Biology subjects

Mu, F.

Publications and source records attributed to Mu, F..

11 recordsLinked to original sources

bioRxiv2026

Trem2hi macrophages bridge inflammation resolution and fibrosis initiation after ischemia-reperfusion injury in the kidney

Maladaptive repair of acute kidney injury (AKI) may lead to the development of chronic kidney disease (CKD) characterized by renal fibrosis. Macrophages play roles in AKI-to-CKD progression; however, the interplay between inflammation and fibrosis after AKI remains controversial and the precise role of the distinct macrophage subsets remains elusive. In the present study we identified a unique population of Trem2hi macrophages derived from the bone marrow as a mediator bridging inflammation resolution and fibrosis establishment after kidney injury. Trem2 deficient mice exhibited mitigated renal fibrosis after ischemia-reperfusion injury (IRI) while the renal injury and inflammation persisted. Mechanistically, Trem2 promoted renal inflammation resolution by facilitating macrophage efferocytosis to remove apoptotic tubule cells and reshaping the macrophage cytokine production profile. Loss of Trem2 expression led to excessive cholesterol accumulation in macrophages via Lxr-Abca1/Abcg1 axis and thus sustained pro-inflammatory cytokines production. Moreover, Trem2hi macrophages orchestrated the pro-fibrotic tubular epithelial cells and the activation of myofibroblasts through SPP1 to promote the establishment of renal fibrotic niche. Based on our findings, Trem2hi macrophages may serve as a potential therapeutic target for AKI-to-CKD in combination with anti-inflammatory remedies.

immunology↗

bioRxiv2023

Genetic diversity of the Atacama Desert shrub Huidobria chilensis in the context of geography and climate

Survival in hyperarid deserts is a major challenge for plant life, requiring the development of evolutionary strategies. The Atacama Desert presents harsh conditions such as limited rainfall, crusted soils, high soil salinity, high altitude, and intense solar radiation. These conditions, together with paleoclimatic variability since the past millions of years, have influenced the genetic structure and connectivity of plant populations, resulting in a diverse flora with high endemism. However, the diversification of most lineages appears to be relatively recent, in contrast to proposed age of the Atacama Desert and the onset, evolution and expansion of hyperarid conditions since the Late Oligocene and Early Miocene. A prominent exception is the Atacama paleoendemic Huidobria chilensis (Loasaceae), which is thought to be adapted to such conditions since the Eocene. Still, the environmental limits and thresholds for life in the Atacama remain poorly understood. To investigate the genetic structure in relation to the history of the Atacama Desert, we studied 186 individuals from 11 populations using genotyping-by-sequencing (GBS). Genome-wide single nucleotide polymorphisms (SNPs) were analyzed for population structure and genetic diversity. We identified three genetic clusters corresponding to geographic regions: the coastal region south of Tocopilla, the Coastal Cordillera around Chanaral, and the Copiapo watershed in the south. These clusters as well as genetic diversity were analyzed alongside rainfall, altitude, and landscape data. Although the genetic data generally supports isolation by distance as a major factor for genetic variation between populations, the study also reveals the influence of the topography on the distribution of H. chilensis and highlights the role of hydrologically connected watersheds and rivers in plant migration and colonization. This shapes the species evolutionary trajectory and genetic diversity. Understanding these patterns provides insights into the adaptation and survival strategies of plants in extreme desert environments such as the Atacama.

genetics↗

bioRxiv2023

Assisting and Accelerating NMR Assignment with Restrainted Structure Prediction

NMR experiments can detect in situ structures and dynamic interactions, but the NMR assignment process requires expertise and is time-consuming, thereby limiting its applicability. Deep learning algorithms have been employed to aid in experimental data analysis. In this work, we developed a RASP model which can enhance structure prediction with restraints. Based on the Evoformer and structure module architecture of AlphaFold, this model can predict structure based on sequence and a flexible number of input restraints. Moreover, it can evaluate the consistency between the predicted structure and the imposed restraints. Based on this model, we constructed an iterative NMR NOESY peak assignment pipeline named FAAST, to accelerate assignment process of NOESY restraints and obtaining high quality structure ensemble. The RASP model and FAAST pipeline not only allow for the leveraging of experimental restraints to improve model prediction, but can also facilitate and expedite experimental data analysis with their integrated capabilities.

bioinformatics↗

bioRxiv2022

Multi-omics analyses of single cell-derived colorectal cancer organoids reveal intratumor heterogeneity and immune response diversity

Colorectal cancer (CRC) organoids have similar genomic and functional characteristics to the original specimens. They have become a novel and promising tumor model. However, systematic research of heterogeneity and evolution based on tumor organoids and unbiased evaluation of CD4+ T cell-mediated tumor recognition is presently lacking. Here, we study the variability in genomic characterization and single-cell transcriptome between different tumor organoid clones derived from the same CRC patient. While clone-specific differences in driver gene mutations and cancer stem cell (CSC) subpopulations were observed, inter-clone heterogeneity was even more prevalent. Using single-cell RNA sequencing, we found that compared with bulk organoids, CRC organoid clones are mainly composed of cancer stem cells and transiently-amplifying cells (TACs), but no epithelial-mesenchymal transition (EMT)-like (S100A4+CDH1-) cells. In addition, we study the phenotypic characteristics and TCR properties of tumor-reactive CD4+ T cells by coupling the organoid-TILs co-culture model, single-cell profiling, and recognition testing of candidate TCRs. We found that the proliferating CD4+ T cells increased significantly from 7.1% to 35.4%, and the TCR clonotypes increased from 110 to 1043 after co-culture with organoids. Moreover, 4 of 10 (40%) candidate TCRs with a significant increase in clone abundance exhibited tumor recognition. These results provide evidence that the proliferating CD4+ T cells with clonal expansion after co-culture with tumor organoids are the potential tumor-reactive T cells. Collectively, we reconstructed intra-patient heterogeneity in single cell-derived organoid models and characterized the phenotypic characteristics and functional properties of tumor-reactive CD4+ T cells.

cancer biology↗

bioRxiv2022

Droplet microfluidics forward for tracing target cells at single-cell transcriptome resolution

The rapid promotion of single-cell omics in various fields has begun to help solve many problems encountered in research including precision medicine, prenatal diagnosis, and embryo development. Meanwhile, single-cell techniques are also constantly updated with increasing demand. For some specific target cells, the workflow from droplet screening to single-cell sequencing is a preferred option, which also should reduce the impact of operation steps such as demulsification and cell recovery. We developed an all-in-droplet method integrating cell encapsulation, target sorting, droplet picoinjection, and single-cell transcriptome profiling on chips to achieve a labor-saving monitor of TCR-T cells. As a proof of concept, in this research, TCR-T cells were encapsulated, sorted, and performed single-cell transcriptome sequencing (scRNA-seq) by injecting reagents into droplet. It avoided the tedious operation of droplet breakage and re-encapsulation between droplet sorting and scRNA-seq. Moreover, convenient device operation will accelerate the progress of chip marketization. The strategy achieved an excellent recovery performance of single cell transcriptome with a median gene number over 4000 and a cross-contamination rate of 8.2 {+/-} 2%. Furthermore, this strategy allows us to develop a device with high integrability to monitor infused TCR-T cells, which will promote the development of adoptive T cell immunotherapy and their clinical application.

cell biology↗

bioRxiv2022

An approachable, flexible, and practical machine learning workshop for biologists

The increasing prevalence and importance of machine learning in biological research has created a need for machine learning training resources tailored towards biological researchers. However, existing resources are often inaccessible, infeasible, or inappropriate for biologists because they require significant computational and mathematical knowledge, demand an unrealistic time-investment, or teach skills primarily for computational researchers. We created the Machine Learning for Biologists (ML4Bio) workshop, a short, intensive workshop that empowers biological researchers to comprehend machine learning applications and pursue machine learning collaborations in their own research. The ML4Bio workshop focuses on classification and was designed around 3 principles: (a) focusing on preparedness over fluency or expertise, (b) necessitating minimal coding and mathematical background, and (c) requiring low time investment. It incorporates active learning methods and custom open source software that allows participants to explore machine learning workflows. After multiple sessions to improve workshop design, we performed a study on 3 workshop sessions. Despite some confusion around identifying subtle methodological flaws in machine learning workflows, participants generally reported that the workshop met their goals, provided them with valuable skills and knowledge, and greatly increased their beliefs that they could engage in research that uses machine learning. ML4Bio is an educational tool for biological researchers, and its creation and evaluation provides valuable insight into tailoring educational resources for active researchers in different domains. The workshop materials are available from https://carpentries-incubator.github.io/ml4bio-workshop/ and the ml4bio software is available from https://github.com/gitter-lab/ml4bio.

scientific communication and education↗

bioRxiv2021

Integrating convolution and self-attention improves language model of human genome for interpreting non-coding regions at base-resolution

Interpretation of non-coding genome remains an unsolved challenge in human genetics due to impracticality of exhaustively annotate biochemically active elements in all conditions. Deep learning based computational approaches emerge recently to help interpretating non-coding regions. Here we present LOGO (Language of Genome), a self-attention based contextualized pre-trained language model containing only 2 self-attention layers with 1 million parameters as a substantially light architecture that applies self-supervision techniques to learn bidirectional representations of unlabeled human reference genome. LOGO is then fine-tuned for sequence labelling task, and further extended to variant prioritization task via a special input encoding scheme of alternative alleles followed by adding a convolutional module. Experiments show that LOGO achieves 15% absolute improvement for promoter identification and up to 4.5% absolute improvement for enhancer-promoter interaction prediction. LOGO exhibits state-of-the-art multi-task predictive power on thousands of chromatin features with only 3% parameterization benchmarking against fully supervised model, DeepSEA and 1% parameterization against a recent BERT-based language model for human genome. For allelic-effect prediction, locality introduced by one dimensional convolution shows improved sensitivity and specificity for prioritizing non-coding variants associated with human diseases. In addition, we apply LOGO to interpret type 2 diabetes (T2D) GWAS signals and infer underlying regulatory mechanisms. We make a conceptual analogy between natural language and human genome and demonstrate LOGO is an accurate, fast, scalable, and robust framework to interpret non-coding regions for global sequence labeling as well as for variant prioritization at base-resolution.

bioinformatics↗

bioRxiv2021

Large field of view-spatially resolved transcriptomics at nanoscale resolution

Spatially resolved transcriptomic technologies are promising tools to study cell fate decisions in a physical microenvironment, which is fundamental for enhancing our knowledge of mammalian development. However, the imbalance between resolution, transcript capture and field of view of current methodologies precludes their systematic application to analyze relatively large and three-dimensional mid- and late-gestation mammalian embryos. Here, we combined DNA nanoball (DNB) patterned arrays and tissue RNA capture to create SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq). This approach allows transcriptomic profiling of large histological sections with high resolution and sensitivity. We have applied Stereo-seq to study the kinetics and directionality of transcriptional variation in a time course of mouse organogenesis. We used this information to gain insight into the molecular basis of regional specification, neuronal migration and differentiation in the developing brain. Furthermore, we mapped the expression of a panel of developmental disease-related loci on our global transcriptomic maps to define the spatiotemporal windows of tissue vulnerability. Our panoramic atlas constitutes an essential resource to investigate longstanding questions concerning normal and abnormal mammalian development.

cell biology↗

bioRxiv2020

The expression of PDCD1 and CD274 in T cells and macrophages correlated positively with COVID-19 severity

The immune responses underlying the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain unclear. To help understand the pathology of coronavirus disease 2019 (COVID-19) pandemics, public data were analyzed and the expression of PDCD1 (encoding PD-1) and CD274 (encoding PD-L1) in T cells and macrophages were identified to correlate positively with COVID-19 severity.

immunology↗

bioRxiv2019

Advanced Whole Genome Sequencing Using a Complete PCR-free Massively Parallel Sequencing (MPS) Workflow

BackgroundSystematic errors can be introduced from DNA amplification during massively parallel sequencing (MPS) library preparation and sequencing array formation. Polymerase chain reaction (PCR)-free genomic library preparation methods were previously shown to improve whole genome sequencing (WGS) quality on the Illumina platform, especially in calling insertions and deletions (InDels). We hypothesized that substantial InDel errors continue to be introduced by the remaining PCR step of DNA cluster generation. In addition to library preparation and sequencing, data analysis methods are also important for the accuracy of the output data.In recent years, several machine learning variant calling pipelines have emerged, which can correct the systematic errors from MPS and improve the data performance of variant calling. ResultsHere, PCR-free libraries were sequenced on the PCR-free DNBSEQ arrays from MGI Tech Co., Ltd. (referred to as MGI) to accomplish the first true PCR-free WGS which the whole process is truly not only PCR-free during library preparation but also PCR-free during sequencing. We demonstrated that PCR-based WGS libraries have significantly (about 5 times) more InDel errors than PCR-free libraries.Furthermore, PCR-free WGS libraries sequenced on the PCR-free DNBSEQ platform have up to 55% less InDel errors compared to the NovaSeq platform, confirming that DNA clusters contain PCR-generated errors.In addition, low coverage bias and less than 1% read duplication rate was reproducibly obtained in DNBSEQ PCR-free using either ultrasonic or enzymatic DNA fragmentation MGI kits combined with MGISEQ-2000. Meanwhile, variant calling performance (single-nucleotide polymorphisms (SNPs) F-score>99.94%, InDels F-score>99.6%) exceeded widely accepted standards using machine learning (ML) methods (DeepVariant or DNAscope). ConclusionsEnabled by the new PCR-free library preparation kits, ultra high-thoughput PCR-free sequencers and ML-based variant calling, true PCR-free DNBSEQ WGS provides a powerful solution for improving WGS accuracy while reducing cost and analysis time, thus facilitating future precision medicine, cohort studies, and large population genome projects.

genomics↗

bioRxiv2019

Detection and characterization of copy number variants based on whole-genome sequencing by MGI platforms

BackgroundNext-generation sequence (NGS) has rapidly developed in past years which makes whole-genome sequencing (WGS) becoming a more cost- and time-efficient choice in wide range of biological researches. We usually focus on some variant detection via WGS data, such as detection of single nucleotide polymorphism (SNP), insertion and deletion (Indel) and copy number variant (CNV), which playing an important role in many human diseases. However, the feasibility of CNV detection based on WGS by DNBSEQ platforms was unclear. We systematically analysed the genome-wide CNV detection power of DNBSEQ platforms and Illumina platforms on NA12878 with five commonly used tools, respectively.\n\nResultsDNBSEQ platforms showed stable ability to detect slighter more CNVs on genome-wide (average 1.24-fold than Illumina platforms). Then, CNVs based on DNBSEQ platforms and Illumina platforms were evaluated with two public benchmarks of NA12878, respectively. DNBSEQ and Illumina platforms showed similar sensitivities and precisions on both two benchmarks. Further, the difference between tools for CNV detection was analyzed, and indicated the selection of tool for CNV detection could affected the CNV performance, such as count, distribution, sensitivity and precision.\n\nConclusionThe major contribution of this paper is providing a comprehensive guide for CNV detection based on WGS by DNBSEQ platforms for the first time.

bioinformatics↗