Publications

# = Correspondence

* = Equal Contribution

2025

Modeling patient tissues at molecular resolution with Eva

Yufan Liu, Rishabh Sharma, Matthew Bieniosek, Amy Kang, Eric Wu, Peter Chou, Irene Li, Maha Rahim, Erica Bauer, Ran Ji, Wei Duan, Li Qian, Ruibang Luo, Padmanee Sharma, Renu Dhanasekaran, Christian M. Schürch, Gregory Charville, Aaron T Mayer, James Zou, Alexandro E Trevino [#], and Zhenqin Wu [#].

In Review (Nature) , Dec 2025

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ai spatial omics histopathology data integration

Tissue structure is essential to function and homeostasis in all organs, and disruptions to structure usually indicate disease. Modeling relationships between structural, molecular, and clinical aspects of tissues could advance new diagnostics and treatment strategies. Although profiling techniques like spatial proteomics can capture these relationships, the data remain challenging to extract insight from. Here, we present Eva, a foundation model for tissue imaging data that learns multi-scale spatial representations of tissues at the molecular, cellular, and sample level. Eva uses a novel vision transformer architecture and is pre-trained on masked reconstruction of matched spatial proteomics and histopathology images. We show that Eva excels at a variety of tasks, including cross-modal inference, quality control, data annotation, zero-shot retrieval, survival modeling, and patient stratification. Extensive evaluations on held-out validation data demonstrate the versatility and generalizability of the learned embeddings. We anticipate that Eva will accelerate translational science by bridging basic research and clinical practice.
Using spatial proteomics to enhance cell type assignments in histology images

Monica T Dayao, Aaron T Mayer, Alexandro E Trevino [#], and Ziv Bar-Joseph [#].

Cell Reports Methods , Oct 2025

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ai data integration histopathology

Hematoxylin and eosin (H&E) staining has been a standard in clinical histopathology for many decades but lacks molecular detail. Advances in multiplexed spatial proteomics imaging allow cell types and tissues to be annotated by their expression patterns as well as their morphological features. However, these technologies are at present unavailable in most clinical settings. In this work, we present a machine learning framework that leverages histopathology foundation models and paired H&E and spatial proteomic imaging data to enable enhanced cell type annotation on H&E-only datasets. We trained and evaluated our method on kidney datasets with paired H&E and spatial proteomic imaging data and found that models trained using our methods outperform models trained directly on the imaging data. We also show how our framework can be used to study biological differences between two major kidney diseases.
ROSIE: AI generated multiplexed immunofluorescence staining from histopathology images

Eric Wu, Matthew Bieniosek, Zhenqin Wu, Jeff Chang, Gregory W Charville, Alexandro E Trevino [#], Aaron T Mayer [#], and James Y Zou [#].

Nature Communications , Jul 2025

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ai imputation histopathology data integration

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.
OmicsNavigator: an LLM-driven multi-agent system for autonomous zero-shot biological analysis in spatial omics

Li Yiyao, Nirvi Vakharia, Weixin Liang, Aaron T Mayer, Ruibang Luo, Alexandro E Trevino [#], and Zhenqin Wu [#].

In Preparation , Jul 2025

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ai spatial omics data integration

Spatial omics provides unprecedented high-resolution insights into molecular tissue compositions but poses significant analytical challenges due to massive data volumes, complex hierarchical spatial structures, and domain-specific interpretive demands. To address these limitations, we introduce OmicsNavigator, an LLM-driven multi-agent system that autonomously distills expert-level biological insights from raw spatial omics data without domain-specific fine-tuning. OmicsNavigator encodes spatial data into concise natural language summaries, enabling zero-shot annotation of structural components, quantitative analysis of pathological relevance, and semantic search of regions of interest using free-form text queries. We evaluated OmicsNavigator on multiple spatial omics studies of kidney cohorts with different phenotypes and biomarker panels, where OmicsNavigator achieved outstanding performances in structural annotations, pathology assessments, and semantic search across studies. OmicsNavigator offers a scalable, interpretable, and modality-agnostic solution for spatial omics analysis.
Spatial multi-omics and deep learning reveal fingerprints of immunotherapy response and resistance in hepatocellular carcinoma

Zhenqin Wu*, Joseph Boen*, Sonali Jindal*, Sreyashi Basu*, Matthew Bieniosek, Siyu He, Michael LaPelusa, Aaron T Mayer, Ahmed O Kaseb, James Y Zou [#], Padmanee Sharma [#], and Alexandro E Trevino [#].

In Preparation , May 2025

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cancer oncology spatial omics ai data integration

Despite advances in immunotherapy treatment for hepatocellular carcinoma (HCC), nonresponse rates remain high. The cellular and molecular mechanisms of tumor resistance to checkpoint inhibition remain unclear. To characterize these mechanisms, we performed spatial transcriptomic and proteomic profiling on human HCC tissue samples collected before and after checkpoint inhibition. Using an interpretable machine learning pipeline, we discover tissue microenvironments in pretreatment biopsies that are strongly predictive of major pathological response, laying groundwork for new patient stratification strategies in HCC immunotherapy. We further uncover microenvironment-specific gene expression programs that underlie residual disease in responders and nonresponders. Molecular pathways prioritized by our integrative analysis were spatially variable, pointing to a need for niche-targeted interventions.
Learning single-cell spatial context through integrated spatial multiomics with CORAL

Siyu He, Matthew F Bieniosek, Dongyuan Song, Jingtian Zhou, Benjamin Chidester, Zhenqin Wu, Joseph Boen, Padmanee Sharma, Alexandro E Trevino [#], and James Y Zou [#].

In Revision (Nature Genetics) , Feb 2025

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ai imputation histopathology data integration

2024

Spatial analysis reveals targetable macrophage-mediated mechanisms of immune evasion in hepatocellular carcinoma minimal residual disease

Lea Lemaitre, Nia Adeniji, Akanksha Suresh, Reshma Reguram, Josephine Zhang, Jangho Park, Amit Reddy, Alexandro E. Trevino, Aaron T. Mayer, Anja Deutzmann, Aida S. Hansen, Ling Tong, Vinodhini Arjunan, Neeraja Kambham, Brendan C. Visser, Monica M. Dua, C. Andrew Bonham, Nishita Kothary, H. Blaize D’Angio, Ryan Preska, Yanay Rosen, James Zou, Vivek Charu, Dean W. Felsher, and Renumathy Dhanasekaran.

Nature Cancer , Sep 2024

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cancer oncology spatial omics data integration

Hepatocellular carcinoma (HCC) frequently recurs from minimal residual disease (MRD), which persists after therapy. Here, we identified mechanisms of persistence of residual tumor cells using post-chemoembolization human HCC (n = 108 patients, 1.07 million cells) and a transgenic mouse model of MRD. Through single-cell high-plex cytometric imaging, we identified a spatial neighborhood within which PD-L1 + M2-like macrophages interact with stem-like tumor cells, correlating with CD8+ T cell exhaustion and poor survival. Further, through spatial transcriptomics of residual HCC, we showed that macrophage-derived TGFβ1 mediates the persistence of stem-like tumor cells. Last, we demonstrate that combined blockade of Pdl1 and Tgfβ excluded immunosuppressive macrophages, recruited activated CD8+ T cells and eliminated residual stem-like tumor cells in two mouse models: a transgenic model of MRD and a syngeneic orthotopic model of doxorubicin-resistant HCC. Thus, our spatial analyses reveal that PD-L1+ macrophages sustain MRD by activating the TGFβ pathway in stem-like cancer cells and targeting this interaction may prevent HCC recurrence from MRD.
Discovery and generalization of tissue structures from spatial omics data

Zhenqin Wu, Ayano Kondo, Monee McGrady, Ethan A G Baker, Benjamin Chidester, Eric Wu, Maha K Rahim, Nathan A Bracey, Vivek Charu, Raymond J Cho, Jeffrey B Cheng, Maryam Afkarian, James Zou [#], Aaron T Mayer [#], and Alexandro E Trevino [#].

Cell Reports Methods , Aug 2024

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ai spatial omics data integration

Tissues are organized into anatomical and functional units at different scales. New technologies for high-dimensional molecular profiling in situ have enabled the characterization of structure-function relationships in increasing molecular detail. However, it remains a challenge to consistently identify key functional units across experiments, tissues, and disease contexts, a task that demands extensive manual annotation. Here, we present spatial cellular graph partitioning (SCGP), a flexible method for the unsupervised annotation of tissue structures. We further present a reference-query extension pipeline, SCGP-Extension, that generalizes reference tissue structure labels to previously unseen samples, performing data integration and tissue structure discovery. Our experiments demonstrate reliable, robust partitioning of spatial data in a wide variety of contexts and best-in-class accuracy in identifying expertly annotated structures. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease, skin disorder, and neoplastic diseases, underscoring its potential to drive biological insight and discovery from spatial datasets.
Spatial proteomics of human diabetic kidney disease, from health to class III

Ayano Kondo, Monee McGrady, Dhiraj Nallapothula, Hira Ali, Alexandro E Trevino, Amy Lam, Ryan Preska, H Blaize D’Angio, Zhenqin Wu, Lauren N Lopez, Harshanna K Badhesha, Chenoa R Vargas, Achyuta Ramesh, Nasim Wiegley, Seung Seok Han, Marc Dall’Era, Kuang-Yu Jen, Aaron T Mayer, and Maryam Afkarian.

Diabetologia , Jul 2024

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kidney disease spatial omics

AIMS/HYPOTHESIS: Diabetic kidney disease (DKD) is the leading cause of chronic and end-stage kidney disease in the USA and worldwide. Animal models have taught us much about DKD mechanisms, but translation of this knowledge into treatments for human disease has been slowed by the lag in our molecular understanding of human DKD. METHODS: Using our Spatial TissuE Proteomics (STEP) pipeline (comprising curated human kidney tissues, multiplexed immunofluorescence and powerful analysis tools), we imaged and analysed the expression of 21 proteins in 23 tissue sections from individuals with diabetes and healthy kidneys (n=5), compared to those with DKDIIA, IIA-B and IIB (n=2 each) and DKDIII (n=1). RESULTS: These analyses revealed the existence of 11 cellular clusters (kidney compartments/cell types): podocytes, glomerular endothelial cells, proximal tubules, distal nephron, peritubular capillaries, blood vessels (endothelial cells and vascular smooth muscle cells), macrophages, myeloid cells, other CD45+ inflammatory cells, basement membrane and the interstitium. DKD progression was associated with co-localised increases in inflammatory cells and collagen IV deposition, with concomitant loss of native proteins of each nephron segment. Cell-type frequency and neighbourhood analyses highlighted a significant increase in inflammatory cells and their adjacency to tubular and \alphaSMA+ (α-smooth muscle actin-positive) cells in DKD. Finally, DKD progression showed marked regional variability within single tissue sections, as well as inter-individual variability within each DKD class. CONCLUSIONS/INTERPRETATION: Using the STEP pipeline, we found alterations in protein expression, cellular phenotypic composition and microenvironment structure with DKD progression, demonstrating the power of this pipeline to reveal the pathophysiology of human DKD.
PEPSI: Polarity measurements from spatial proteomics imaging suggest immune cell engagement

Eric Wu, Zhenqin Wu, Aaron T Mayer [#], Alexandro E Trevino [#], and James Y Zou [#].

Pacific Symposium on Biocomputing , 2024

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oncology subcellular localization spatial omics

Subcellular protein localization is important for understanding functional states of cells, but measuring and quantifying this information can be difficult and typically requires high-resolution microscopy. In this work, we develop a metric to define surface protein polarity from immunofluorescence (IF) imaging data and use it to identify distinct immune cell states within tumor microenvironments. We apply this metric to characterize over two million cells across 600 patient samples and find that cells identified as having polar expression exhibit characteristics relating to tumor-immune cell engagement. Additionally, we show that incorporating these polarity-defined cell subtypes improves the performance of deep learning models trained to predict patient survival outcomes. This method provides a first look at using subcellular protein expression patterns to phenotype immune cell functional states with applications to precision medicine.

2023

7-UP: Generating in silico CODEX from a small set of immunofluorescence markers

Eric Wu*, Alexandro E Trevino*, Zhenqin Wu, Kyle Swanson, Honesty J Kim, H Blaize D’Angio, Ryan Preska, Aaron E Chiou, Gregory W Charville, Piero Dalerba, Umamaheswar Duvvuri, Alexander D Colevas, Jelena Levi, Nikita Bedi, Serena Chang, John Sunwoo, Ann Marie Egloff, Ravindra Uppaluri, Aaron T Mayer, and James Zou.

PNAS Nexus , Jun 2023

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ai imputation spatial omics

Multiplex immunofluorescence (mIF) assays multiple protein biomarkers on a single tissue section. Recently, high-plex CODEX (co-detection by indexing) systems enable simultaneous imaging of 40+ protein biomarkers, unlocking more detailed molecular phenotyping, leading to richer insights into cellular interactions and disease. However, high-plex data can be slower and more costly to collect, limiting its applications, especially in clinical settings. We propose a machine learning framework, 7-UP, that can computationally generate in silico 40-plex CODEX at single-cell resolution from a standard 7-plex mIF panel by leveraging cellular morphology. We demonstrate the usefulness of the imputed biomarkers in accurately classifying cell types and predicting patient survival outcomes. Furthermore, 7-UP’s imputations generalize well across samples from different clinical sites and cancer types. 7-UP opens the possibility of in silico CODEX, making insights from high-plex mIF more widely available.
CasKAS: direct profiling of genome-wide dCas9 and Cas9 specificity using ssDNA mapping

Georgi K Marinov, Samuel H Kim, S Tansu Bagdatli, Soon Il Higashino, Alexandro E Trevino, Josh Tycko, Tong Wu, Lacramioara Bintu, Michael C Bassik, Chuan He, Anshul Kundaje, and William J Greenleaf.

Genome Biology , Apr 2023

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crispr gene editing specificity

Detecting and mitigating off-target activity is critical to the practical application of CRISPR-mediated genome and epigenome editing. While numerous methods have been developed to map Cas9 binding specificity genome-wide, they are generally time-consuming and/or expensive, and not applicable to catalytically dead CRISPR enzymes. We have developed CasKAS, a rapid, inexpensive, and facile assay for identifying off-target CRISPR enzyme binding and cleavage by chemically mapping the unwound single-stranded DNA structures formed upon binding of a sgRNA-loaded Cas9 protein. We demonstrate this method in both in vitro and in vivo contexts.
A Multi-Granularity Approach to Similarity Search in Multiplexed Immunofluorescence Images

Jennifer Yu, Zhenqin Wu, Aaron T. Mayer, Alexandro E Trevino, and James Zou.

Machine Learning in Computational Biology 2023 , 2023

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ai spatial omics histopathology

Due to the rapid increase and importance of multiplexed immunofluorescence (mIF) imaging data in spatial biology, there is a pressing need to develop efficient image-to-image search pipelines for both diagnostic and research purposes. While several image search methods have been introduced for conventional images and digital pathology, mIF images present three main challenges: (1) high dimension-ality, (2) domain-specificity, and (3) complex additional molecular information. To address this gap, we introduce the MIISS framework, a Multi-granularity mIF Image Similarity Search pipeline that employs self-supervised learning models to extract features from mIF image patches and an entropy-based aggregation method to enable similarity searches at higher, multi-granular levels. We then benchmarked various feature generation approaches to handle high dimensional images and tested them on various foundation models. We conducted evaluations using datasets from different tissues on both patch- and patient-level, which demonstrate the frame-work’s effectiveness and generalizability. Notably, we found that domain-specific models consistently outperformed other models, further showing their robustness and generalizability across different datasets. The MIISS framework offers an effective solution for navigating the growing landscape of mIF images, providing tangible clinical benefits and opening new avenues for pathology research.Competing Interest StatementThe authors have declared no competing interest.
emObject: domain specific data abstraction for spatial omics

Ethan A G Baker, Meng-Yao Huang, Amy Lam, Maha K Rahim, Matthew F Bieniosek, Bobby Wang, Nancy R Zhang, Aaron T Mayer, and Alexandro E Trevino.

bioRxiv , 2023

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spatial omics data integration

Recent advances in high-parameter spatial biology have yielded a rapidly growing new class of biological data, allowing researchers to more comprehensively characterize cellular state and morphology in native tissue context. However, spatial biology lacks a cohesive data abstraction on which to build novel computational tools and algorithms, making it difficult to fully leverage these emergent data. Here, we present emObject, a domain-specific data abstraction for spatial biology data and experiments. We demonstrate the simplicity, flexibility, and extensibility of emObject for a range of spatial omics data types, including the analysis of Visium, MIBI, and CODEX data, as well as for integrated spatial multiomic experiments. The development of emObject is an essential step towards building a unified data science ecosystem for spatial biology and accelerating the pace of scientific discovery.Competing Interest StatementE.A.G.B., M.H., M.K.R., M.F.B., A.E.T., A.L., B.W., and A.T.M. are employees of Enable Medicine, Inc. and may hold equity.

2022

Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens

Zhenqin Wu*, Alexandro E Trevino*, Eric Wu, Kyle Swanson, Honesty J Kim, H Blaize D’Angio, Ryan Preska, Gregory W Charville, Piero D Dalerba, Ann Marie Egloff, Ravindra Uppaluri, Umamaheswar Duvvuri, Aaron T Mayer, and James Zou.

Nature Biomedical Engineering , Dec 2022

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ai oncology cancer spatial omics

Multiplexed immunofluorescence imaging allows the multidimensional molecular profiling of cellular environments at subcellular resolution. However, identifying and characterizing disease-relevant microenvironments from these rich datasets is challenging. Here we show that a graph neural network that leverages spatial protein profiles in tissue specimens to model tumour microenvironments as local subgraphs captures distinctive cellular interactions associated with differential clinical outcomes. We applied this spatial cellular-graph strategy to specimens of human head-and-neck and colorectal cancers assayed with 40-plex immunofluorescence imaging to identify spatial motifs associated with cancer recurrence and with patient survival after treatment. The graph deep learning model was substantially more accurate in predicting patient outcomes than deep learning approaches that model spatial data on the basis of the local composition of cell types, and it generated insights into the effect of the spatial compartmentalization of tumour cells and granulocytes on patient prognosis. Local graphs may also aid in the analysis of disease-relevant motifs in histology samples characterized via spatial transcriptomics and other -omics techniques.
Single-cell transcriptomic census of endothelial changes induced by matrix stiffness and the association with atherosclerosis

Maedeh Zamani, Yu-Hao Cheng, Frank Charbonier, Vivek Kumar Gupta, Aaron T Mayer, Alexandro E Trevino, Thomas Quertermous, Ovijit Chaudhuri, Patrick Cahan, and Ngan F Huang.

Advanced Functional Materials , Nov 2022

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spatial omics extracellular matrix

Vascular endothelial cell (EC) plasticity plays a critical role in the progression of atherosclerosis by giving rise to mesenchymal phenotypes in the plaque lesion. Despite the evidence for arterial stiffening as a major contributor to atherosclerosis, the complex interplay among atherogenic stimuli in vivo has hindered attempts to determine the effects of extracellular matrix (ECM) stiffness on endothelial-mesenchymal transition (EndMT). To study the regulatory effects of ECM stiffness on EndMT, an in vitro model is developed in which human coronary artery ECs are cultured on physiological or pathological stiffness substrates. Leveraging single-cell RNA sequencing, cell clusters with mesenchymal transcriptional features are identified to be more prevalent on pathological substrates than physiological substrates. Trajectory inference analyses reveal a novel mesenchymal-to-endothelial reverse transition, which is blocked by pathological stiffness substrates, in addition to the expected EndMT trajectory. ECs pushed to a mesenchymal character by pathological stiffness substrates are enriched in transcriptional signatures of atherosclerotic ECs from human and murine plaques. This study characterizes at single-cell resolution the transcriptional programs that underpin EC plasticity in both physiological or pathological milieus, and thus serves as a valuable resource for more precisely defining EndMT and the transcriptional programs contributing to atherosclerosis.
Nociceptor neurons affect cancer immunosurveillance

Mohammad Balood, Maryam Ahmadi, Tuany Eichwald, Ali Ahmadi, Abdelilah Majdoubi, Karine Roversi, Katiane Roversi, Christopher T Lucido, Anthony C Restaino, Siyi Huang, Lexiang Ji, Kai-Chih Huang, Elise Semerena, Sini C Thomas, Alexandro E Trevino, Hannah Merrison, Alexandre Parrin, Benjamin Doyle, Daniel W Vermeer, William C Spanos, Caitlin S Williamson, Corey R Seehus, Simmie L Foster, Hongyue Dai, Chengyi J Shu, Manu Rangachari, Jacques Thibodeau, Sonia Del Rincon, Ronny Drapkin, Moutih Rafei, Nader Ghasemlou, Paola D Vermeer, Clifford J Woolf, and Sebastien Talbot.

Nature , Nov 2022

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cancer neuroimmunology

Solid tumours are innervated by nerve fibres that arise from the autonomic and sensory peripheral nervous systems1-5. Whether the neo-innervation of tumours by pain-initiating sensory neurons affects cancer immunosurveillance remains unclear. Here we show that melanoma cells interact with nociceptor neurons, leading to increases in their neurite outgrowth, responsiveness to noxious ligands and neuropeptide release. Calcitonin gene-related peptide (CGRP)-one such nociceptor-produced neuropeptide-directly increases the exhaustion of cytotoxic CD8+ T cells, which limits their capacity to eliminate melanoma. Genetic ablation of the TRPV1 lineage, local pharmacological silencing of nociceptors and antagonism of the CGRP receptor RAMP1 all reduced the exhaustion of tumour-infiltrating leukocytes and decreased the growth of tumours, nearly tripling the survival rate of mice that were inoculated with B16F10 melanoma cells. Conversely, CD8+ T cell exhaustion was rescued in sensory-neuron-depleted mice that were treated with local recombinant CGRP. As compared with wild-type CD8+ T cells, Ramp1-/- CD8+ T cells were protected against exhaustion when co-transplanted into tumour-bearing Rag1-deficient mice. Single-cell RNA sequencing of biopsies from patients with melanoma revealed that intratumoral RAMP1-expressing CD8+ T cells were more exhausted than their RAMP1-negative counterparts, whereas overexpression of RAMP1 correlated with a poorer clinical prognosis. Overall, our results suggest that reducing the release of CGRP from tumour-innervating nociceptors could be a strategy to improve anti-tumour immunity by eliminating the immunomodulatory effects of CGRP on cytotoxic CD8+ T cells.

2021

Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution

Alexandro E Trevino*, Fabian Müller*, Jimena Andersen*, Laksshman Sundaram*, Arwa Kathiria, Anna Shcherbina, Kyle Farh, Howard Y Chang, Anca M Pașca, Anshul Kundaje, Sergiu P Pașca, and William J Greenleaf.

Cell , Sep 2021

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brain development genomics gene regulation

Genetic perturbations of cortical development can lead to neurodevelopmental disease, including autism spectrum disorder (ASD). To identify genomic regions crucial to corticogenesis, we mapped the activity of gene-regulatory elements generating a single-cell atlas of gene expression and chromatin accessibility both independently and jointly. This revealed waves of gene regulation by key transcription factors (TFs) across a nearly continuous differentiation trajectory, distinguished the expression programs of glial lineages, and identified lineage-determining TFs that exhibited strong correlation between linked gene-regulatory elements and expression levels. These highly connected genes adopted an active chromatin state in early differentiating cells, consistent with lineage commitment. Base-pair-resolution neural network models identified strong cell-type-specific enrichment of noncoding mutations predicted to be disruptive in a cohort of ASD individuals and identified frequently disrupted TF binding sites. This approach illustrates how cell-type-specific mapping can provide insights into the programs governing human development and disease.
Transcription-dependent domain-scale three-dimensional genome organization in the dinoflagellate Breviolum minutum

Georgi K Marinov*, Alexandro E Trevino*, Tingting Xiang, Anshul Kundaje, Arthur R Grossman, and William J Greenleaf.

Nat. Genet. , May 2021

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gene regulation

Dinoflagellate chromosomes represent a unique evolutionary experiment, as they exist in a permanently condensed, liquid crystalline state; are not packaged by histones; and contain genes organized into tandem gene arrays, with minimal transcriptional regulation. We analyze the three-dimensional genome of Breviolum minutum, and find large topological domains (dinoflagellate topologically associating domains, which we term ’dinoTADs’) without chromatin loops, which are demarcated by convergent gene array boundaries. Transcriptional inhibition disrupts dinoTADs, implicating transcription-induced supercoiling as the primary topological force in dinoflagellates.

2020

Diverse lncRNA mechanisms in brain development and disease

Cheen Euong Ang, Alexandro E Trevino, and Howard Y Chang.

Curr. Opin. Genet. Dev. , Dec 2020

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lncrna brain development

Long noncoding RNAs (lncRNAs) are a diverse and pervasive class of genes. Recent studies in the mammalian brain have uncovered several novel mechanisms. LncRNA loci are often located in proximity to developmental transcriptional factors. The lncRNA product may act like a transcription factor to control distantly located genes, or in other instances, the lncRNA loci contain DNA regulatory elements that act locally on neighboring genes. Circular RNAs are covalently closed single-stranded RNAs that can control neuronal function by acting as microRNA sponges and additional mechanisms. LncRNAs can also engage in target-directed microRNA degradation to shape the pool of microRNAs and translation. Thus, diverse mechanisms allow lncRNAs to act in the nucleus and cytoplasm to control neuronal fate and function.
Chromatin accessibility dynamics in a model of human forebrain development

Alexandro E Trevino*, Nasa Sinnott-Armstrong*, Jimena Andersen*, Se-Jin Yoon, Nina Huber, Jonathan K Pritchard, Howard Y Chang, William J Greenleaf, and Sergiu P Pașca.

Science , Jan 2020

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brain development gene regulation genomics

Forebrain development is characterized by highly synchronized cellular processes, which, if perturbed, can cause disease. To chart the regulatory activity underlying these events, we generated a map of accessible chromatin in human three-dimensional forebrain organoids. To capture corticogenesis, we sampled glial and neuronal lineages from dorsal or ventral forebrain organoids over 20 months in vitro. Active chromatin regions identified in human primary brain tissue were observed in organoids at different developmental stages. We used this resource to map genetic risk for disease and to explore evolutionary conservation. Moreover, we integrated chromatin accessibility with transcriptomics to identify putative enhancer-gene linkages and transcription factors that regulate human corticogenesis. Overall, this platform brings insights into gene-regulatory dynamics at previously inaccessible stages of human forebrain development, including signatures of neuropsychiatric disorders.

2019

Mitigation of off-target toxicity in CRISPR-Cas9 screens for essential non-coding elements

Josh Tycko, Michael Wainberg, Georgi K Marinov, Oana Ursu, Gaelen T Hess, Braeden K Ego, Aradhana, Amy Li, Alisa Truong, Alexandro E Trevino, Kaitlyn Spees, David Yao, Irene M Kaplow, Peyton G Greenside, David W Morgens, Douglas H Phanstiel, Michael P Snyder, Lacramioara Bintu, William J Greenleaf, Anshul Kundaje, and Michael C Bassik.

Nat. Commun. , Sep 2019

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crispr screening specificity gene regulation

Pooled CRISPR-Cas9 screens are a powerful method for functionally characterizing regulatory elements in the non-coding genome, but off-target effects in these experiments have not been systematically evaluated. Here, we investigate Cas9, dCas9, and CRISPRi/a off-target activity in screens for essential regulatory elements. The sgRNAs with the largest effects in genome-scale screens for essential CTCF loop anchors in K562 cells were not single guide RNAs (sgRNAs) that disrupted gene expression near the on-target CTCF anchor. Rather, these sgRNAs had high off-target activity that, while only weakly correlated with absolute off-target site number, could be predicted by the recently developed GuideScan specificity score. Screens conducted in parallel with CRISPRi/a, which do not induce double-stranded DNA breaks, revealed that a distinct set of off-targets also cause strong confounding fitness effects with these epigenome-editing tools. Promisingly, filtering of CRISPRi libraries using GuideScan specificity scores removed these confounded sgRNAs and enabled identification of essential regulatory elements.

2017

An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues

M Ryan Corces, Alexandro E Trevino, Emily G Hamilton, Peyton G Greenside, Nicholas A Sinnott-Armstrong, Sam Vesuna, Ansuman T Satpathy, Adam J Rubin, Kathleen S Montine, Beijing Wu, Arwa Kathiria, Seung Woo Cho, Maxwell R Mumbach, Ava C Carter, Maya Kasowski, Lisa A Orloff, Viviana I Risca, Anshul Kundaje, Paul A Khavari, Thomas J Montine, William J Greenleaf, and Howard Y Chang.

Nat. Methods , Oct 2017

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protocol genomics

We present Omni-ATAC, an improved ATAC-seq protocol for chromatin accessibility profiling that works across multiple applications with substantial improvement of signal-to-background ratio and information content. The Omni-ATAC protocol generates chromatin accessibility profiles from archival frozen tissue samples and 50-\mum sections, revealing the activities of disease-associated DNA elements in distinct human brain structures. The Omni-ATAC protocol enables the interrogation of personal regulomes in tissue context and translational studies.

2015

Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex

Silvana Konermann*, Mark D Brigham*, Alexandro E Trevino, Julia Joung, Omar O Abudayyeh, Clea Barcena, Patrick D Hsu, Naomi Habib, Jonathan S Gootenberg, Hiroshi Nishimasu, Osamu Nureki, and Feng Zhang.

Nature , Jan 2015

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crispr screening gene regulation synthetic biology

Systematic interrogation of gene function requires the ability to perturb gene expression in a robust and generalizable manner. Here we describe structure-guided engineering of a CRISPR-Cas9 complex to mediate efficient transcriptional activation at endogenous genomic loci. We used these engineered Cas9 activation complexes to investigate single-guide RNA (sgRNA) targeting rules for effective transcriptional activation, to demonstrate multiplexed activation of ten genes simultaneously, and to upregulate long intergenic non-coding RNA (lincRNA) transcripts. We also synthesized a library consisting of 70,290 guides targeting all human RefSeq coding isoforms to screen for genes that, upon activation, confer resistance to a BRAF inhibitor. The top hits included genes previously shown to be able to confer resistance, and novel candidates were validated using individual sgRNA and complementary DNA overexpression. A gene expression signature based on the top screening hits correlated with markers of BRAF inhibitor resistance in cell lines and patient-derived samples. These results collectively demonstrate the potential of Cas9-based activators as a powerful genetic perturbation technology.

2014

Genome-wide binding of the CRISPR endonuclease Cas9 in mammalian cells

Xuebing Wu, David A Scott, Andrea J Kriz, Anthony C Chiu, Patrick D Hsu, Daniel B Dadon, Albert W Cheng, Alexandro E Trevino, Silvana Konermann, Sidi Chen, Rudolf Jaenisch, Feng Zhang, and Phillip A Sharp.

Nat. Biotechnology , Jul 2014

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crispr

Bacterial type II CRISPR-Cas9 systems have been widely adapted for RNA-guided genome editing and transcription regulation in eukaryotic cells, yet their in vivo target specificity is poorly understood. Here we mapped genome-wide binding sites of a catalytically inactive Cas9 (dCas9) from Streptococcus pyogenes loaded with single guide RNAs (sgRNAs) in mouse embryonic stem cells (mESCs). Each of the four sgRNAs we tested targets dCas9 to between tens and thousands of genomic sites, frequently characterized by a 5-nucleotide seed region in the sgRNA and an NGG protospacer adjacent motif (PAM). Chromatin inaccessibility decreases dCas9 binding to other sites with matching seed sequences; thus 70% of off-target sites are associated with genes. Targeted sequencing of 295 dCas9 binding sites in mESCs transfected with catalytically active Cas9 identified only one site mutated above background levels. We propose a two-state model for Cas9 binding and cleavage, in which a seed match triggers binding but extensive pairing with target DNA is required for cleavage.
Genome editing using Cas9 nickases

Alexandro E Trevino, and Feng Zhang.

Methods Enzymol. , 2014

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crispr gene editing protocol

The RNA-guided, sequence-specific endonuclease Cas9 has been widely adopted as genome engineering tool due to its efficiency and ease of use. Derived from the microbial CRISPR (clustered regularly interspaced short palindromic repeat) type II adaptive immune system, Cas9 has now been successfully engineered for genome editing applications in a variety of animal and plant species. To reduce potential off-target mutagenesis by wild-type Cas9, homology- and structure-guided mutagenesis of Streptococcus pyogenes Cas9 catalytic domains has produced “nicking” enzymes (Cas9n) capable of inducing single-strand nicks rather than double-strand breaks. Since nicks are generally repaired with high fidelity in eukaryotic cells, Cas9n can be leveraged to mediate highly specific genome editing, either via nonhomologous end-joining or homology-directed repair. Here we describe the preparation, testing, and application of Cas9n reagents for precision mammalian genome engineering.

2013

Double nicking by RNA-guided CRISPR Cas9 for enhanced genome editing specificity

F Ann Ran, Patrick D Hsu, Chie-Yu Lin, Jonathan S Gootenberg, Silvana Konermann, Alexandro E Trevino, David A Scott, Azusa Inoue, Shogo Matoba, Yi Zhang, and Feng Zhang.

Cell , Sep 2013

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crispr specificity synthetic biology

Targeted genome editing technologies have enabled a broad range of research and medical applications. The Cas9 nuclease from the microbial CRISPR-Cas system is targeted to specific genomic loci by a 20 nt guide sequence, which can tolerate certain mismatches to the DNA target and thereby promote undesired off-target mutagenesis. Here, we describe an approach that combines a Cas9 nickase mutant with paired guide RNAs to introduce targeted double-strand breaks. Because individual nicks in the genome are repaired with high fidelity, simultaneous nicking via appropriately offset guide RNAs is required for double-stranded breaks and extends the number of specifically recognized bases for target cleavage. We demonstrate that using paired nicking can reduce off-target activity by 50- to 1,500-fold in cell lines and to facilitate gene knockout in mouse zygotes without sacrificing on-target cleavage efficiency. This versatile strategy enables a wide variety of genome editing applications that require high specificity.
Optical control of mammalian endogenous transcription and epigenetic states

Silvana Konermann*, Mark D Brigham*, Alexandro E Trevino, Patrick D Hsu, Matthias Heidenreich, Le Cong, Randall J Platt, David A Scott, George M Church, and Feng Zhang.

Nature , Aug 2013

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synthetic biology optogenetics gene regulation

The dynamic nature of gene expression enables cellular programming, homeostasis and environmental adaptation in living systems. Dissection of causal gene functions in cellular and organismal processes therefore necessitates approaches that enable spatially and temporally precise modulation of gene expression. Recently, a variety of microbial and plant-derived light-sensitive proteins have been engineered as optogenetic actuators, enabling high-precision spatiotemporal control of many cellular functions. However, versatile and robust technologies that enable optical modulation of transcription in the mammalian endogenous genome remain elusive. Here we describe the development of light-inducible transcriptional effectors (LITEs), an optogenetic two-hybrid system integrating the customizable TALE DNA-binding domain with the light-sensitive cryptochrome 2 protein and its interacting partner CIB1 from Arabidopsis thaliana. LITEs do not require additional exogenous chemical cofactors, are easily customized to target many endogenous genomic loci, and can be activated within minutes with reversibility. LITEs can be packaged into viral vectors and genetically targeted to probe specific cell populations. We have applied this system in primary mouse neurons, as well as in the brain of freely behaving mice in vivo to mediate reversible modulation of mammalian endogenous gene expression as well as targeted epigenetic chromatin modifications. The LITE system establishes a novel mode of optogenetic control of endogenous cellular processes and enables direct testing of the causal roles of genetic and epigenetic regulation in normal biological processes and disease states.