Biology subjects

Bi, T.

Publications and source records attributed to Bi, T..

5 recordsLinked to original sources

bioRxiv2026

Cell Ploidy Modulates Nucleolar Size via Dosage of Nucleolar Organizing Regions

Abnormal nucleolar morphology is a pathognomonic histologic feature of cancer cells. Nucleoli are biomolecular condensates and cytogenetic entities that form around nucleolar organizing regions (NORs), located on the short arms of acrocentric chromosomes. Given that aneuploidy is pervasive in human cancers and can involve NOR-bearing chromosomes, we investigated how aneuploidy-driven changes in NOR dosage impact nucleolar morphology. We used reversine, a spindle assembly checkpoint inhibitor, to induce chromosomal instability and complex aneuploidy in DLD1 colorectal cancer cells (near-euploid) and normal human colon organoids (colonoids, euploid). High resolution 3D imaging and quantitative analyses of reversine-treated cells showed overall doubling of total nucleolar volume per nucleus. Reversine-associated nucleolar enlargement occurred independently of global protein synthesis, and thus was not solely driven by increases in biosynthetic demand. The volume of individual nucleoli showed a direct linear correlation with the number of associated NORs, as revealed by 3D co-localization with NOR-specific FISH probes. These findings establish NOR dosage as a determinant of nucleolar morphology and metric of genomic instability, providing a new paradigm by which aneuploidy can modulate spatial chromosomal constraints and nuclear architecture in cancer. Significance statementCancer cells often show enlarged and abnormal nucleoli. We show that aneuploidy, a common feature of cancer, can directly reshape nucleolar morphology by altering the copy number of acrocentric chromosomal regions around which nucleoli form, NORs. Using high-resolution 3D imaging in cell lines and patient-derived colonoids, we find that nucleolar number and volume directly correlate with NOR number, independent of overall protein synthesis. Individual nucleolar volume scales with the number of 3D NOR associations, and prominent single nucleoli arise when NORs accumulate. These results provide evidence that aneuploidy can lead to aberrant nucleolar morphologies seen in many human cancers.

cancer biology↗

bioRxiv2026

Protein-peptide Interaction Representation Learning with Pretrained Language Models

Protein-peptide Interactions (PpIs) paly essential roles in diverse cellular processes, yet their systematic identification remains challenging due to the limited availability of experimentally annotated protein-peptide interaction data. To address this challenge, we present PepInter, a sequence-based Deep Learning (DL) framework that leverages large-scale pretraining on structurally derived pseudo protein-peptide pairs to learn interaction-relevant representations. Specifically, energy-dominant peptide fragments are extracted from protein complexes curated from non-redundant Protein Data Bank (PDB) structures, enabling the construction of pseudo protein-peptide interaction pairs that capture interface interaction patterns shared with canonical protein-protein interactions. This strategy allows the model to acquire interaction-aware priors in the absence of large-scale annotated protein-peptide complex datasets. Built upon the ESM-Cambrian (ESMC) architecture, PepInter adopts a two-stage pretraining strategy. In the first stage, masked language modeling is used to learn general protein sequence representations. In the second stage, the model is further trained to predict Rosetta-derived energetic scores, explicitly incorporating structural interaction signals into the learned embeddings. Following pretraining, PepInter is fine-tuned for both protein-peptide interaction classification and peptide bioactivity regression tasks. Across multiple benchmark datasets, including protein-peptide binding affinity prediction, PepInter consistently outperforms existing baseline methods and demonstrates strong generalization in identifying biologically meaningful PpIs. Case studies further highlight its ability to recover known interaction patterns and predict novel protein-peptide interactions. Together, these results establish PepInter as a scalable and effective framework for protein-peptide interaction prediction, with strong potential to accelerate peptide-based drug discovery.

bioinformatics↗

bioRxiv2025

Molecular and cellular dynamics of measurable residual disease progression in myelodysplastic syndromes

Cancer relapse after treatment invariably proceeds from the persistence and progression of measurable residual disease (MRD), an ultrasmall malignant population. Despite the poor prognostic impact established by increasingly sensitive MRD detection methods, the molecular pathways defining MRD remain unknown. To identify the unique features of MRD and the molecular forces shaping its progression, we performed single-cell multi-omic profiling on DNA, RNA and protein layers for a longitudinal patient cohort with relapsed myelodysplastic syndromes (MDS) after stem cell transplantation (SCT), the only curative modality for MDS. We provide a comprehensive molecular portrait of MRD cells with novel markers, shared across genetically heterogeneous patients. MDS relapse after SCT manifested universally with marked phenotypic evolution. Genotype and phenotype analyses revealed MRD progression as a dynamic, evolutionary process rather than a static expansionary one, driven by both subclonal sweeping and cell state transitions. Malignant cells adapted to infiltrating T cells by rewiring IFN-{gamma} responses to activate a key immunoevasive pathway. Our study demonstrates the power of longitudinal, single-cell multi-omic analysis for identifying, tracking, and understanding MRD cells, opening new avenues to target MRD persistence and progression.

cancer biology↗

bioRxiv2023

Differential effects of aneuploidy on growth and differentiation in human intestinal stem cells

Aneuploidy, a near ubiquitous genetic feature of tumors, is a context-dependent driver of cancer evolution; however, the mechanistic basis of this role remains unclear. Here, by inducing heterogeneous aneuploidy in non-transformed human colon organoids (colonoids), we investigate how the effects of aneuploidy on cell growth and differentiation may promote malignant transformation. Single-cell RNA sequencing reveals that the gene expression signature across over 100 unique aneuploid karyotypes is enriched with p53 responsive genes. The primary driver of p53 activation is karyotype complexity. Complex aneuploid cells with multiple unbalanced chromosomes activate p53 and undergo G1 cell-cycle arrest, independent of DNA damage and without evidence of senescence. By contrast, simple aneuploid cells with 1-3 chromosomes gained or lost continue to proliferate, demonstrated by single cell tracking in colonoids. Notably, simple aneuploid cells exhibit impaired differentiation when niche factors are withdrawn. These findings suggest that while complex aneuploid cells are eliminated from the normal epithelium due to p53 activation, simple aneuploid cells can escape this checkpoint and may contribute to niche factor-independent growth of cancer-initiating cells.

cell biology↗

bioRxiv2019

A targeted multi-omic analysis approach measures protein expression and low abundance transcripts on the single cell level

High throughput single-cell RNA sequencing (sc-RNAseq) has become a frequently used tool to assess immune cell function and heterogeneity. Recently, the combined measurement of RNA and protein expression by sequencing was developed, which is commonly known as CITE-Seq. Acquisition of protein expression data along with transcriptome data resolves some of the limitations inherent to only assessing transcript, but also nearly doubles the sequencing read depth required per single cell. Furthermore, there is still a paucity of analysis tools to visualize combined transcript-protein datasets.\n\nHere, we describe a novel targeted transcriptomics approach that combines analysis of over 400 genes with simultaneous measurement of over 40 proteins on more than 25,000 cells. This targeted approach requires only about 1/10 of the read depth compared to a whole transcriptome approach while retaining high sensitivity for low abundance transcripts. To analyze these multi-omic transcript-protein datasets, we adapted One-SENSE for intuitive visualization of the relationship of proteins and transcripts on a single-cell level.

immunology↗