Irene A. Chen
· PhDVerifiedUniversity of California, Los Angeles · Chemistry and Biochemistry
Active 1955–2026
About
Irene A. Chen is a Professor in the Department of Chemical and Biomolecular Engineering at the University of California, Los Angeles, and also holds a position in the School of Chemistry and Biochemistry. Her laboratory studies life-like biochemical systems to understand their fundamental properties and address emerging challenges in biotechnology and infectious disease. Her research encompasses the development and analysis of biochemical systems, including the design of antibacterial agents, antimicrobial polymers, and engineered phages, as well as the investigation of prebiotic chemistry, ribozyme evolution, and the origins of life. She has contributed to understanding the properties of RNA, the evolution of catalytic RNA, and the application of phage engineering in antimicrobial therapy. Her work integrates biochemistry, biophysics, microbiology, and nanotechnology to explore the molecular mechanisms underlying biological systems and their potential applications.
Research topics
- Biology
- Computer Science
- Chemistry
- Materials science
- Genetics
- Biochemistry
- Computational biology
- Nanotechnology
- Combinatorics
- Cell biology
- Ecology
- Mathematics
- Algorithm
- Microbiology
- Biophysics
Selected publications
On the Terminology of Lipid Nanoparticles, Liposomes, and Vesicles
ChemSystemsChem · 2026-04-20
articleOpen access1st authorCorrespondingABSTRACT Lipid‐based nanoparticles are critical subjects of study in biotechnology. However, inconsistency in the use of similar terms blurs important biophysical distinctions and confuses newcomers to the field. Here, three terms, namely, “liposome,” “vesicle,” and “lipid nanoparticle,” (LNP) are clarified. Vesicles, including liposomes, consist of a membrane boundary that self‐assembles around an aqueous core due to the hydrophobic effect. LNPs are densely packed structures brought together by electrostatic complexation in addition to the hydrophobic effect, which lack an aqueous core. “Lipid‐based nanoparticles” is a general term that can describe both types of structures. Clarity in terminology is likely to benefit students in the field.
Intellectual frameworks to understand complex biochemical systems at the origin of life
Nature Chemistry · 2025-01-01 · 10 citations
articleSenior authorStudies of the Twin Coherency on Electroless (111) Nanotwins
2025-04-15 · 1 citations
articleRecently, additives-induced highly (111)-textured nanotwins in electroless deposition have revealed significant potential for applications in the electronics industry. This study aims to investigate the mechanical and electrical properties of the electroless deposited highly (111)-textured copper (Cu) that can be heavily influenced by the lattice arrangement within these nanotwins, specifically through twin coherency. Coherency in nanotwinned Cu is often determined by a specific lattice arrangement called Coincidence Site Lattice (CSL) grain boundaries. The degree of this alignment is represented by the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Sigma$</tex> value, with a lower <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Sigma$</tex> value indicating a higher degree of coincidence, generally corresponding to lower energy and a more stable structure. In this study, two (111) textured electroless deposited nanotwinned Cu films with varying proportions of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Sigma 3$</tex> {111} coherent twin boundaries were fabricated. Various tests, including cross-sectional observation, electron backscatter diffraction analysis, nanoindentation, and electrical resistivity, were conducted to assess the impact of twin coherency on this engineering material.
Nucleic Acids Research · 2025-09-16 · 1 citations
articleOpen accessSenior authorGiven concerning trends in antibiotic resistance, phages have been increasingly explored as promising antimicrobial agents. However, a major problem with phage therapy is the overly high specificity of phages for their hosts, which is currently addressed by a personalized approach involving screening a bank of wild-type phages against each clinical isolate. To shorten this process, we propose that a focused library of synthetic phages could be rapidly selected for a member binding to a given clinical isolate. We created libraries of recombinant M13 phages expressing receptor-binding proteins based on the collective metagenome of inovirus phages, a diverse group whose members appear to infect nearly all bacterial phyla. Using two rounds of a pull-down selection, phage variants were identified against several Gram-negative pathogens, including a variant (M13PAB) that bound to several Pseudomonas aeruginosa strains, including clinical isolates. To confer bactericidal activity to the nonlytic phage, a last-line but nephrotoxic lipopeptide, colistin, was cross-linked to the M13PAB virions. The colistin-M13PAB phage conjugate lowered the minimal inhibitory concentration of colistin by 1-2 orders of magnitude for multiple strains of P. aeruginosa and showed a lack of hemolytic or cytotoxic activity in vitro, suggesting high potency combined with low toxicity. Thus, a metagenome-inspired library displayed on the M13 phage scaffold, when subjected to a short selection for binding to a bacterial clinical isolate, could yield a phage variant that targets the specified strain. This approach may improve the speed, consistency, and cost-effectiveness of personalized phage therapy.
ACS Biomaterials Science & Engineering · 2025-05-08
articleOpen accessSenior authorCorrespondingReal-time in vivo imaging of bacterial infections is an important goal to aid the study and treatment of bacterial infections. Phages can be genetically engineered to ensure a particular biomolecular target specificity, and gold nanomaterials can be conjugated to phages for a variety of applications including biosensing. In this paper, we describe methods to use phage-gold nanorod conjugates for in vivo detection and imaging of the bacterial species Pseudomonas aeruginosa in mice. The imaging modalities are computed tomography (CT), using gold as a contrast agent, and fluorescence, which can be applied when the FDA-approved near-infrared (NIR) dye indocyanine green (ICG) is also chemically cross-linked to the bioconjugates. In addition, rapid protocols for validating bioconjugate synthesis and the initial assessment of toxicity are given. In this example, the phage-gold nanorod probe is shown to specifically highlight P. aeruginosa without cross-reactivity to another Gram-negative organism (V. cholerae) in vivo and appears to be biocompatible. Phage-directed imaging probes may thus be useful for the characterization and diagnosis of bacterial infections.
Publisher Correction: Protocells by spontaneous reaction of cysteine with short-chain thioesters
Nature Chemistry · 2025-01-21
erratumOpen accessThe Journal of Rheumatology · 2025-05-20
articleOpen accessPT012 / #274 Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes POSTER TOUR 03: RECENT ADVANCEMENTS IN SLE CLINICAL OUTCOMES AND THERAPY 23-05-2025 10:00 AM - 10:40 AM Background/Purpose Antinuclear antibody (ANA) immunofluorescence (IFA) patterns and titres are a key part of rheumatology diagnostics, however, there is considerable intra- and interlaboratory variability with manual interpretation. Replacing manual interpretation with a standardized and automated approach could help reduce variability, increasing laboratory accuracy and efficiency. We developed machine learning (ML) models (ANA Reader©) to aid laboratories in ANA pattern and titre interpretation, including a model for the nuclear dense fine-speckled (DFS) ANA pattern (AC-2), a rare pattern among systemic autoimmune rheumatic disease (SARD) patients that decreases the likelihood of these conditions. Methods 13,671 ANA images from SLE patients enrolled in the Systemic Lupus International Collaborating Clinics Inception Cohort (SLICC, n=2,825 images), non-SLE subjects enrolled in the Ontario Health Study (OHS, n=10,639 images), and the International Consensus on ANA Patterns (ICAP, n=207 images) were analyzed. All SLICC and OHS ANA were performed in 1 central laboratory using IFA on HEp-2 cells (NovaLite, Werfen, SD) and read on a digital IFA microscope (NovaView, Werfen, SD). As the reference standard, 1 laboratory technologist (HH) with >30 years of experience in ANA studies interpreted 13 ANA patterns and titre for each image. We developed and compared the performance of 8 ML models for ANA pattern recognition. To evaluate ANA titre, we used an ML technique for imaging processing that identified individual HEp-2 cells in the ANA images and then calculated the cell illuminance and cut-offs corresponding to each titre (1:80-1:5120). Fifty images were randomly selected to compare the titre classification based on image processing with the lab technologist as the reference standard. Results 6,307 images containing at least 1 ANA pattern (≥1:80) from SLICC (n=2,806 images), OHS (n=3339 images), and ICAP (n=162 images) were included. We identified 1 ML model (ANA Reader©) with the best performance for ANA pattern identification compared to the reference with a high area under the curve (AUC) score of 83.4%, modest weighted accuracy of 68.4%, precision of 67.1%, sensitivity of 70.1%, and F1 score of 67.2%. The AUC for individual ANA patterns ranged from 0.71 to 0.97 (Figure 1). There was a strong correlation between titres reported by the ANA Reader© and the technologist’s interpretation (Spearman rank 0.93, p <0.0001), where the titres reported were identical or differed by only 1 dilution in most cases (96.0%). The ANA patterns with the best performance were centromere (AUC 0.97) and pleomorphic patterns (AUC 0.97). On average, there were 5 images per patient sample for SLICC, 3 images per patient sample for OHS, and 1 image per patient from ICAP. 80% of the images were used for model training and the remaining 20% for validation. In total, there were 512 patients in the SLICC cohort, 3,559 individuals in the OHS cohort, and 207 patients from ICAP who were included in the study. Figure 1. Area under the curve (AUC) scores for the 13 antinuclear antibody (ANA) patterns using the ANA Reader© model, which had the best performance compared to 7 other machine learning techniques. Conclusions ML has the potential to become a highly effective and efficient approach to evaluating ANA patterns and titres. The performance of our ANA Reader© is expected to improve as we continue to train our models with more ANA images. Future external validation studies and the development of other ML models to predict more complex and multiple ANA patterns and titres are also underway.
ACS Central Science · 2025-07-31 · 1 citations
articleOpen accessSenior authorCorrespondingwas well-tolerated, with no toxic effects. Conjugates of antimicrobial peptides and synthetic phages combine engineerable targeting with large payload capacity, improving potency and therapeutic index for otherwise toxic molecules.
The Journal of Rheumatology · 2025-07-01 · 1 citations
articleOpen accessObjectives Antinuclear antibody (ANA) immunofluorescence (IFA) patterns and titers are a key part of rheumatology diagnostics, however, there is considerable intra- and inter-laboratory variability with manual interpretation. Replacing manual interpretation with a standardized and automated approach could help reduce variability, increasing laboratory accuracy and efficiency. We developed and compared 8 different machine learning (ML) models to aid laboratories in ANA pattern and titer interpretation. Methods 13,671 ANA images from SLE patients enrolled in the Systemic Lupus International Collaborating Clinics Inception Cohort (SLICC, n=2,825 images), non-SLE subjects enrolled in the Ontario Health Study (OHS, n=10,639 images), and the International Consensus on ANA Patterns (ICAP, n=207 images) were analyzed. All SLICC and OHS ANA were performed in 1 central laboratory using IFA on HEp-2 cells (NovaLite, Werfen, SD) and read on a digital IFA microscope (NovaView, Werfen, SD). One laboratory technologist (HH) with >30 years of experience in ANA studies interpreted 13 ANA patterns and titer for each image. We developed and compared the performance of 8 ML models for ANA pattern recognition. To evaluate ANA titer, we used ML to identify individual HEp-2 cells in the ANA images and then calculated cell illuminance and cut-offs corresponding to each titer (1:80-1:5120). Fifty images were randomly selected to compare the titer classification based on image processing with the lab technologist as the reference standard. Results 6,307 images containing at least 1 ANA pattern (≥1:80) from SLICC (n=2806 images), OHS (n=3339 images), and ICAP (n=162 images) were included. We identified 1 ML model (ANA Reader©) with the best performance for ANA pattern identification compared to the reference with a high area under the curve (AUC) score of 83.4%, modest weighted accuracy of 68.4%, precision of 67.1%, sensitivity of 70.1%, and F1 score of 67.2%. The AUC for individual ANA patterns ranged from 0.71 to 0.97 (Figure 1). There was a strong correlation between titers reported by the ANA Reader© and the technologist’s interpretation (Spearman rank 0.93, p <0.0001), where the titers reported were identical or differed by only 1 dilution in most cases (96.0%). Figure 1. Area-under-the-curve (AUC) scores for the thirteen anti-nuclear antibody (ANA) patterns using the ANA Reader© model, which had the best performance compared to seven other machine learning techniques. The ANA patterns with the best performance were centromere (AUC 0.97) and pleomorphic patterns (AUC 0.97). On average, there were five images per patient sample for SLICC, three images per patient sample for OHS, and one image per patient from ICAP. 80% of the images were used for model training and the remaining 20% for validation. In total, there were 512 patients in the SLICC cohort, 3,559 individuals in the OHS cohort, and 207 patients from ICAP who were included in the study. Conclusion ML has the potential to become a highly effective and efficient approach to evaluating ANA patterns and titers. The performance of our ANA Reader© is expected to improve as we continue to train our models with more ANA images. Future external validation studies and the development of other ML models to predict more complex and multiple ANA patterns and titers are also underway.
Antibody‐Nanoparticle Conjugates in Therapy: Combining the Best of Two Worlds
Small · 2025-03-06 · 30 citations
reviewOpen accessSenior authorCorrespondingMonoclonal antibodies (mAbs) and antibody fragments have revolutionized medicine as highly specific binding agents and inhibitors. At the same time, several types of nanomaterials, including liposomes, lipid nanoparticles (NPs), polymersomes, metal and metal oxide NPs, and protein nanostructures, are increasingly utilized and explored for therapeutic potential due to their versatility, chemical and physical properties, and tunability. However, nanomaterials alone often lack specificity, leading to relatively low efficacy and/or high toxicity. To address this problem, a rapidly emerging area is antibody-nanomaterial conjugates (ANCs), which combine the precise targeting specificity of antibodies with the effector functionality of the nanomaterial. In this review, we give a brief introduction to mAbs and major conjugation techniques, describe major classes of nanomaterials being studied for therapeutic potential, and review the literature on ANCs of each class. Special focus is given to emerging applications including ANCs addressing the blood-brain barrier, ANCs delivering nucleic acids, and light-activated ANCs. While many disease targets are related to cancer, ANCs are also under development to address autoimmune, neurological, and infectious diseases. While important challenges remain, ANCs are poised to become a next-generation therapeutic technology.
Recent grants
NIH · $1.3M · 2016–2020
NIH · $994k · 2016–2022
Transitions: Emergent Microstructures of Protocells
NSF · $758k · 2023–2027
NIH · $24.4M · 2014
Frequent coauthors
- 50 shared
Jack W. Szostak
- 28 shared
José I. Jiménez
- 27 shared
Martin A. Nowak
Harvard University
- 24 shared
Kirill S. Korolev
Boston University
- 23 shared
Sudha Rajamani
- 22 shared
Kevin Leu
University of California, San Francisco
- 20 shared
Celia Blanco
Universidade de Santiago de Compostela
- 18 shared
Ranajay Saha
University of California, Los Angeles
Education
- 2007
Ph.D., Biophysics
Harvard University
- 2007
M.D.
Harvard Medical School
- 1999
A.B., Chemistry
Harvard University
Awards & honors
- Camille Dreyfus Teacher-Scholar Award (2018 - 2023)
- Simons Investigator, Collaboration on the Origins of Life (2…
- NIH New Innovator Award (2016 - 2021)
- Searle Scholar Award (2014 - 2017)
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