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Jing Huang, Chenyang Yuan, Jiahui Jiang, Jianfeng Chen, Yesim Gokmen-Polar, Rossana L. Segura, Xinmiao Yan, Jianjun Gao, Bing Yao, Michael Epstein, Sunil S. Badve, Alexander Lazar, Linghua Wang* & Jian Hu*.
Multi-modal spatial omics data are invaluable for exploring complex cellular behaviors in diseases from both morphological and molecular perspectives. Current analytical methods primarily focus on clustering and classification, and do not adequately examine the relationship between cell morphology and molecular dynamics. Here, we present MorphLink, a framework designed to systematically identify disease-related morphological-molecular interplays. MorphLink has been evaluated across a wide array of datasets, showcasing its effectiveness in extracting and linking interpretable morphological features with various molecular measurements in spatial omics analyses. These linkages provide a transparent view of cellular behavior heterogeneity within tissue regions with similar cell type compositions, characterizing tumor subtypes and immune diversity across different organs. Additionally, MorphLink is scalable and robust against cross-sample batch effects, making it an efficient method for integrative spatial omics data analysis across samples, cohorts, and modalities, and enhancing the interpretation of results for large-scale studies. Go to the article
Kyle Coleman*, Amelia Schroeder, Melanie Loth, Daiwei Zhang, Jeong Hwan Park, Ji-Youn Sung, Niklas Blank, Alexis J. Cowan, Xuyu Qian, Jianfeng Chen, Jiahui Jiang, Hanying Yan, Laith Z. Samarah, Jean R. Clemenceau, Inyeop Jang, Minji Kim, Isabel Barnfather, Joshua D. Rabinowitz, Yanxiang Deng, Edward B. Lee, Alexander Lazar, Jianjun Gao, Emma E. Furth, Tae Hyun Hwang, Linghua Wang, Christoph A. Thaiss, Jian Hu* & Mingyao Li*. Spatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multimodal spatial omics data is crucial for understanding tissue complexity and underlying biology. Furthermore, improvements in spatial resolution have led to the advent of technologies that can generate spatial molecular data with subcellular resolution, requiring the development of computationally efficient methods that can handle the resulting large-scale datasets. MISO (MultI-modal Spatial Omics) is a versatile algorithm for feature extraction and clustering, capable of integrating multiple modalities from diverse spatial omics experiments with high spatial resolution. Its effectiveness is demonstrated across various datasets, encompassing gene expression, protein expression, epigenetics, metabolomics and tissue histology modalities. MISO outperforms existing methods in identifying biologically relevant spatial domains, representing a substantial advancement in multimodal spatial omics analysis. Moreover, MISO’s computational efficiency ensures its scalability to handle large-scale datasets generated by subcellular resolution spatial omics technologies. Go to the article
Jiahui Jiang, Yunhe Liu, Jiangjiang Qin, Jianfeng Chen, Jingjing Wu, Melissa P. Pizzi, Rossana Lazcano, Kohei Yamashita, Zhiyuan Xu, Guangsheng Pei, Kyung Serk Cho, Yanshuo Chu, Ansam Sinjab, Fuduan Peng, Xinmiao Yan, Guangchun Han, Ruiping Wang, Enyu Dai, Yibo Dai, Bogdan A. Czerniak, Andrew Futreal, Anirban Maitra, Alexander Lazar, Humam Kadara, Amir A. Jazaeri, Xiangdong Cheng, Jaffer Ajani, Jianjun Gao, Jian Hu* & Linghua Wang*. Recent advances in spatial transcriptomics (ST) techniques provide valuable insights into cellular interactions within the tumor microenvironment (TME). However, most analytical tools lack consideration of histological features and rely on matched single-cell RNA sequencing data, limiting their effectiveness in TME studies. To address this, we introduce the Morphology-Enhanced Spatial Transcriptome Analysis Integrator (METI), an end-to-end framework that maps cancer cells and TME components, stratifies cell types and states, and analyzes cell co-localization. By integrating spatial transcriptomics, cell morphology, and curated gene signatures, METI enhances our understanding of the mole- cular landscape and cellular interactions within the tissue. We evaluate the performance of METI on ST data generated from various tumor tissues, including gastric, lung, and bladder cancers, as well as premalignant tissues. We also conduct a quantitative comparison of METI with existing clustering and cell deconvolution tools, demonstrating METI’s robust and consistent performance. Go to the article
Our collaborations focus on interdisciplinary work to elucidate the mechanisms behind diverse diseases. We have published several collaborative papers in high-impact journals such as Nature, Nature Medicine, and Cancer Discovery. These partnerships grant our lab access to several large cancer cohort studies with unique resources that are crucial for validating the novel statistical tools we develop.
Jian Hu*, Kyle Coleman, Edward B. Lee, Humam Kadara, Linghua Wang, Mingyao Li*. Spatial transcriptomics have enabled the comprehensive characterization of gene expression in tumor microenvironment. However, ST only measures expression in discrete spots, which limits their usefulness in studying the detailed structure of TME. Here we present TESLA, a machine learning framework for multi-level tissue annotation in ST. TESLA integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image, and further detects tertiary lymphoid structures and differential transcriptome programs between the edge and core of a tumor. TESLA provides a powerful tool for understanding the spatial architecture of the TME. Go to the article
Jian Hu*, Xiangjie Li, Kyle Coleman, Amelia Schroeder, Nan Ma, David J Irwin, Edward B Lee, Russell T Shinohara, Mingyao Li*. Recent advances in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive characterization of gene expression patterns in the context of tissue microenvironment. To elucidate spatial gene expression variation, we present SpaGCN, a graph convolutional network approach that integrates gene expression, spatial location and histology in SRT data analysis. Through graph convolution, SpaGCN aggregates gene expression of each spot from its neighboring spots, which enables the identification of spatial domains with coherent expression and histology. The subsequent domain guided differential expression (DE) analysis then detects genes with enriched expression patterns in the identified domains. Analyzing seven SRT datasets using SpaGCN, we show it can detect genes with much more enriched spatial expression pat- terns than competing methods. Furthermore, genes detected by SpaGCN are transferrable and can be utilized to study spatial variation of gene expression in other datasets. SpaGCN is computationally fast, platform independent, making it a desirable tool for diverse SRT studies. Go to the article
Jian Hu, Amelia Schroeder, Kyle Coleman, Chixiang Chen, Benjamin J Auerbach, Mingyao Li
Spatial resolved transcriptomics (SRT) technologies have enabled scientists to get an integrated understanding of cells in their morphological context. Applications of these technologies in diverse tissues and diseases have transformed our views of transcriptional complexity. Most published studies utilized tools developed for single-cell RNA sequencing (scRNA-seq) for data analysis. However, SRT data exhibit different properties from scRNA-seq. To take full advantage of the added dimension on spatial location information in such data, new methods that are tailored for SRT are needed. Additionally, SRT data often have companion high-resolution histology information available. Incorporating histological features in gene expression analysis is an underexplored area. In this review, we will focus on the statistical and machine learning aspects for SRT data analysis and discuss how spatial location and histology information can be integrated with gene expression to advance our understanding of the transcriptional complexity. We also point out open problems and future research directions in this field.
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Jian Hu, Xiangjie Li, Gang Hu, Yafei Lyu, Katalin Susztak, Mingyao Li
Clustering and cell type classification are important steps in single-cell RNA-seq (scRNA-seq) analysis. As more and more scRNA-seq data are becoming available, supervised cell type classification methods that utilize external well-annotated source data start to gain popularity over unsupervised clustering algorithms; however, the performance of existing supervised meth- ods is highly dependent on source data quality and they often have limited accuracy to classify cell types that are missing in the source data. We developed ItClust to overcome these limitations, a transfer learning algorithm that borrows ideas from super- vised cell type classification algorithms, but also leverages information in target data to ensure sensitivity in classifying cells that are only present in the target data. Through extensive evaluations using data from different species and tissues generated with diverse scRNA-seq protocols, we show that ItClust considerably improves clustering and cell type classification accuracy over popular unsupervised clustering and supervised cell type classification algorithms.
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