Biology subjects

Li, C. I.

Publications and source records attributed to Li, C. I..

5 recordsLinked to original sources

bioRxiv2026

PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns

Multiplex imaging is a valuable tool for spatially examining tissue microenvironments at the single-cell level to uncover biological and clinical insights. However, most multiplex image analysis workflows currently require manual intervention for cell phenotyping, which slows progress, demands human effort, and yields operator-dependent outputs. Here, we developed PhenoBIC, a pre-trained deep learning model for image classification of the multiplexed biomarker signals in a cell (Biomarker Imprint of a Cell) to classify cell phenotypes. We show that PhenoBIC (F1-score [~]0.88) outperforms manual gating (widely used) and other machine learning-based computational approaches for cell marker expression classification. We validated this across multiple biomarkers, tissue sampling strategies (whole biopsies and tissue microarrays), multiplex panels, imaging platforms, and tissue types. We have released our in-house training and validation datasets of [~]1.4 million manually curated cell expression ground truth labels. We have also open-sourced PhenoBIC and enabled its community-wide deployment via the QuPath interface.

bioinformatics↗

bioRxiv2026

Metabolic specialization structures gut bacterial niches and drives colorectal cancer progression

Despite the established association between the gut microbiome and colorectal cancer (CRC), the functional distinction between microbial passengers and drivers of CRC progression remains unresolved. Here, we collected stool, blood, as well as paired tumor, and normal mucosa tissues from seventy-seven CRC patients to characterize the systemic and localized impact of the gut microbiome on early- and late-stage CRC. By deep shotgun metagenomic sequencing, we identified distinct bacterial species and functions residing in tumor versus normal mucosa, highlighting an enrichment of oral-associated bacteria in tumor tissues. Several of these species remained undetected in the stool microbiome analysis. We further combined bacterial culturing with untargeted metabolomics of bacteria enriched in tumor and normal mucosa tissues, revealing distinct clusters of metabolic potential. Functional testing of multiple members from one cluster comprising both tumor- and mucosa-enriched species revealed Leptotrichia wadei as a pro-tumorigenic bacterium in a murine CRC model. Single-nucleus RNA sequencing and in vitro experiments further demonstrated that L. wadei and its secretome induces M2 macrophage polarization to promote tumor growth. Overall, our study shows that metabolic specialization structures microbial colonization niches, while species-specific metabolic outputs identify functional drivers of CRC progression, and uncovers L. wadei as an oncogenic bacterium in CRC.

microbiology↗

bioRxiv2025

Obesity promotes conserved inflammatory and metabolic transcriptional programs in mouse and human colon tumors

BackgroundThe global prevalence of obesity, an established risk and progression factor for colon cancer, is high and rising. Unfortunately, the mechanisms underlying the obesity-colon cancer association are incompletely understood, and new molecular targets enabling more effective intervention strategies to break the obesity-colon cancer link are urgently needed. ObjectiveThis study integrated RNA sequencing data from mouse and human colon tumor samples, as well as human adipose samples, to rigorously establish obesity-associated transcriptomic signatures conserved between the two species. MethodsWe employed a mouse colon cancer model with colonoscopy-guided orthotopic transplantation of syngeneic Apc-null;KrasG12D/+;Trp53-null;Smad4-null;tdTomato colon tumor organoids. Epithelial cell adhesion molecule (EpCAM)-positive cells from murine tumors, and 193 human colon tumors and 188 human mesenteric adipose tissue samples from the ColoCare cohort underwent transcriptomic analyses. ResultsDiet-induced obesity reduced survival in the mouse model of colon cancer. Integrated transcriptomic analyses of EpCAM-positive murine tumor cells and bulk human tumors revealed obesity-driven enrichment of inflammation and metabolic pathways, including upregulation of genes involved in innate immune sensing (TLR2, MYD88, IRF4) and tumor microenvironment remodeling (MMP9, TGFB1, SERPINE1). Analysis of paired mesenteric visceral adipose tissue and tumor samples from the ColoCare cohort indicated that obesity amplifies inflammatory signaling pathways through unique adipose ligand-tumor receptor interactions. ConclusionsThese results establish obesity-associated adipose tissue dysregulation as a key inter-tissue modulator of biology, with concordant cross-species effects on tumor cell-intrinsic inflammatory and metabolic programs.

cancer biology↗

bioRxiv2024

ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images

Hematoxylin and eosin (H&E) is a common and inexpensive histopathology assay. Though widely used and information-rich, it cannot directly inform about specific molecular markers, which require additional experiments to assess. To address this gap, we present ROSIE, a deep-learning framework that computationally imputes the expression and localization of dozens of proteins from H&E images. Our model is trained on a dataset of over 1000 paired and aligned H&E and multiplex immunofluorescence (mIF) samples from 20 tissues and disease conditions, spanning over 16 million cells. Validation of our in silico mIF staining method on held-out H&E samples demonstrates that the predicted biomarkers are effective in identifying cell phenotypes, particularly distinguishing lymphocytes such as B cells and T cells, which are not readily discernible with H&E staining alone. Additionally, ROSIE facilitates the robust identification of stromal and epithelial microenvironments and immune cell subtypes like tumor-infiltrating lymphocytes (TILs), which are important for understanding tumor-immune interactions and can help inform treatment strategies in cancer research.

pathology↗

bioRxiv2020

A combined proteomics and Mendelian randomization approach to investigate the effects of aspirin-targeted proteins on colorectal cancer

BackgroundEvidence for aspirins chemopreventative properties on colorectal cancer (CRC) is substantial, but its mechanism of action is not well-understood. We combined a proteomic approach with Mendelian randomization (MR) to identify possible new aspirin targets that decrease CRC risk. MethodsHuman colorectal adenoma cells (RG/C2) were treated with aspirin (24 hours) and a stable isotope labelling with amino acids in cell culture (SILAC) based proteomics approach identified altered protein expression. Protein quantitative trait loci (pQTLs) from INTERVAL (N=3,301) and expression QTLs (eQTLs) from the eQTLGen Consortium (N=31,684) were used as genetic proxies for protein and mRNA expression levels. Two-sample MR of mRNA/protein expression on CRC risk was performed using eQTL/pQTL data combined with CRC genetic summary data from the Colon Cancer Family Registry (CCFR), Colorectal Transdisciplinary (CORECT), Genetics and Epidemiology of Colorectal Cancer (GECCO) consortia and UK Biobank (55,168 cases and 65,160 controls). ResultsAltered expression was detected for 125/5886 proteins. Of these, aspirin decreased MCM6, RRM2 and ARFIP2 expression and MR analysis showed that a standard deviation increase in mRNA/protein expression was associated with increased CRC risk (OR:1.08, 95% CI:1.03-1.13, OR:3.33, 95% CI:2.46-4.50 and OR:1.15, 95% CI:1.02-1.29, respectively). ConclusionMCM6 and RRM2 are involved in DNA repair whereby reduced expression may lead to increased DNA aberrations and ultimately cancer cell death, whereas ARFIP2 is involved in actin cytoskeletal regulation indicating a possible role in aspirins reduction of metastasis. ImpactOur approach has shown how laboratory experiments and population-based approaches can combine to identify aspirin-targeted proteins possibly affecting CRC risk.

cancer biology↗