Biology subjects

Lian, Q.

Publications and source records attributed to Lian, Q..

28 records · Page 2Linked to original sources

bioRxiv2022

Agonistic anti-CD40 converts Tregs into Type 1 effectors within the tumor micro-environment

Multiple cell types, molecules, and processes contribute to inhibition of anti-tumor effector responses, often frustrating effective immunotherapy. Among these, Foxp3+ CD4+ cells (Tregs) are well-recognized to play an immunosuppressive role in the tumor microenvironment. The first clinically successful checkpoint inhibitor, anti-CTLA-4 antibody, may deplete Tregs at least in part by antibody-dependent cellular cytotoxicity (ADCC), but this effect is unreliable in mice, including in a genetically engineered mouse model of pancreatic ductal adenocarcinoma (PDAC). In contrast, agonistic CD40 antibody, which serves as an effective therapy, is associated with notable Treg disappearance in the PDAC model. The mechanism of CD40-mediated Treg loss is poorly understood, as Tregs are CD40-negative. Here we have explored the mechanistic basis for the loss of Foxp3 T cells upon anti-CD40 treatment and find, using tissue-level multiplex immunostaining and orthogonal dissociated cell analyses, that Tregs are not depleted but converted into interferon-{gamma} (IFN-{gamma}) producing, Type I CD4+ T effector cells. This process depends on IL-12 and IFN-{gamma} signaling evoked by action of the anti-CD40 antibody on dendritic cells (DCs), especially BATF3-dependent cDC1s. These findings provide insight into a previously unappreciated mechanism of CD40 agonism as a potent anti-tumor intervention that promotes the re-programming of Tregs into tumor-reactive CD4+ effector T cells, markedly augmenting the anti-tumor response.

immunology↗

bioRxiv2022

High-frequency synthetic apomixis in hybrid rice

Introducing asexual reproduction through seeds - apomixis - into crop species could revolutionize agriculture by allowing F1 hybrids with enhanced yield and stability to be clonally propagated. Engineering synthetic apomixis has proven feasible in inbred rice through the inactivation of three genes (MiMe), which results in the conversion of meiosis into mitosis in a line ectopically expressing the BABYBOOM1 (BBM1) parthenogenetic trigger in egg cells. However, only 10-30% of the seeds were clonal. We show here that synthetic apomixis can be induced in a F1 hybrid of rice by inducing MiMe mutations and egg cell expression of BBM1 in a single step. We generated hybrid plants that produced more than 95% of clonal seeds across multiple generations. Clonal apomictic plants maintained the phenotype of the F1 hybrid along successive generations. Our results demonstrate that there is no barrier to almost fully penetrant synthetic apomixis in an important crop species, rendering it compatible with use in agriculture.

plant biology↗

bioRxiv2022

TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics

Sequencing-based spatial transcriptomics (ST) is an emerging technique to study in situ gene expression patterns at the whole-genome scale. In addition to transcriptomic data, the technique usually generates matched histopathological images for the same tissue sample. ST data analysis is complicated by severe technical noise; matched histopathological images with high spatial continuity and resolution introduce complementary cellular phenotypical information and provide a chance to mitigate the noise in ST data. Hence, we propose a novel ST data analysis method called transcriptome and histopathological image integrative analysis for spatial transcriptomics (TIST), which integrates the information from sequencing-based ST data and histopathological images. TIST uses a Markov random field (MRF) model to learn the macroscopic cellular features from histopathological images and devises a random-walk-based strategy to integrate the extracted image features, the transcriptomic features and the location information for spatial cluster (SC) identification and gene expression enhancement. We tested TIST both on simulated datasets and on 33 real datasets; we found that TIST achieved superior performance on multiple tasks, which illustrates the utility of this method in facilitating the discovery of biological insights from sequencing-based ST data.

bioinformatics↗

bioRxiv2022

A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences, Bulk RNA-seq, and Single-cell RNA-seq

Alternative polyadenylation (APA) plays important roles in modulating mRNA stability, translation, and subcellular localization, and contributes extensively to shaping eukaryotic transcriptome complexity and proteome diversity. Identification of poly(A) sites (pAs) on a genome-wide scale is a critical step toward understanding the underlying mechanism of APA-mediated gene regulation. A number of established computational tools have been proposed to predict pAs from diverse genomic data. Here we provided an exhaustive overview of computational approaches for predicting pAs from DNA sequences, bulk RNA-seq data, and single-cell RNA-seq (scRNA-seq) data. Particularly, we examined several representative tools using RNA-seq and scRNA-seq data from peripheral blood mononuclear cells and put forward operable suggestions on how to assess the reliability of pAs predicted by different tools. We also proposed practical guidelines on choosing appropriate methods applicable to diverse scenarios. Moreover, we discussed in depth the challenges in improving the performance of pA prediction and benchmarking different methods. Additionally, we highlighted outstanding challenges and opportunities using new machine learning and integrative multi-omics techniques and provided our perspective on how computational methodologies might evolve in the future for non-3 UTR, tissue-specific, cross-species, and single-cell pA prediction.

bioinformatics↗

bioRxiv2022

Dual control of meiotic crossover patterning

Most meiotic crossovers (COs), called class I crossovers, are produced by a conserved pathway catalyzed by the ZMM proteins; COs are limited in number, typically to 1-3 per chromosome, and are prevented from occurring close to one other by crossover interference1-3. In many species, CO number is subject to dimorphism between males and females, and a lower CO number is associated with shorter chromosome axes and stronger interference4. How the patterning of COs is imposed, however, remains poorly understood. Here, we show that overexpression of the ZMM protein HEI10 increases COs and reduces crossover interference but maintains sexual dimorphism; shorter axes length in female meiosis is still associated with fewer COs and stronger interference than in male meiocytes. Disrupting the synaptonemal complex (SC) by mutating ZYP1 also leads to an increase in class I COs but, in contrast, abolishes interference and disrupts the link between chromosome axis length and COs, with female and male meiocytes having the same CO frequency despite different axis lengths. Combining HEI10 overexpression and zyp1 mutation leads to a massive increase in class I COs and absence of interference, while axes lengths are still unaffected. These observations support, and can be effectively predicted by, a recently proposed coarsening model5,6 in which HEI10 diffusion is funneled by the central element of the SC before coarsening into large, well-spaced CO-promoting droplets. Given the conservation of the components, this model may account for CO patterning in many eukaryotes.

genetics↗

bioRxiv2022

The megabase-scale crossover landscape is independent of sequence divergence

Meiotic recombination frequency varies along chromosomes and strongly correlates with sequence divergence. However, the causality underlying this correlation is unclear. To untangle the relationship between recombination landscapes and polymorphisms, we characterized the genome-wide recombination landscape in the absence of polymorphisms, using Arabidopsis thaliana homozygous inbred lines in which a few hundred genetic markers were introduced through mutagenesis. We found that megabase-scale recombination landscapes in inbred lines are strikingly similar to the recombination landscapes in hybrids, with the sole exception of heterozygous large rearrangements where recombination is prevented locally. In addition, we found that the megabase-scale recombination landscape can be accurately predicted by chromatin features. Our results show that polymorphisms are not causal for the shape of the megabase-scale recombination landscape, rather, favor alternative models in which recombination and chromatin shape sequence divergence across the genome.

genetics↗

bioRxiv2021

Comprehensive Analysis of Spatial Architecture in Primary Liver Cancer

Heterogeneity is the major challenge for cancer prevention and therapy. Here, we firstly constructed high-resolution spatial transcriptomes of primary liver cancers (PLCs) containing 84,823 spots within 21 tissues from 7 patients. The sequential comparison of spatial tumor microenvironment (TME) characteristics from non-tumor to leading-edge to tumor regions revealed that the tumor capsule potentially affects intratumor spatial cluster continuity, transcriptome diversity and immune cell infiltration. Locally, we found that the bidirectional ligand-receptor interactions at the 100 m wide cluster-cluster boundary contribute to maintaining intratumor architecture. Our study provides novel insights for diverse tumor ecosystem of PLCs and has potential benefits for cancer intervention.

cancer biology↗

bioRxiv2020

Artificial-Cell-Type Aware Cell Type Classification in CITE-seq

Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), couples the measurement of surface marker proteins with simultaneous sequencing of mRNA at single cell level, which brings accurate cell surface phenotyping to single cell transcriptomics. Unfortunately, multiplets in CITE-seq datasets create artificial cell types and complicates the automation of cell surface phenotyping. We propose CITE-sort, an artificial-cell-type aware surface marker clustering method for CITE-seq. CITE-sort is aware of and is robust to multiplet-induced artificial cell types. We benchmarked CITE-sort with real and simulated CITE-seq datasets and compared CITE-sort against canonical clustering methods. We show that CITE-sort produces the best clustering performance across the board. CITE-sort not only accurately identifies real biological cell types but also consistently and reliably separates multiplet-induced artificial-cell-type droplet clusters from real biological-cell-type droplet clusters. In addition, CITE-sort organizes its clustering process with a binary tree, which facilitates easy interpretation and verification of its clustering result and simplifies cell type annotation with domain knowledge in CITE-seq.

bioinformatics↗

bioRxiv2019

Sample demultiplexing, multiplet detection, experiment planning and novel cell type verification in single cell sequencing

Identifying and removing multiplets from downstream analysis is essential to improve the scalability and reliability of single cell RNA sequencing (scRNA-seq). High multiplet rates create artificial cell types in the dataset. Sample barcoding, including the cell hashing technology and the MULTI-seq technology, enables analytical identification of a fraction of multiplets in a scRNA-seq dataset.\n\nWe propose a Gaussian-mixture-model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes the sample-barcoding-detectable multiplets and estimates the percentage of sample-barcoding-undetectable multiplets in the remaining dataset. GMM-Demux describes the droplet formation process with an augmented binomial probabilistic model, and uses the model to authenticate cell types discovered from a scRNA-seq dataset.\n\nWe conducted two cell-hashing experiments, collected a public cell-hashing dataset, and generated a simulated cellhashing dataset. We compared the classification result of GMM-Demux against a state-of-the-art heuristic-based classifier. We show that GMM-Demux is more accurate, more stable, reduces the error rate by up to 69x, and is capable of reliably recognizing 9 multiplet-induced fake cell types and 8 real cell types in a PBMC scRNA-seq dataset.

bioinformatics↗

bioRxiv2018

Mesenchymal stem cells protect retinal ganglion cells from degeneration via mitochondrial donation

Retinal ganglion cell (RGC) degeneration is extremely hard to repair or regenerate and is often coupled with mitochondrial dysfunction. Mesenchymal stem cells (MSCs)-based treatment has been demonstrated beneficial for RGC against degeneration. However, underlying mechanisms of MSC-provided RGC protection are largely unknown other than neuropectective paracrine actions. In this study, we sought to investigate whether mitochondrial donation can preserve RGC functions, in a mitochondrial Ndufs4 deficient mouse model of RGC degeneration. The results revealed intravitreal transplanted by induced pluripotent stem cell derived-MSCs (iPSC-MSC) could donate their mitochondria through crossing inner limited membrane to host RGCs. Furthermore, the donated mitochondria effectively protected against RGC death and largely preserved retinal function in Ndufs4-KO mice. Importantly, the protective effects of mitochondrial donation from MSCs were associated with management of pro-inflammatory cytokines. Our data identified a novel role of MSCs-mitochondrial donation in protection of RGC from degeneration, and highlight a viable therapeutic strategy by manipulating stem cell mitochondrial donation for the treatment of retina degeneration in future.

cell biology↗