage of AD, as well as to MCI onset age. An independent cohort of health controls and MCI patients from ADNI3 was used to validate our main findings. Between-group comparison revealed significant connectivity reduction between the right NbM and right middle temporal gyrus in MCI. This regional vulnerability to NbM dysconnectivity correlated with intrinsic expression of genes enriched in protein and immune functions, as well as with differential expression of genes enriched in cholinergic receptors, immune, vascular and energy metabolism functions. TGRS of these NbM dysconnectivity-correlated gene sets are associated with longitudinal amyloid-beta change at preclinical stages of AD, and contributed to MCI onset age independent of traditional AD risks. Our findings revealed the transcriptional vulnerability to NbM dysconnectivity and their crucial role in explaining preclinical amyloid-beta change and MCI onset age, which offer new insights into the early AD pathology and encourage more investigation and clinical trials targeting NbM.

Acid-Resistant Near-Infrared II Ag2Se Quantum Dots for Gastrointestinal Imaging.

With the development of near-infrared II (NIR-II) fluorescence imaging, Ag2Se quantum dots (QDs) have become promising label candidates due to their negligible toxicity and narrow band gap. Despite their potential for gastrointestinal (GI) imaging, the application of Ag2Se QDs still presents significant challenges due to issues such as fluorescence extinction or poor stability in the complex digestive microenvironment. Herein, we have proposed a novel approach to the continuous production of Se precursors using glutathione (GSH) as the reductant under acidic conditions, realizing the continuous growth of water-dispersible Ag2Se QDs. The Ag2Se QDs emitting at 600-1100 nm have been successfully synthesized. Meanwhile, the silver-rich surface of the synthesized NIR-II Ag2Se QDs has been passivated well with the dense GSH, resulting in exceptional colloidal stability and photostability and endowing them with acid resistance. As a result, the obtained NIR-II Ag2Se QDs have exhibited remarkable stability in gastric acid, thus enabling their utilization for long-term real-time monitoring of GI peristalsis via NIR-II fluorescence imaging. Moreover, in contrast to conventional barium meal-based X-ray imaging, NIR-II fluorescence imaging with as-prepared NIR-II Ag2Se QDs can offer clearer visualization of fine intestinal structures, with a width as small as 1.07 mm. The developed strategy has offered a new opportunity for the synthesis of acid-resistant nanocrystals, and the acid-resistant, low-toxicity, and biocompatible NIR-II Ag2Se QDs synthesized in this work show a great promise for GI imaging and diagnosis of GI diseases in vivo.

Comprehensive analysis of partial methylation domains in colorectal cancer based on single-cell methylation profiles.

Epigenetic aberrations have played a significant role in affecting the pathophysiological state of colorectal cancer, and global DNA hypomethylation mainly occurs in partial methylation domains (PMDs). However, the distribution of PMDs in individual cells and the heterogeneity between cells are still unclear. In this study, the DNA methylation profiles of colorectal cancer detected by WGBS and scBS-seq were used to depict PMDs in individual cells for the first time. We found that more than half of the entire genome is covered by PMDs. Three subclasses of PMDS have distinct characteristics, and Gain-PMDs cover a higher proportion of protein coding genes. Gain-PMDs have extensive epigenetic heterogeneity between different cells of the same tumor, and the DNA methylation in cells is affected by the tumor microenvironment. In addition, abnormally elevated promoter methylation in Gain-PMDs may further promote the growth, proliferation and metastasis of tumor cells through silent transcription. The PMDs detected in this study have the potential as epigenetic biomarkers and provide a new insight for colorectal cancer research based on single-cell methylation data.

DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor.

Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

Global characterization of B cell receptor repertoire in COVID-19 patients by single-cell V(D)J sequencing.

The world is facing a pandemic of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Adaptive immune responses are essential for SARS-CoV-2 virus clearance. Although a large body of studies have been conducted to investigate the immune mechanism in COVID-19 patients, we still lack a comprehensive understanding of the BCR repertoire in patients. In this study, we used the single-cell V(D)J sequencing to characterize the BCR repertoire across convalescent COVID-19 patients. We observed that the BCR diversity was significantly reduced in disease compared with healthy controls. And BCRs tend to skew toward different V gene segments in COVID-19 and healthy controls. The CDR3 sequences of heavy chain in clonal BCRs in patients were more convergent than that in healthy controls. In addition, we discovered increased IgG and IgA isotypes in the disease, including IgG1, IgG3 and IgA1. In all clonal BCRs, IgG isotypes had the most frequent class switch recombination events and the highest somatic hypermutation rate, especially IgG3. Moreover, we found that an IgG3 cluster from different clonal groups had the same IGHV, IGHJ and CDR3 sequences (IGHV4-4-CARLANTNQFYDSSSYLNAMDVW-IGHJ6). Overall, our study provides a comprehensive characterization of the BCR repertoire in COVID-19 patients, which contributes to the understanding of the mechanism for the immune response to SARS-CoV-2 infection.