About

Han Xiao serves as the Director of the SynthX Center and holds the position of Associate Professor within the Department of Chemistry, Biosciences, and Bioengineering at Rice University. His research focuses on understanding complex biological systems and developing novel therapeutic approaches through the interface of chemistry and biology. His program emphasizes the development of chemical tools to precisely probe and manipulate biological systems, with interests including adding new building blocks with unique properties into biological systems, enhancing chemical biological tools for various applications, and exploring their therapeutic utilities in cancer, autoimmune, and metabolic diseases. Han Xiao's work has a strong translational focus, seeking to initiate new clinical opportunities and contribute to advances in chemical biology, glycobiology, and cancer immunology.

Research topics

• Biochemistry
• Immunology
• Biology
• Medicine
• Internal medicine
• Oncology
• Chemistry
• Cancer research
• Organic chemistry
• Cell biology
• Biophysics
• Computational biology
• Combinatorial chemistry
• Gastroenterology
• Neuroscience

Selected publications

• Alterations of the gut microbiota and their clinical associations in AIDS patients with Talaromyces marneffei co-infection

  Research Square · 2026-05-13

  preprintOpen access
  PublisherOA PDFDOI

• Et₃B-catalyzed 1,3-sulfonyl migration of N-sulfonyl ynamides

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Sequence Display enables large-scale sequence–activity datasets for rapid protein evolution

  Nature Biotechnology · 2026-04-08

  articleOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

• Dynamic changes in mortality risk factors for acute leukemia patients in the ICU and Post-ICU periods

  Annals of Hematology · 2026-02-04

  articleOpen access

  Acute leukemia (AL) is an aggressive hematologic malignancy that often causes serious complications and requires intensive care unit (ICU) treatment. Identifying risk factors for mortality in AL patients during and post-ICU admission can improve prognosis. This retrospective study enrolled AL patients first admitted to the ICU of the Third Xiangya Hospital, Central South University, from November 2008 to October 2023. It analyzed risk factors associated with ICU mortality, 1-month mortality and 6-month mortality, and conducted a subgroup analysis of the prognosis of ICU survivors. A total of 126 patients were included in this study, with a median age of 42 years. Acute respiratory failure (46%) and sepsis (23%) were the main reasons for ICU admission. Overall, the mortality rates were 57.9% in the ICU, 67.5% at 1 month, and 69% at 6 months. Notably, among the ICU survivors, 26.4% (14/53) died within 6 months, with 85.7% (12/14) occurring within the first month. The time from hospital admission to ICU transfer >10 days and invasive mechanical ventilation were independent risk factors for ICU mortality, 1-month mortality and 6-month mortality. Relapsed or refractory leukemia (OR = 20.715) and low albumin levels (OR = 34.428) were independent risk factors for death within one month among ICU survivors. The risk factors for mortality in AL patients admitted to the ICU exhibit dynamic changes across different stages. For ICU survivors, optimizing nutritional support and initiating anti-tumor treatment as early as possible after discharge are crucial for improving prognosis.

  PublisherOA PDFDOI

• Raw Illumina Sequencing Data for the Large-Scale 5NNK SlugCas9 Sequence–Activity Dataset

  Open MIND · 2026-03-02

  datasetSenior author

  This repository contains the raw Illumina sequencing data for the large-scale 5NNK SlugCas9 sequence–activity dataset, associated Illumina run information files, and the code used to process the raw data and generate the final sequence–activity dataset (CSV).

  DOI

• Integrating tumor habitat heterogeneity with a hybrid deep learning architecture for ultrasound radiomics: a dual-center study on non-invasive prediction of PD-L1 expression in triple-negative breast cancer

  Breast Cancer Research · 2026-04-04

  articleOpen access

  We sought to create a non-invasive method for predicting programmed death-ligand 1 (PD-L1) expression in triple-negative breast cancer (TNBC) by combining ultrasound radiomics with tumor habitat analysis and a Transformer-ResNet hybrid deep learning approach. Pathologically confirmed TNBC patients treated from January 2020 through December 2024 at two centers were retrospectively analyzed. Pretreatment ultrasound images and PD-L1 immunohistochemistry results were collected, with positivity defined as a combined positive score ≥ 10. We applied K-means clustering to partition tumor regions into three habitat zones and extracted radiomic features from each zone separately. Transformer and ResNet networks provided additional deep learning features. A multi-stage selection process—including intraclass correlation coefficient testing, univariate screening, correlation filtering, and LASSO regression—was used to build Habitat, Transformer, and ResNet models individually. These were then merged into a Combined nomogram. Model performance was examined through ROC curves, calibration plots, and decision curve analysis. Six hundred fifty-four patients were enrolled (252 with PD-L1 positivity; 402 without). Training used 457 cases from Fujian Medical University Union Hospital; external validation involved 197 cases from the First Affiliated Hospital of Xiamen University. Zone 3 yielded the most predictive features (n = 18). Training AUCs reached 0.843, 0.869, 0.854, and 0.945 for Habitat, Transformer, ResNet, and Combined models respectively. External validation AUCs were 0.812, 0.842, 0.827, and 0.946 respectively. The Combined approach exceeded individual models by 10.4–13.4% and showed superior net benefit at threshold probabilities from 0.2 to 0.7. Our Combined model accurately predicts PD-L1 status in TNBC using integrated habitat and deep learning features while offering a practical imaging biomarker for immunotherapy candidate selection.

  PublisherDOI

• 26-A-14775-ACC GUT MICROBIAL SIGNATURES OF HYPERTENSION ASSOCIATED WITH DIETARY AND SLEEP PHENOTYPES

  Journal of the American College of Cardiology · 2026-03-27

  article
  PublisherDOI

• Real‐Time Detection of Burn Wound Infection via a Turn‐On NIR‐II Fluorescent Probe

  Chemistry - A European Journal · 2026-04-10

  articleOpen accessSenior authorCorresponding

  ABSTRACT Skin infections represent a major clinical challenge and are frequently associated with persistent bacterial colonization and delayed wound healing. The progression of these skin infections, particularly in burn wounds, is directly mirrored by an elevation in local pH, which serves as a critical physicochemical marker of bacterial metabolic activity. However, studies using pH as a biomarker for real‐time, in situ monitoring of infection status remain relatively limited. Current pH‐responsive probes are often restricted by shallow tissue penetration and significant background interference, which reduce sensitivity and imaging contrast in complex biological environments. Herein, using a difluoroboron (BF 2 ) formazanate scaffold, we designed a series of NIR‐II probes that are selectively activated by elevated pH associated with bacterial infection. Within these probes, AlkaP‐BF 2 exhibits strong NIR‐II emission (> 1000 nm) and displays a “turn‐on” fluorescence over the pH range of 7.26–9.23. The utility of AlkaP‐BF 2 was demonstrated by monitoring intracellular pH changes in bacterial cells and visualizing infection‐associated pH elevations within burn wounds using NIR‐II fluorescence imaging. Collectively, these findings establish AlkaP‐BF 2 as a powerful noninvasive tool that enables the precise, real‐time visualization of infection‐associated microenvironmental changes in skin tissues.

  PublisherOA PDFDOI

• Raw Illumina Sequencing Data for the Large-Scale 5NNK SlugCas9 Sequence–Activity Dataset

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-02 · 1 citations

  datasetOpen accessSenior author

  This repository contains the raw Illumina sequencing data for the large-scale 5NNK SlugCas9 sequence–activity dataset, associated Illumina run information files, and the code used to process the raw data and generate the final sequence–activity dataset (CSV).

  PublisherDOI

• Self‐Assembled <i>Dictamni Cortex</i> Nanoparticles Ameliorate Psoriasis by Epigenetic Modulation of HSP90AB1 and Suppression of the Inflammatory Response

  Advanced Science · 2025-10-20 · 2 citations

  articleOpen access

  Psoriasis, a chronic immune-mediated dermatological disease with high recurrence rates and limited therapeutic efficacy, requires novel treatment strategies. Dictamni Cortex (BXP), a traditional Chinese medicine (TCM), has demonstrated potential in alleviating psoriasis; however, its clinical application is hampered by poor water solubility and low bioavailability. It is developed infinite coordination polymer nanoparticles (BXP-Fe (III) ICPs, NB), which enhance the aqueous solubility by 95-fold and bioavailability of BXP, exerting therapeutic effect through efficient transdermal delivery. NB significantly suppresses keratinocyte hyperproliferation, inflammation, and oxidative stress in both M5 (a cocktail of cytokines)-treated human epidermal keratinocytes (HEKa) cells and imiquimod (IMQ)-induced psoriatic mice. Nascent proteomics identified heat shock protein 90 alpha family class B member 1 (HSP90AB1) as a key target downregulated by NB. It is further revealed that NB suppresses HSP90AB1 transcription by inhibiting its activator, CCCTC-binding factor (CTCF), and disrupts the HSP90AB1-CDC37 (cell division cycle 37, the co-chaperone) chaperone complex, thereby inactivating the pivotal client proteins STAT3 and Akt. Notably, NB demonstrated superior therapeutic efficacy over the canonical HSP90 inhibitor AUY922. This study highlights NB as a promising topical nanotherapy for psoriasis, integrating TCM with modern nanotechnology to overcome pharmacological limitations. The underlying molecular mechanisms of NB are elucidated through the CTCF-HSP90AB1-STAT3 axis.

  PublisherOA PDFDOI

Recent grants

• Targeting Siglec-9/Sialoglycan Interactions to Enhance NK Functions During HIV Infection

  NIH · $3.1M · 2021–2027

• Modulation of Epigenetic Target in the Bone to Treat Breast Cancer Metastasis

  NIH · $1.9M · 2023–2028

• Engineering Proteins with Noncanonical Amino Acids

  NIH · $2.6M · 2019–2029

• Development of Bone-Targeting Antibodies for Ewing Sarcoma Using Genetic Code Expansion

  NIH · $568k · 2021–2024

Frequent coauthors

• Rori Salvaggio

  Memorial Sloan Kettering Cancer Center

  38 shared

• Wendy Perchick

  Memorial Sloan Kettering Cancer Center

  37 shared

• Lior Gazit

  37 shared

• Brett A. Simon

  37 shared

• Dmitriy Gorenshteyn

  37 shared

• Alice Zervoudakis

  Memorial Sloan Kettering Cancer Center

  37 shared

• Lynn Adams

  Memorial Sloan Kettering Cancer Center

  37 shared

• Mikel Ross

  Memorial Sloan Kettering Cancer Center

  37 shared

Labs

• XIAO LABPI

  Not provided

Education

• Postdoctoral Fellow, Chemistry

  Stanford University

  2017

• PhD, Chemistry

  Scripps Research Institute

  2015

• BS in Chemistry, Chemistry

  University of Science and Technology of China

  2010

Awards & honors

• Level 2 Breast Cancer Research Program Breakthrough Award (D…
• Maximizing Investigators’ Research Award for Early Stage Inv…
• Norman Hackerman - Welch Young Investigator Award
• CPRIT Faculty Recruitment Award