Biology subjects

Bao, F.

Publications and source records attributed to Bao, F..

8 recordsLinked to original sources

bioRxiv2025

Three-dimensional spatial transcriptomics at isotropic resolution enabled by generative deep learning

Mapping the complete three-dimensional (3D), transcriptome-wide spatial architecture of tissues and organs remains a fundamental challenge in biology. Typically, studies approximate 3D structures by profiling serial two-dimensional (2D) tissue sections, resulting in incomplete and anisotropic reconstructions. Here, we demonstrate that organ-wide, isotropic-resolution 3D gene expression landscapes can be effectively inferred from sparse 2D spatial transcriptomics data using artificial intelligence. Our framework, isoST, leverages stochastic differential equations (SDEs) to model the intrinsic biological continuity of gene expression along tissue depth, enabling the integration of discrete 2D measurements into coherent 3D transcriptomic maps. isoST is applied across diverse biological systems, including the brain, kidney, spinal cord, and gastrulating embryos, accurately reconstructing anatomical structures and molecular patterns that are only partially resolved in 2D analyses. isoST enables the comprehensive characterization of tissue niches at full 3D resolution, uncovering radial and spherical gene expression gradients associated with disease progression that are often distorted or missed in 2D approaches. Furthermore, isoST is extended to integrate histological imaging for reducing experimental complexity and to reconstruct high-resolution developmental spatial trajectories over time. Together, isoST provides a practical and scalable solution toward comprehensive high-resolution 3D spatial transcriptomic atlases.

bioinformatics↗

bioRxiv2024

Calorie Restriction Induces Degeneration of Neurons with Mitochondrial DNA Depletion by Altering ER-Mitochondria Calcium Transfer

Mitochondrial DNA (mtDNA) mutations and/or depletion are implicated in epilepsy and many neurodegenerative diseases. However, systematic investigation into how mtDNA alterations relate to epilepsy and neural degeneration is needed. Here, we established a mouse model where in mtDNA depletion induced by Herpes Simplex Virus Type 1 (HSV-1) protein-UL12.5 in the brain led to an epileptic phenotype characterized by abnormal electroencephalography (EEG) patterns and increased neural excitability in hippocampus. We also found that UL12.5 mediated mtDNA depletion in neurons in vitro (rho-) causes epilepsy-like abnormal EEG. Caloric restriction (CR) is a strategy proven to reduce epileptic activity, however CR mimetic 2-deoxy-D-glucose (2-DG), induced degeneration in mtDNA depleted neurons. Mechanistically, mtDNA depletion increased mitochondria-endoplasmic reticulum (ER) contacts, facilitating CR-induced mitochondrial calcium overload. Rho- neurons did not show changes in mitochondrial motility or membrane potential. Our study revealed an unexpected axis of mtDNA depletion, ER-mitochondrial contacts, and calcium overload in the rho- neuron model. This is the first description of animal and neuronal models of mitochondrial epilepsy. Our findings with these models suggest that CR may not be a viable clinical intervention in patients with mtDNA depletion.

cell biology↗

bioRxiv2021

A chloride efflux transporter OsBIRG1 regulates grain size and salt tolerance in rice

Grain size is determined by the number of cells and cell size of the grain. Regulation of grain size is crucial for improving crop yield. However, the genes and underlying molecular mechanisms controlling grain size remain elusive. Here we report a member of Detoxification efflux carrier (DTX)/Multidrug and Toxic Compound Extrusion (MATE) family transporter, BIG RICE GRAIN 1 (BIRG1), negatively regulates the grain size in rice. BIRG1 is highly expressed in reproductive organs and roots. In birg1 grain, the size of the outer parenchyma layer cells of spikelet hulls is noticeably larger but the cell number is not altered compared with that in the wild-type (WT) grain. When expressed in Xenopus oocytes, BIRG1 exhibits chloride efflux activity. In line with the role of BIRG1 in mediating chloride efflux, the birg1 mutant shows reduced tolerance to salt stress under which the chloride level is toxic. Moreover, the birg1 grains contain higher level of chloride compared to WT grains when grown under normal paddy field. The birg1 roots accumulate more chloride than those of WT under saline condition. Collectively, our findings suggest that BIRG1 functions as a chloride efflux transporter regulating grain size and salt tolerance via controlling chloride homeostasis in rice.

plant biology↗

bioRxiv2020

Characterizing tissue composition through combined analysis of single-cell morphologies and transcriptional states

Advances in spatial transcriptomics technologies enable optical profiling of morphological and transcriptional modalities from the same cells within tissues. Here, we present multi-modal structured embedding (MUSE), an approach to deeply characterize tissue heterogeneity through analysis of combined image and transcriptional single-cell measurements. We demonstrate that MUSE can discover cellular subpopulations missed by either modality as well as compensate for modality-specific noise. MUSE identified biologically meaningful cellular subpopulations and stereotyped spatial patterning within heterogeneous mouse cortex brain tissues, profiled by seqFISH+ or STARmap technologies. MUSE provides a framework for combining multi-modal single-cell data to reveal deeper insights into the states, functions and organization of cells in complex biological tissues.

bioinformatics↗

bioRxiv2019

Explaining the genetic causality for complex diseases via deep association kernel learning

The genetic effect explains the causality from genetic mutation to the development of complex diseases. Existing genome-wide association study (GWAS) approaches are always built under a linear assumption, restricting their generalization in dissecting complicated causality such as the recessive genetic effect. Therefore, a sophisticated and general GWAS model that can work with different types of genetic effects is highly desired. Here, we introduce a Deep Association Kernel learning (DAK) model to enable automatic causal genotype encoding for GWAS at pathway level. DAK can detect both common and rare variants with complicated genetic effects that existing approaches fail. When applied to real-world GWAS data, our approach discovered potential casual pathways that could be explained by alternative biological studies.

bioinformatics↗

bioRxiv2019

Giotto, a pipeline for integrative analysis and visualization of single-cell spatial transcriptomic data

The rapid development of novel spatial transcriptomic and proteomic technologies has provided new opportunities to investigate the interactions between cells and their native microenvironment. However, effective use of such technologies requires the development of innovative computational tools that are easily accessible and intuitive to use. Here we present Giotto, a comprehensive, flexible, robust, and open-source toolbox for spatial transcriptomic and proteomic data analysis and visualization. The data analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing cell-type distribution, spatially coherent gene expression patterns, and interactions between each cell and its surrounding neighbors. Furthermore, Giotto can also be used in conjunction with external single-cell RNAseq data to infer the spatial enrichment of cell types from data that do not have single-cell resolution. The data visualization module allows users to interactively visualize the gene expression data, analysis outputs, and additional imaging features, thereby providing a user-friendly workspace to explore multiple modalities of information for biological investigation. These two modules can be used iteratively for refined analysis and hypothesis development. We applied Giotto to a wide range of public datasets encompassing diverse technologies and platforms, thereby demonstrating its general applicability for spatial transcriptomic and proteomic data analysis and visualization.

bioinformatics↗

bioRxiv2018

Massive single-cell RNA-seq analysis and imputation via deep learning

Recent advances in large-scale single cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states within heterogeneous tissues. We present scScope, a scalable deep-learning based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles.

bioinformatics↗

bioRxiv2017

p53 binding sites in normal and cancer cells are characterized by distinct chromatin context

The tumor suppressor protein p53 interacts with DNA in a sequence-dependent manner. Thousands of p53 binding sites have been mapped genome-wide in normal and cancer cells. However, the way p53 selectively binds its cognate sites in different types of cells is not fully understood. Here, we performed a comprehensive analysis of 25 published p53 cistromes and identified 3,551 and 6,039 high-confidence binding sites in normal and cancer cells, respectively. Our analysis revealed two distinct epigenetic features underlying p53-DNA interactions in vivo. First, p53 binding sites are associated with transcriptionally active histone marks (H3K4me3 and H3K36me3) in normal-cell chromatin, but with repressive histone marks (H3K27me3) in cancer-cell chromatin. Second, p53 binding sites in cancer cells are characterized by a lower level of DNA methylation than their counterparts in normal cells, probably related to global hypomethylation in cancers. Intriguingly, regardless of the cell type, p53 sites are highly enriched in the endogenous retroviral elements of the ERV1 family, highlighting the importance of this repeat family in shaping the transcriptional network of p53. Moreover, the p53 sites exhibit an unusual combination of chromatin patterns: high nucleosome occupancy and, at the same time, high sensitivity to DNase I. Our results suggest that p53 can access its target sites in a chromatin environment that is non-permissive to most DNA-binding transcription factors, which may allow p53 to act as a pioneer transcription factor in the context of chromatin.

genomics↗