Mingyao Li

Understanding the cellular processes that underlie early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies. Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8+ alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival …

Single-cell data from RNA-seq, chromatin accessibility, DNA methylation and other modalities can be readily integrated using two new methods.

Vascular smooth muscle cells (VSMCs) play a central role in the development of atherosclerosis due in part to their capability to phenotypically transition into either a protective or harmful state. However, the ability to identify and trace VSMCs and their progeny in vivo is limited due to the lack of well-defined VSMC cell surface markers. Therefore, investigations into VSMC fate must utilize lineage-tracing mouse models, which are time-consuming and challenging to generate and not feasible in humans. Here, we employed CITE-seq to characterize the phenotypic expression of 119 cell surface proteins in mouse atherosclerosis. We found that CD200 is a highly expressed and specific marker of VSMCs, which persists even with phenotypic modulation. We validated our findings using a combination of flow cytometry, qPCR, and immunohistochemistry, all confirming that CD200 can identify and mark VSMCs and their derived cells in early to advanced mouse atherosclerotic lesions. Additionally, we describe a similar expression pattern of CD200 in human coronary and carotid atherosclerosis. Thus, our data support the use of CD200 as a lineage marker for VSMCs and VSMC-derived cells in mouse and human atherosclerosis.

Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.

Summary The transition from 2D to 3D spatial profiling marks a revolutionary era in cancer research, offering unprecedented potential to enhance cancer diagnosis and treatment. This commentary outlines the experimental and computational advancements and challenges in 3D spatial molecular profiling, underscoring the innovation needed in imaging tools, software, artificial intelligence, and machine learning to overcome implementation hurdles and harness the full potential of 3D analysis in the field.

Monocytes are a critical innate immune system cell type that serves homeostatic and immunoregulatory functions. The Cell surface expression of CD14 and CD16 has historically identified them, however, recent single-cell studies have uncovered that they are much more heterogeneous than previously realized. We utilized cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-cell RNA sequencing (scRNA-seq) to describe the comprehensive transcriptional and phenotypic landscape of 437,126 monocytes. This high-dimensional multimodal approach identified vast phenotypic diversity and functionally distinct subsets, including IFN-responsive, MHCIIhi, monocyte-platelet aggregates, and non-classical, as well as several subpopulations of classical monocytes. Using flow cytometry, we validated the existence of MHCII+CD275+ MHCIIhi, CD42b+ monocyte-platelet aggregates, CD16+CD99- non-classical monocytes, and CD99+ classical monocytes. Each subpopulation exhibited unique functions, developmental trajectories, transcriptional regulation, and tissue distribution. Moreover, we revealed alterations associated with cardiovascular disease (CVD) risk factors, including race, smoking, and hyperlipidemia, and the effect of hyperlipidemia was recapitulated in mouse models of elevated cholesterol. This integrative and cross-species comparative analysis provides a unique resource to compare alterations in monocytes in pathological conditions and offers insights into monocyte-driven mechanisms in CVD and the potential for targeted therapies.

BACKGROUND Atherosclerotic plaques are complex tissues composed of a heterogeneous mixture of cells. However, our understanding of the comprehensive transcriptional and phenotypic landscape of the cells within these lesions is limited. METHODS To characterize the landscape of human carotid atherosclerosis in greater detail, we combined cellular indexing of transcriptomes and epitopes by sequencing and single-cell RNA sequencing to classify all cell types within lesions (n=21; 13 symptomatic) to achieve a comprehensive multimodal understanding of the cellular identities of atherosclerosis and their association with clinical pathophysiology. RESULTS We identified 25 cell populations, each with a unique multiomic signature, including macrophages, T cells, NK cells, mast cells, B cells, plasma cells, neutrophils, dendritic cells, endothelial cells, fibroblasts, and smooth muscle cells (SMCs). Among the …

Tertiary lymphoid structures (TLSs) are clusters of immune cells formed in non-lymphoid tissues. They are often found at sites of chronic inflammation, notably within the invasive margins and the core of various solid tumors. TLSs are pivotal in mediating anti-tumor immunity. However, our understanding of TLSs in large/complex tissue contexts remains incomplete due to the lack of computational tools to effectively detect and phenotype TLSs. Recent advances in spatially resolved transcriptomics (SRT) present a broader spectrum of analytical possibilities for investigating the spatial phenotypic heterogeneity of TLSs and their interaction with stromal and cancer cells. Here, we present iStarTLS (Inferring Super-resolution Tissue ARchitecture for TLSs), a computational toolkit designed to process SRT data for TLS detection and phenotyping and showcase its performance on breast, bladder, and lung cancer samples …

Understanding the earliest changes during lung adenocarcinoma (LUAD) development can set the stage for discovery of fertile targets for disease interception, thereby mitigating the dire public health burden of LUAD. We previously identified bulk-level molecular and immunological changes that are enriched in normal-appearing tissue (NAT) in the local niche of human LUAD, as well as those that commence in adenomatous premalignant lesions (aPMLs, LUAD precursors) and that are further enriched in LUADs. Yet, in-depth understanding of the identities, states, and properties of specific cell subsets in NAT and precancer that trigger LUAD remain largely elusive due to inherent roadblocks to sampling and characterizing aPMLs that are at the center of this trajectory. Here, we aimed to map molecular profiles, states, and interactions of cell subsets that underlie initiation in lesion-adjacent NAT, and to determine …

Cancer cell state heterogeneity constitutes a foundational characteristic of cancer, posing a formidable barrier to the discovery of biomarkers and the efficacy of therapeutic interventions. However, current understanding of cancer cell heterogeneity and plasticity remains limited, especially in the spatial context. Previous studies underscored that cancer cell states are not strictly governed by genetics, but rather display a remarkable degree of plasticity. Recent pan-cancer single-cell studies have unveiled dozens of recurrent cancer cell states. Nevertheless, the relationship between these cancer cell states and tumor microenvironment (TME) remains poorly understood, especially when considering the diversity across different tumor types. Recent advancements in spatial transcriptomics (ST) have paved the way for a novel approach to spatially profile cell location, organization, and interaction within the tumor …

Neuroimaging data from multiple batches (ie acquisition sites, scanner manufacturer, datasets, etc.) are increasingly necessary to gain new insights into the human brain. However, multi-batch data, as well as extracted radiomic features, exhibit pronounced technical artifacts across batches. These batch effects introduce confounding into the data and can obscure biological effects of interest, decreasing the generalizability and reproducibility of findings. This is especially true when multi-batch data is used alongside complex downstream analysis models, such as machine learning methods. Image harmonization methods seeking to remove these batch effects are important for mitigating these issues; however, significant multivariate batch effects remain in the data following harmonization by current state-of-the-art statistical and deep learning methods. We present DeepCombat, a deep learning harmonization …

The authors would like to make corrections in Figure S1, Figure S2, Table S2 and Table S4 corresponding to quality control (QC) plots and individual sample numbers where the following sample labels were transposed: 3 and 4; 9 and 13; 11 and 14. These corrections do not alter any of the other reported results or any of the study conclusions. The authors would like to thank David Dai for informing us of these errors and for assisting in generating an updated supplementary file1.

Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information …

BackgroundIndividual colorectal polyp risk factors are well characterized; however, insights into their pathway-specific interactions are scarce. We aimed to identify the impact of individual risk factors and their joint effects on adenomatous (AP) and serrated polyp (SP) risk.MethodsWe collected information on 363 lifestyle and metabolic parameters from 1597 colonoscopy participants, resulting in over 521,000 data points. We used multivariate statistics and machine-learning approaches to assess associations of single variables and their interactions with AP and SP risk.ResultsIndividual factors and their interactions showed common and polyp subtype-specific effects. Abdominal obesity, high body mass index (BMI), metabolic syndrome, and red meat consumption globally increased polyp risk. Age, gender, and western diet associated with AP risk, while smoking was associated with SP risk. CRC family history was …

Spatially resolved transcriptomics (SRT) has advanced our understanding of the spatial patterns of gene expression, but the lack of single-cell resolution in spatial barcoding-based SRT hinders the inference of specific locations of individual cells. To determine the spatial distribution of cell types in SRT, we present SpaDecon, a semi-supervised learning approach that incorporates gene expression, spatial location, and histology information for cell-type deconvolution. SpaDecon was evaluated through analyses of four real SRT datasets using knowledge of the expected distributions of cell types. Quantitative evaluations were performed for four pseudo-SRT datasets constructed according to benchmark proportions. Using mean squared error and Jensen-Shannon divergence with the benchmark proportions as evaluation criteria, we show that SpaDecon performance surpasses that of published cell-type …

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in health and disease. However, the lack of physical relationships among dissociated cells has limited its applications. To address this issue, we present CeLEry (Cell Location recovEry), a supervised deep learning algorithm that leverages gene expression and spatial location relationships learned from spatial transcriptomics to recover the spatial origins of cells in scRNA-seq. CeLEry has an optional data augmentation procedure via a variational autoencoder, which improves the method’s robustness and allows it to overcome noise in scRNA-seq data. We show that CeLEry can infer the spatial origins of cells in scRNA-seq at multiple levels, including 2D location and spatial domain of a cell, while also providing uncertainty estimates for the recovered locations. Our comprehensive benchmarking evaluations on …

The development of immunotherapy drugs, such as immune checkpoint inhibitors (ICIs) has changed the environment of cancer treatment tremendously by providing efficacious therapeutic options for many cancer patients. However, only a minority of patients experience durable clinical benefit and increasing evidence has linked the efficacy of ICIs to tumor cell heterogeneity, the complex tumor immune microenvironment and their interactions, which remains poorly understood, particularly in the tissue context. Recent advances in spatially resolved transcriptomics (SRT) has provided great opportunity to better understand spatial tumor-immune interactions. In this study, we obtained public and in-house SRT data on a total of 136 tissue sections across 11 different cancer types, representing to date, the largest collection of SRT on human cancer. Sample and spot-level quality filters were applied, batch effects were …

Tumor-infiltrating T cells offer a promising avenue for cancer treatment, yet their states remain to be fully characterized. Here we present a single-cell atlas of T cells from 308,048 transcriptomes across 16 cancer types, uncovering previously undescribed T cell states and heterogeneous subpopulations of follicular helper, regulatory and proliferative T cells. We identified a unique stress response state, TSTR, characterized by heat shock gene expression. TSTR cells are detectable in situ in the tumor microenvironment across various cancer types, mostly within lymphocyte aggregates or potential tertiary lymphoid structures in tumor beds or surrounding tumor edges. T cell states/compositions correlated with genomic, pathological and clinical features in 375 patients from 23 cohorts, including 171 patients who received immune checkpoint blockade therapy. We also found significantly upregulated heat shock gene …

Background Long noncoding RNAs (lncRNAs) have emerged as novel regulators of macrophage biology and inflammatory cardiovascular diseases. However, studies focused on lncRNAs in human macrophage subtypes, particularly human lncRNAs that are not conserved in rodents, are limited. Methods Through RNA-sequencing of human monocyte–derived macrophages, we identified suppressor of inflammatory macrophage apoptosis lncRNA (SIMALR). Lipopolysaccharide/IFNγ (interferon γ) stimulated human macrophages were treated with SIMALR antisense oligonucleotides and subjected to RNA-sequencing to investigate the function of SIMALR. Western blots, luciferase assay, and RNA immunoprecipitation were performed to validate function and potential mechanism of SIMALR. RNAscope was performed to identify SIMALR expression in human carotid atherosclerotic plaques. Results RNA …

Age-related macular degeneration (AMD) is a leading cause of blindness, and elucidating its underlying disease mechanisms is vital to the development of appropriate therapeutics. We identified differentially expressed genes (DEGs) and differentially spliced genes (DSGs) across the clinical stages of AMD in disease-affected tissue, the macular retina pigment epithelium (RPE)/choroid and the macular neural retina within the same eye. We utilized 27 deeply phenotyped donor eyes (recovered within a 6 h postmortem interval time) from Caucasian donors (60–94 years) using a standardized published protocol. Significant findings were then validated in an independent set of well-characterized donor eyes (n = 85). There was limited overlap between DEGs and DSGs, suggesting distinct mechanisms at play in AMD pathophysiology. A greater number of previously reported AMD loci overlapped with DSGs compared to DEGs between disease states, and no DEG overlap with previously reported loci was found in the macular retina between disease states. Additionally, we explored allele-specific expression (ASE) in coding regions of previously reported AMD risk loci, uncovering a significant imbalance in C3 rs2230199 and CFH rs1061170 in the macular RPE/choroid for normal eyes and intermediate AMD (iAMD), and for CFH rs1061147 in the macular RPE/choroid for normal eyes and iAMD, and separately neovascular AMD (NEO). Only significant DEGs/DSGs from the macular RPE/choroid were found to overlap between disease states. STAT1, validated between the iAMD vs. normal comparison, and AGTPBP1, BBS5, CERKL, FGFBP2, KIFC3 …