Biology subjects

Chen, D. Z.

Publications and source records attributed to Chen, D. Z..

7 recordsLinked to original sources

bioRxiv2026

ARSA: an autonomous research scientist for target nomination in Alzheimer's disease and related dementias

Expanding therapeutic options for Alzheimer's disease and related dementias (ADRD) requires biologically grounded targets, yet nomination demands labor-intensive analysis and multidisciplinary evidence synthesis. To address this challenge, we present ARSA, an autonomous research scientist that transforms natural-language research interests and molecular data into prioritized, evidence-grounded target shortlists. ARSA formulates and audits hypotheses, adapts molecular analyses to observed results, and prioritizes targets using cross-cohort evidence and disease-specific knowledge, preserving the evidence and decisions underlying each nomination. Across three complementary evaluations, we show that ARSA generates hypotheses corresponding to subsequent research and identifies credible candidates within and beyond community nomination records. In structured assessment by 14 experts spanning all four technical cores of the Indiana University School of Medicine-Purdue University TREAT-AD Center, every expert assigned higher mean credibility to ARSA-retained candidates than to rejected comparators. ARSA enables systematic, transparent target exploration, opening opportunities to broaden the therapeutic mechanisms investigated in ADRD.

bioinformatics↗

bioRxiv2026

Exploring Multi-Scale Local and Global Features in Whole Slide Images Using State Space Models

Whole slide image (WSI) classification is crucial in computational pathology, yet the gigapixel scale of WSIs makes it challenging to extract discriminative and compact WSI-level features for disease diagnosis. In this paper, we propose MambaWSI, a novel method that leverages the state space model (SSM) for WSI classification by exploring multi-scale local and global features. Unlike existing approaches that sequentially traverse WSI tiles and rely on vanilla SSMs for long-range dependency modeling, we exploit a traversal strategy in a higher-dimensional discrete space that preserves spatial proximity, enabling a first-local-then-global feature extraction process. Furthermore, to align with the clinical workflow of pathologists when examining WSIs at multiple scales, we propose a two-stage hierarchical fusion strategy: inter-scale feature alignment and aggregation, followed by attention-based fusion across magnifications, integrating complementary information from multiple magnifications. Experiments on two datasets demonstrate that MambaWSI outperforms state-of-the-art methods in classification performance.1

pathology↗

bioRxiv2023

Mapping Cell-to-cell Interactions from Spatially Resolved Transcriptomics Data

Cell-cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently, through the introduction of spatially resolved transcriptomics technologies (SRTs), especially those that achieve single cell resolution. However, significant challenges remain to analyze such highly complex data properly. Here, we introduce a Bayesian multi-instance learning framework, spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight spacias power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand-receptor relationships and prior interaction databases, high false positive rates, and most importantly the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of spacia for all three commercialized single cell resolution ST technologies: MERSCOPE/Vizgen, CosMx/Nanostring, and Xenium/10X. Spacia unveiled how endothelial cells, fibroblasts and B cells in the tumor microenvironment contribute to Epithelial-Mesenchymal Transition and lineage plasticity in prostate cancer cells. We deployed spacia in a set of pan-cancer datasets and showed that B cells also participate in PDL1/PD1 signaling in tumors. We demonstrated that a CD8+ T cell/PDL1 effectiveness signature derived from spacia analyses is associated with patient survival and response to immune checkpoint inhibitor treatments in 3,354 patients. We revealed differential spatial interaction patterns between {gamma}{delta} T cells and liver hepatocytes in healthy and cancerous contexts. Overall, spacia represents a notable step in advancing quantitative theories of cellular communications.

bioinformatics↗

bioRxiv2023

A new label-free optical imaging method for the lymphatic system enhanced by deep learning

Our understanding of the lymphatic vascular system lags far behind that of the blood vascular system, limited by available imaging technologies. We present a label-free optical imaging method that visualizes the lymphatic system with high contrast. We developed an orthogonal polarization imaging (OPI) in the shortwave infrared range (SWIR) and imaged both lymph nodes and lymphatic vessels of mice and rats in vivo through intact skin, as well as human mesenteric lymph nodes in colectomy specimens. By integrating SWIR-OPI with U-Net, a deep learning image segmentation algorithm, we automated the lymph node size measurement process. Changes in lymph nodes in response to cancer progression were monitored in two separate mouse cancer models, through which we obtained insights into pre-metastatic niches and correlation between lymph node masses and many important biomarkers. In a human pilot study, we demonstrated the effectiveness of SWIR-OPI to detect human lymph nodes in real time with clinical colectomy specimens. One Sentence SummaryWe develop a real-time high contrast optical technique for imaging the lymphatic system, and apply it to anatomical pathology gross examination in a clinical setting, as well as real-time monitoring of tumor microenvironment in animal studies.

bioengineering↗

bioRxiv2022

Daily administration of low-dose daunorubicin or doxorubicin inhibits hypoxia-inducible factor 1 and tumor vascularization

Using a hypoxia-inducible factor 1 (HIF-1)-dependent luciferase reporter in Hep3B human hepatocellular carcinoma cells, we screened over 3,000 drugs that have been used in clinical trials and identified multiple anthracyclines as inhibitors of HIF-1 activity. Anthracyclines interfered with the ability of HIF-1 to bind to DNA. Daily injection of tumor-bearing mice with anthracyclines at low dose inhibited expression of the luciferase reporter and HIF-1 target genes that encode vascular endothelial growth factor A (VEGFA; ligand of VEGFR2), stromal-derived factor 1 (SDF-1; ligand of CXCR4), and stem cell factor (SCF; ligand of CD117) in tumor tissue. Increased numbers of circulating CXCR4+/Sca1+, VEGFR2+/CD34+, and VEGFR2+/CD117+ cells were demonstrated in immunodeficient mice bearing prostate cancer xenografts but not in tumor-bearing mice treated with anthracyclines, which also significantly inhibited angiogenesis in tumor tissue. Our findings indicate that HIF-1 inhibition underlies the anti-angiogenic effect associated with daunorubicin or doxorubicin metronomic therapy and suggest that these drugs may be particularly effective in patients with high levels of HIF-1 in their diagnostic tumor biopsy.

cancer biology↗

bioRxiv2021

Over 400 million years of cooperation: Untangling the chondrocranium-dermatocranium connection

The cranial endo- and dermal skeletons, which comprise the vertebrate skull, evolved independently over 470 million years ago and form separately during embryogenesis. In mammals, much of the cartilaginous chondrocranium is transient, undergoing endochondral ossification or disappearing, so its role in skull morphogenesis is not well studied and it remains an enigmatic structure. We provide complete three-dimensional (3D) reconstructions of the laboratory mouse chondrocranium from embryonic day 13.5 through 17.5 using a novel methodology of uncertainty-guided segmentation of phosphotungstic enhanced 3D microcomputed tomography images with sparse annotation. We evaluate the embryonic mouse chondrocranium and dermatocranium in 3D and delineate the effects of a Fgfr2 variant on embryonic chondrocranial cartilages and on the association with forming dermal bones using the Fgfr2cC342Y/+ Crouzon syndrome mouse. We show that the dermatocranium develops outside of and in shapes that conform to the chondrocranium. Results reveal direct effects of the Fgfr2 variant on embryonic cartilage, on chondrocranium morphology, and on the association between chondrocranium and dermatocranium development. Histologically we observe a trend of relatively more chondrocytes, larger chondrocytes, and/or more matrix in the Fgfr2cC342Y/+ embryos at all timepoints before the chondrocranium begins to disintegrate at E16.5. The chondrocrania and forming dermatocrania of Fgfr2cC342Y/+ embryos are relatively large, but a contrasting trend begins at E16.5 and continues into early postnatal (P0 and P2) timepoints, with the skulls of older Fgfr2cC342Y/+ mice reduced in most dimensions compared to Fgfr2c+/+ littermates. Our findings have implications for the study and treatment of human craniofacial disease, for understanding the impact of chondrocranial morphology on skull growth, and potentially on the evolution of skull morphology.

developmental biology↗

bioRxiv2018

Computer vision and deep learning automates nocturnal rainforest ant tracking to provide insight into behavior and disease risk

Determining how ant colonies optimize foraging while mitigating disease risk provides insight into how the ants have achieved ecological success. Fungal infected cadavers surround the main foraging trails of the carpenter ant Camponotus rufipes, offering a system to study how foragers behave given the persistent occurrence of disease threats. Studies on social insect foraging behavior typically require many hours of human labor due to the high density of individuals. To overcome this, we developed deep learning based computer vision algorithms to track foraging ants, frame-by-frame, from video footage. We found foragers can be divided into behavioral categories based on how straight they walk across the trail. Eighty percent of ants walk directly across the trail, while 20% wander or circle when crossing the trail. Departure from the main trail encourages exploration of new areas and could enhance discovery of new food resources. Conversely, results from our agent-based model simulations suggest deviation from a straight path exposes foragers to more infectious fungal spores. Consistency in walking behavior may protect most ants from infection, while the foragers with increased exposure due to their mode of walking could be a sufficient number of new hosts to sustain disease in this environment.

animal behavior and cognition↗